13.3.12.4 Optimizations, Computational Issues

Ceres Solver,
2016.
WWW Link. Code, Optimization, C++. 1707
Open source C++ library for modeling and solving large, complicated optimization problems.

Yuan, H.Q.[Hua-Qiang], Ye, Y.D.[Yang-Dong],
Iterative sIB algorithm,
PRL(32), No. 4, 1 March 2011, pp. 606-614.
Elsevier DOI 1102
Information Bottleneck; sIB algorithm; Mutation; Mutual information. sIB: sequential Information Bottleneck. Optimization technique. BibRef

Fadili, J.M.[Jalal M.], Peyre, G.[Gabriel],
Total Variation Projection With First Order Schemes,
IP(20), No. 3, March 2011, pp. 657-669.
IEEE DOI 1103
BibRef
Earlier: ICIP09(1325-1328).
IEEE DOI 0911
BibRef

Chesneau, C., Fadili, M.J., Starck, J.L.,
Image deconvolution by stein block thresholding,
ICIP09(1329-1332).
IEEE DOI 0911
BibRef

Starck, J.L., Fadili, M.J.,
An overview of inverse problem regularization using sparsity,
ICIP09(1453-1456).
IEEE DOI 0911
BibRef

Fadili, M.J., Starck, J.L.,
Monotone operator splitting for optimization problems in sparse recovery,
ICIP09(1461-1464).
IEEE DOI 0911
BibRef

Felzenszwalb, P.F.[Pedro F.], Zabih, R.[Ramin],
Dynamic Programming and Graph Algorithms in Computer Vision,
PAMI(33), No. 4, April 2011, pp. 721-740.
IEEE DOI 1103
Discrete Optimization techniques. BibRef

Rota Bulo, S.[Samuel], Pelillo, M.[Marcello], Bomze, I.M.[Immanuel M.],
Graph-based quadratic optimization: A fast evolutionary approach,
CVIU(115), No. 7, July 2011, pp. 984-995.
Elsevier DOI 1106
Quadratic optimization; Population dynamics; Graph-based problems BibRef

Strandmark, P.[Petter], Kahl, F.[Fredrik], Schoenemann, T.[Thomas],
Parallel and distributed vision algorithms using dual decomposition,
CVIU(115), No. 12, December 2011, pp. 1721-1732.
Elsevier DOI 1111
BibRef
Earlier: A1, A2, Only:
Parallel and distributed graph cuts by dual decomposition,
CVPR10(2085-2092).
IEEE DOI Video of talk:
WWW Link. 1006
Graph cuts; Dual decomposition; Parallel; MRF; MPI; GPU BibRef

Strandmark, P.[Petter], Kahl, F.[Fredrik], Overgaard, N.C.[Niels C.],
Optimizing parametric total variation models,
ICCV09(2240-2247).
IEEE DOI 0909
BibRef

Kahl, F.[Fredrik], Strandmark, P.[Petter],
Generalized roof duality for pseudo-boolean optimization,
ICCV11(255-262).
IEEE DOI 1201
BibRef

Ranjbar, M., Lan, T.[Tian], Wang, Y.[Yang], Robinovitch, S.N., Li, Z.N.[Ze-Nian], Mori, G.,
Optimizing Nondecomposable Loss Functions in Structured Prediction,
PAMI(35), No. 4, April 2013, pp. 911-924.
IEEE DOI 1303
BibRef

Ranjbar, M.[Mani], Mori, G.[Greg], Wang, Y.[Yang],
Optimizing Complex Loss Functions in Structured Prediction,
ECCV10(II: 580-593).
Springer DOI 1009
for various applications where the measure is function of false positive and false negative. BibRef

El-Zehiry, N.Y.[Noha Youssry], Grady, L.[Leo],
Combinatorial Optimization of the Discretized Multiphase Mumford-Shah Functional,
IJCV(104), No. 3, September 2013, pp. 270-285.
WWW Link. 1308
BibRef
Earlier:
Discrete Optimization of the Multiphase Piecewise Constant Mumford-Shah Functional,
EMMCVPR11(233-246).
Springer DOI 1107
BibRef

Duan, Y.P.[Yu-Ping], Huang, W.M.[Wei-Min],
A fixed-point augmented Lagrangian method for total variation minimization problems,
JVCIR(24), No. 7, 2013, pp. 1168-1181.
Elsevier DOI 1309
Convex optimization BibRef

Raguet, H.[Hugo], Landrieu, L.[Loïc],
Preconditioning of a Generalized Forward-Backward Splitting and Application to Optimization on Graphs,
SIIMS(8), No. 4, 2015, pp. 2706-2739.
DOI Link 1601
BibRef

Syed, M., Pardalos, P., Principe, J.C.,
Invexity of the Minimum Error Entropy Criterion,
SPLetters(20), No. 12, 2013, pp. 1159-1162.
IEEE DOI 1311
convex programming. optimization properties of Minimization of Error Entropy. BibRef

Papadakis, N., Peyré, G., Oudet, E.,
Optimal Transport with Proximal Splitting,
SIIMS(7), No. 1, 2014, pp. 212-238.
DOI Link 1404
first order convex optimization schemes. BibRef

Treiber, M.A.[Marco Alexander],
Optimization for Computer Vision: An Introduction to Core Concepts and Methods,

Ochs, P.[Peter], Chen, Y., Brox, T.[Thomas], Pock, T.[Thomas],
iPiano: Inertial Proximal Algorithm for Nonconvex Optimization,
SIIMS(7), No. 2, 2014, pp. 1388-1419.
DOI Link 1407
BibRef

Ochs, P.[Peter], Brox, T.[Thomas], Pock, T.[Thomas],
iPiasco: Inertial Proximal Algorithm for Strongly Convex Optimization,
JMIV(53), No. 2, October 2015, pp. 171-181.
Springer DOI 1508
BibRef

Ochs, P.[Peter], Dosovitskiy, A.[Alexey], Brox, T.[Thomas], Pock, T.[Thomas],
On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision,
SIIMS(8), No. 1, 2015, pp. 331-372.
DOI Link 1503
BibRef
Earlier:
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision,
CVPR13(1759-1766)
IEEE DOI 1309
non-smooth non-convex optimization; optimization BibRef

Ochs, P.[Peter], Ranftl, R.[René], Brox, T.[Thomas], Pock, T.[Thomas],
Techniques for Gradient-Based Bilevel Optimization with Non-smooth Lower Level Problems,
JMIV(56), No. 2, October 2016, pp. 175-194.
Springer DOI 1609
BibRef
Earlier:
Bilevel Optimization with Nonsmooth Lower Level Problems,
SSVM15(654-665).
Springer DOI 1506
BibRef

Simonyan, K.[Karen], Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Learning Local Feature Descriptors Using Convex Optimisation,
PAMI(36), No. 8, August 2014, pp. 1573-1585.
IEEE DOI 1407
BibRef
Earlier:
Descriptor Learning Using Convex Optimisation,
ECCV12(I: 243-256).
Springer DOI 1210
Detectors BibRef

Novotny, D.[David], Larlus, D.[Diane], Vedaldi, A.[Andrea],
Capturing the Geometry of Object Categories from Video Supervision,
PAMI(42), No. 2, February 2020, pp. 261-275.
IEEE DOI 2001
BibRef
Earlier:
Learning 3D Object Categories by Looking Around Them,
ICCV17(5228-5237)
IEEE DOI 1802
BibRef
Earlier:
Learning the Structure of Objects from Web Supervision,
DeepLearn16(III: 218-235).
Springer DOI 1611
more about the particular object, not more objects. Geometry, Shape, Solid modeling, Estimation, Image reconstruction, Training, geometry reconstruction. computational geometry, image motion analysis, learning (artificial intelligence), object detection, BibRef

Li, M., Yang, S., Li, K., Liu, X.,
Evolutionary Algorithms With Segment-Based Search for Multiobjective Optimization Problems,
Cyber(44), No. 8, August 2014, pp. 1295-1313.
IEEE DOI 1407
Convergence BibRef

Goldstein, T., O'Donoghue, B., Setzer, S., Baraniuk, R.,
Fast Alternating Direction Optimization Methods,
SIIMS(7), No. 3, 2014, pp. 1588-1623.
DOI Link 1410
BibRef

Kappes, J.H.[Jörg H.], Andres, B.[Bjoern], Hamprecht, F.A.[Fred A.], Schnörr, C.[Christoph], Nowozin, S.[Sebastian], Batra, D.[Dhruv], Kim, S.W.[Sung-Woong], Kausler, B.X.[Bernhard X.], Kröger, T.[Thorben], Lellmann, J.[Jan], Komodakis, N.[Nikos], Savchynskyy, B.[Bogdan], Rother, C.[Carsten],
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems,
IJCV(115), No. 2, November 2015, pp. 155-184.
Springer DOI 1511
BibRef
Earlier: A1, A2, A3, A4, A5, A6, A7, A8, A10, A11, A13, Only:
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems,
CVPR13(1328-1335)
IEEE DOI 1309
Markov random fields; benchmark; discrete optimization; graphical models BibRef

Chierchia, G., Pustelnik, N., Pesquet, J.C., Pesquet-Popescu, B.,
Epigraphical projection and proximal tools for solving constrained convex optimization problems,
SIViP(9), No. 8, November 2015, pp. 1737-1749.
Springer DOI 1511
BibRef

Harizanov, S.[Stanislav], Pesquet, J.C.[Jean-Christophe], Steidl, G.[Gabriele],
Epigraphical Projection for Solving Least Squares Anscombe Transformed Constrained Optimization Problems,
SSVM13(125-136).
Springer DOI 1305
BibRef

Chen, C.H.[Cai-Hua], Chan, R.H.[Raymond H.], Ma, S.Q.[Shi-Qian], Yang, J.F.[Jun-Feng],
Inertial Proximal ADMM for Linearly Constrained Separable Convex Optimization,
SIIMS(8), No. 4, 2015, pp. 2239-2267.
DOI Link 1601
BibRef

Swoboda, P.[Paul], Shekhovtsov, A., Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph], Savchynskyy, B.[Bogdan],
Partial Optimality by Pruning for MAP-Inference with General Graphical Models,
PAMI(38), No. 7, July 2016, pp. 1370-1382.
IEEE DOI 1606
BibRef
Earlier: A1, A5, A3, A4, Only: CVPR14(1170-1177)
IEEE DOI 1409
Award, CVPR, Student. BibRef
Earlier: A1, A5, A3, A4, Only:
Partial Optimality via Iterative Pruning for the Potts Model,
SSVM13(477-488).
Springer DOI 1305
Graphical models. Discrete Optimization; Graphical Models; MAP-inference BibRef

Li, L.L.[Ling-Ling], Jiao, L.C.[Li-Cheng], Zhao, J.[Jiaqi], Shang, R.H.[Rong-Hua], Gong, M.[Maoguo],
Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering,
PR(63), No. 1, 2017, pp. 1-14.
Elsevier DOI 1612
Multi-objective optimization BibRef

Shen, F., Zhou, X., Yang, Y., Song, J., Shen, H.T., Tao, D.,
A Fast Optimization Method for General Binary Code Learning,
IP(25), No. 12, December 2016, pp. 5610-5621.
IEEE DOI 1612
binary codes BibRef

Pock, T.[Thomas], Sabach, S.[Shoham],
Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems,
SIIMS(9), No. 4, 2016, pp. 1756-1787.
DOI Link 1612
BibRef

Yu, Y.[Yi], Zhao, H.Q.[Hai-Quan],
A joint-optimization NSAF algorithm based on the first-order Markov model,
SIViP(11), No. 3, March 2017, pp. 509-516.
WWW Link. 1702
BibRef

Dillon, K.[Keith], Fainman, Y.[Yeshaiahu],
Element-wise uniqueness, prior knowledge, and data-dependent resolution,
SIViP(11), No. 1, January 2017, pp. 41-48.
Springer DOI 1702
solutions to underdetermined linear systems. BibRef

Ono, S.[Shunsuke],
L_0 Gradient Projection,
IP(26), No. 4, April 2017, pp. 1554-1564.
IEEE DOI 1704
approximation theory BibRef

Watanabe, M.[Makoto], Kyochi, S.[Seisuke], Ono, S.[Shunsuke],
Gradient-domain image decomposition for image recovery,
ICIP15(4768-4772)
IEEE DOI 1512
Convex optimization BibRef

Korkinof, D.[Dimitrios], Demiris, Y.[Yiannis],
Multi-task and multi-kernel Gaussian process dynamical systems,
PR(66), No. 1, 2017, pp. 190-201.
Elsevier DOI 1704
Gaussian processes BibRef

Zhang, W.Z.[Wei-Zhong], Zhang, L.J.[Li-Jun], Jin, Z.M.[Zhong-Ming], Jin, R.[Rong], Cai, D.[Deng], Li, X.L.[Xue-Long], Liang, R.H.[Rong-Hua], He, X.F.[Xiao-Fei],
Sparse Learning with Stochastic Composite Optimization,
PAMI(39), No. 6, June 2017, pp. 1223-1236.
IEEE DOI 1705
Algorithm design and analysis, Convergence, Linear programming, Optimization, Standards, Stochastic processes, Training, Sparse learning, stochastic composite optimization, stochastic, optimization BibRef

Laue, H.E.A.[Heinrich Edgar Arnold], du Plessis, W.P.[Warren Paul],
A Coherence-Based Algorithm for Optimizing Rank-1 Grassmannian Codebooks,
SPLetters(24), No. 6, June 2017, pp. 823-827.
IEEE DOI 1705
Coherence, Convergence, Euclidean distance, Manifolds, Optimization, Signal processing algorithms, Coherence, compressive sensing (CS), optimization, rank-1 Grassmannian codebooks, sensing, matrix BibRef

Deng, Y.[Yue], Zhao, Y.Y.[Yan-Yu], Ren, Z.Q.[Zhi-Quan], Kong, Y.Y.[You-Yong], Bao, F.[Feng], Dai, Q.H.[Qiong-Hai],
Discriminant Kernel Assignment for Image Coding,
Cyber(47), No. 6, June 2017, pp. 1434-1445.
IEEE DOI 1706
Dictionaries, Histograms, Image coding, Integrated circuits, Jacobian matrices, Kernel, Optimization, Fisher discriminant criterion, image coding (IC), image understanding, optimization BibRef

Guan, S.[Sihai], Li, Z.[Zhi],
Optimal step size of least mean absolute third algorithm,
SIViP(11), No. 6, September 2017, pp. 1105-1113.
WWW Link. 1708
BibRef

Benchikhi, L.[Loubna], Sadgal, M.[Mohamed], Elfazziki, A.[Aziz], Mansouri, F.[Fatimaezzahra],
An ant colony based model to optimize parameters in industrial vision,
ELCVIA(16), No. 1, 2017, pp. 33-53.
DOI Link 1708
BibRef

Liu, R.[Ruochen], Wang, R.[Ruinan], Yu, X.[Xin], An, L.[Lijia],
Shape automatic clustering-based multi-objective optimization with decomposition,
MVA(28), No. 5-6, August 2017, pp. 497-508.
WWW Link. 1708
BibRef

Chen, Y.Y.[Yong-Yong], Wang, Y.L.[Yong-Li], Li, M.Q.[Ming-Qiang], He, G.P.[Guo-Ping],
Augmented Lagrangian alternating direction method for low-rank minimization via non-convex approximation,
SIViP(11), No. 7, October 2017, pp. 1271-1278.
WWW Link. 1708
BibRef

Yuan, X.T.[Xiao-Tong], Liu, Q.S.[Qing-Shan],
Newton-Type Greedy Selection Methods for L_0-Constrained Minimization,
PAMI(39), No. 12, December 2017, pp. 2437-2450.
IEEE DOI 1711
BibRef
Earlier:
Newton Greedy Pursuit: A Quadratic Approximation Method for Sparsity-Constrained Optimization,
CVPR14(4122-4129)
IEEE DOI 1409
Convergence, Iterative methods, Linear programming, Minimization. Greedy Pursuit; Newton Method; Optimization; Sparsity Model BibRef

Shalom, S.S., Tabrikian, J.,
Efficient Computation of MSE Lower Bounds via Matching Pursuit,
SPLetters(24), No. 12, December 2017, pp. 1798-1802.
IEEE DOI 1712
Bayes methods, computational complexity, covariance analysis, greedy algorithms, iterative methods, mean square error methods, weiss-weinstein class BibRef

Zhu, B., Bai, B., Chen, W., Liu, Z.,
Sparse Network Completion via Discrete-Constrained Nuclear-Norm Minimization,
SPLetters(24), No. 12, December 2017, pp. 1896-1900.
IEEE DOI 1712
Complex networks, Minimization, Optimization, Simulation, Social network services, Sparse matrices, Symmetric matrices, sparse graph BibRef

Karlsson, J.[Johan], Ringh, A.[Axel],
Generalized Sinkhorn Iterations for Regularizing Inverse Problems Using Optimal Mass Transport,
SIIMS(10), No. 4, 2017, pp. 1935-1962.
DOI Link 1801
BibRef

Bocklin, C.L.[Christofer L.], Andersson, C.[Claes], Gustafsson, M.G.[Mats G.],
Self-tuning density estimation based on Bayesian averaging of adaptive kernel density estimations yields state-of-the-art performance,
PR(78), 2018, pp. 133-143.
Elsevier DOI 1804
PDF Estimation. Data Available:
PDF File. Adaptive density estimation, Variable bandwidth, Bayesian model averaging, Square root law, Multivariate, Univariate BibRef

Zheng, M.L.[Mian-Lun], Yuan, Z.Y.[Zhi-Yong], Tong, Q.Q.[Qian-Qian], Zhang, G.[Guian], Zhu, W.X.[Wei-Xu],
A novel unconditionally stable explicit integration method for finite element method,
VC(34), No. 5, May 2018, pp. 721-733.
WWW Link. 1804
BibRef

Koçanaogullari, A., Erdogmus, D., Akçakaya, M.,
On Analysis of Active Querying for Recursive State Estimation,
SPLetters(25), No. 6, June 2018, pp. 743-747.
IEEE DOI 1806
combinatorial mathematics, convergence, minimisation, optimisation, query processing, recursive estimation, state estimation, recursive state estimation BibRef

Robini, M., Yang, F., Zhu, Y.,
Inexact Half-Quadratic Optimization for Linear Inverse Problems,
SIIMS(11), No. 2, 2018, pp. 1078-1133.
DOI Link 1807
BibRef

Wang, B.[Bin], Jiang, H.Y.[Hong-Yu], Fang, J.[Jun], Duan, H.P.[Hui-Ping],
A Proximal ADMM for Decentralized Composite Optimization,
SPLetters(25), No. 8, August 2018, pp. 1121-1125.
IEEE DOI 1808
optimisation, composite optimization problem, decentralized network, decentralized composite optimization, proximal ADMM BibRef

Xie, C.L.[Chun-Lei], Sun, Y.J.[Yu-Juan],
Constructions of Even-Period Binary Z-Complementary Pairs With Large ZCZs,
SPLetters(25), No. 8, August 2018, pp. 1141-1145.
IEEE DOI 1808
binary codes, correlation methods, Golay codes, even-period binary Z-complementary pairs, zero-correlation zone (ZCZ) BibRef

Almeida, I.[Inês], Xavier, J.[João],
DJAM: Distributed Jacobi Asynchronous Method for Learning Personal Models,
SPLetters(25), No. 9, September 2018, pp. 1389-1392.
IEEE DOI 1809
data handling, distributed algorithms, learning (artificial intelligence), multi-agent systems, optimization BibRef

Ahmed, N.,
Data-Free/Data-Sparse Softmax Parameter Estimation With Structured Class Geometries,
SPLetters(25), No. 9, September 2018, pp. 1408-1412.
IEEE DOI 1809
approximation theory, learning (artificial intelligence), maximum likelihood estimation, optimisation, supervised learning BibRef

Pan, J.F.[Jin-Feng], Shen, J.[Jin], Gao, M.L.[Ming-Liang], Yin, L.J.[Li-Ju], Liu, F.Y.[Fa-Ying], Zou, G.F.[Guo-Feng],
Orthogonal gradient measurement matrix optimisation method,
IET-IPR(12), No. 10, October 2018, pp. 1773-1779.
DOI Link 1809
BibRef

Pizzo, A., Zappone, A., Sanguinetti, L.,
Solving Fractional Polynomial Problems by Polynomial Optimization Theory,
SPLetters(25), No. 10, October 2018, pp. 1540-1544.
IEEE DOI 1810
Optimization, Signal to noise ratio, Signal processing algorithms, Programming, polynomial optimization theory BibRef

Zhang, Z.W.[Zheng-Wu], Su, J.Y.[Jing-Yong], Klassen, E.[Eric], Le, H.L.[Hui-Ling], Srivastava, A.[Anuj],
Rate-Invariant Analysis of Covariance Trajectories,
JMIV(60), No. 8, October 2018, pp. 1306-1323.
Springer DOI 1810
BibRef

Iutzeler, F., Condat, L.,
Distributed Projection on the Simplex and L_1 Ball via ADMM and Gossip,
SPLetters(25), No. 11, November 2018, pp. 1650-1654.
IEEE DOI 1811
convex programming, distributed algorithms, mathematics computing, distributed projection, computing network, simplex BibRef

Koçanaogullari, A.[Aziz], Marghi, Y.M.[Yeganeh M.], Akçakaya, M.[Murat], Erdogmus, D.[Deniz],
Optimal Query Selection Using Multi-Armed Bandits,
SPLetters(25), No. 12, December 2018, pp. 1870-1874.
IEEE DOI 1812
information theory, Monte Carlo methods, query processing, optimal query selection, multi-armed bandit framework BibRef

Kushwaha, N.[Neetu], Pant, M.[Millie], Kant, S.[Surya], Jain, V.K.[Vinay Kumar],
Magnetic optimization algorithm for data clustering,
PRL(115), 2018, pp. 59-65.
Elsevier DOI 1812
Clustering inspired by magnetic force. Magnetic optimization, Clustering, Nature inspired algorithms, -means, Partitional clustering BibRef

Geiping, J., Moeller, M.,
Composite Optimization by Nonconvex Majorization-Minimization,
SIIMS(11), No. 4, 2018, pp. 2494-2528.
DOI Link 1901
BibRef

Ye, H.S.[Hai-Shan], Xie, G.Z.[Guang-Zeng], Luo, L.[Luo], Zhang, Z.H.[Zhi-Hua],
Fast stochastic second-order method logarithmic in condition number,
PR(88), 2019, pp. 629-642.
Elsevier DOI 1901
BibRef

Hadfield, S.[Simon], Lebeda, K.[Karel], Bowden, R.[Richard],
HARD-PnP: PnP Optimization Using a Hybrid Approximate Representation,
PAMI(41), No. 3, March 2019, pp. 768-774.
IEEE DOI 1902
Minimization, Cameras, Cost function, Geometry, Robustness, PnP, perspective-n-point, multiview geometry BibRef

Lin, P., Ren, W., Wang, H., Al-Saggaf, U.M.,
Multiagent Rendezvous With Shortest Distance to Convex Regions With Empty Intersection: Algorithms and Experiments,
Cyber(49), No. 3, March 2019, pp. 1026-1034.
IEEE DOI 1902
Optimization, Convergence, Algorithm design and analysis, Multi-agent systems, Linear programming, Lyapunov methods, shortest-distance rendezvous BibRef

Daei, S., Haddadi, F., Amini, A.,
Improved Recovery of Analysis Sparse Vectors in Presence of Prior Information,
SPLetters(26), No. 2, February 2019, pp. 222-226.
IEEE DOI 1902
matrix algebra, numerical analysis, optimisation, signal processing, prior information, recovery stage, conic integral geometry BibRef

Tan, X.G.[Xue-Gang], Cao, J.[Jinde], Li, X.D.[Xiao-Di],
Consensus of Leader-Following Multiagent Systems: A Distributed Event-Triggered Impulsive Control Strategy,
Cyber(49), No. 3, March 2019, pp. 792-801.
IEEE DOI 1902
Multi-agent systems, Convergence, Measurement errors, Microprocessors, Information processing, multiagent systems BibRef

Caissard, T.[Thomas], Coeurjolly, D.[David], Lachaud, J.O.[Jacques-Olivier], Roussillon, T.[Tristan],
Laplace-Beltrami Operator on Digital Surfaces,
JMIV(61), No. 3, March 2019, pp. 359-379.
Springer DOI 1903
BibRef
Earlier:
Heat Kernel Laplace-Beltrami Operator on Digital Surfaces,
DGCI17(241-253).
Springer DOI 1711
variational problems involving the discretization of the Laplace-Beltrami operator. BibRef

Chau, G., Wohlberg, B., Rodriguez, P.,
Efficient Projection onto the Ll_(inf,1) Mixed-Norm Ball Using a Newton Root Search Method,
SIIMS(12), No. 1, 2019, pp. 604-623.
DOI Link 1904
BibRef

Gong, X.F.[Xiao-Feng], Lin, Q.H.[Qiu-Hua], Cong, F.Y.[Feng-Yu], de Lathauwer, L.[Lieven],
Double coupled canonical polyadic decomposition of third-order tensors: Algebraic algorithm and relaxed uniqueness conditions,
SP:IC(73), 2019, pp. 22-36.
Elsevier DOI 1904
Tensor, Canonical polyadic decomposition, Double coupled, Algebraic algorithm, Uniqueness BibRef

Xu, Y., Qi, L., Sun, W.,
On semi-definiteness and minimal H-eigenvalue of a symmetric space tensor using nonnegative polynomial optimization techniques,
SP:IC(73), 2019, pp. 3-11.
Elsevier DOI 1904
Space tensor, Nonnegative polynomials, Semidefinite program, H-eigenvalue BibRef

Volaric, I.[Ivan], Sucic, V.[Victor],
Sparse time-frequency distributions based on the L1-norm minimization with the fast intersection of confidence intervals rule,
SIViP(13), No. 3, April 2019, pp. 499-506.
WWW Link. 1904
BibRef

Mirebeau, J.M.[Jean-Marie], Portegies, J.[Jorg],
Hamiltonian Fast Marching: A Numerical Solver for Anisotropic and Non-Holonomic Eikonal PDEs,
IPOL(9), 2019, pp. 47-93.
DOI Link 1904
HamiltonFastMarching code, designed to solve various classes of eikonal equations, and extract the related minimal paths. BibRef

Repetti, A.[Audrey], Pereyra, M.[Marcelo], Wiaux, Y.[Yves],
Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization,
SIIMS(12), No. 1, 2019, pp. 87-118.
DOI Link 1904
BibRef

Pereyra, M.[Marcelo],
Revisiting Maximum-a-Posteriori Estimation in Log-Concave Models,
SIIMS(12), No. 1, 2019, pp. 650-670.
DOI Link 1904
BibRef

Luo, X., Zhang, P., Huang, Z., Nie, L., Xu, X.,
Discrete Hashing With Multiple Supervision,
IP(28), No. 6, June 2019, pp. 2962-2975.
IEEE DOI 1905
binary codes, iterative methods, learning (artificial intelligence), optimisation, supervised hashing BibRef

Wu, B.Y.[Bao-Yuan], Ghanem, B.[Bernard],
L_p-L_p-Box ADMM: A Versatile Framework for Integer Programming,
PAMI(41), No. 7, July 2019, pp. 1695-1708.
IEEE DOI 1906
Integer Programming networks, Convex functions, Linear programming, Optimization, Machine learning, Convergence, machine learning BibRef

Wu, B.Y.[Bao-Yuan], Shen, L.[Li], Zhang, T.[Tong], Ghanem, B.[Bernard],
MAP Inference Via L_2-Sphere Linear Program Reformulation,
IJCV(128), No. 7, July 2020, pp. 1913-1936.
Springer DOI 2007
BibRef

Altilio, R.[Rosa], di Lorenzo, P.[Paolo], Panella, M.[Massimo],
Distributed data clustering over networks,
PR(93), 2019, pp. 603-620.
Elsevier DOI 1906
Distributed learning, Clustering, Gaussian mixtures, Expectation maximization, Non-Convex optimization BibRef

Irani, M.[Michal],
'Blind' visual inference by composition,
PRL(124), 2019, pp. 39-54.
Elsevier DOI 1906
Solve complex inverse problems when the degradation model is unknown. BibRef

Xie, A., Yin, F., Xu, Y., Ai, B., Chen, T., Cui, S.,
Distributed Gaussian Processes Hyperparameter Optimization for Big Data Using Proximal ADMM,
SPLetters(26), No. 8, August 2019, pp. 1197-1201.
IEEE DOI 1908
Big Data, distributed algorithms, Gaussian processes, learning (artificial intelligence), hyperparameter optimization BibRef

Xin, R., Jakovetic, D., Khan, U.A.,
Distributed Nesterov Gradient Methods Over Arbitrary Graphs,
SPLetters(26), No. 8, August 2019, pp. 1247-1251.
IEEE DOI 1908
directed graphs, eigenvalues and eigenfunctions, gradient methods, optimisation, stochastic processes, optimization methods BibRef

Akyildiz, Ö.D., Chouzenoux, É., Elvira, V., Míguez, J.,
A Probabilistic Incremental Proximal Gradient Method,
SPLetters(26), No. 8, August 2019, pp. 1257-1261.
IEEE DOI 1908
Bayes methods, gradient methods, Kalman filters, optimisation, probability, state-space methods, extended Kalman filtering BibRef

Yeredor, A., Haardt, M.,
Maximum Likelihood Estimation of a Low-Rank Probability Mass Tensor From Partial Observations,
SPLetters(26), No. 10, October 2019, pp. 1551-1555.
IEEE DOI 1909
Maximum likelihood estimation, Minimization, Computational modeling, Optimization, Radio frequency, Estimation-Maximization (EM) BibRef

Dai, J.[Jicheng], Yan, L.[Li], Liu, H.[Hua], Chen, C.J.[Chang-Jun], Huo, L.[Liang],
An Offline Coarse-To-Fine Precision Optimization Algorithm for 3D Laser SLAM Point Cloud,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Tichavský, P.[Petr], Phan, A.H.[Anh-Huy], Cichocki, A.[Andrzej],
Sensitivity in Tensor Decomposition,
SPLetters(26), No. 11, November 2019, pp. 1653-1657.
IEEE DOI 1911
Sensitivity, Matrix decomposition, Signal processing algorithms, Convergence, Symmetric matrices, Optimization BibRef

Su, X.H.[Xin-Hua], Wang, Y.L.[Yi-Lun], Kang, X.J.[Xue-Jing], Tao, R.[Ran],
Nonconvex Truncated Nuclear Norm Minimization Based on Adaptive Bisection Method,
CirSysVideo(29), No. 11, November 2019, pp. 3159-3172.
IEEE DOI 1911
Minimization, Sparse matrices, Approximation algorithms, Heuristic algorithms, Optimization, Estimation, nonconvex optimization BibRef

Mao, F.[Feng], Huang, M.[Min], Zheng, J.[Jian], Qian, Y.X.[Yu-Xiang],
Optimisation model and solution method of multi-sources guide paths,
IET-ITS(13), No. 11, November 2019, pp. 1668-1676.
DOI Link 1911
BibRef

Hafiene, Y.[Yosra], Fadili, J.M.[Jalal M.], Elmoataz, A.[Abderrahim],
Continuum Limits of Nonlocal p-Laplacian Variational Problems on Graphs,
SIIMS(12), No. 4, 2019, pp. 1772-1807.
DOI Link 1912
BibRef

Santos, I., Murillo-Fuentes, J.J., Arias-de-Reyna, E.,
A Double EP-Based Proposal for Turbo Equalization,
SPLetters(27), 2020, pp. 121-125.
IEEE DOI 2001
Expectation propagation (EP), MMSE, low-complexity, turbo equalization, ISI, Wiener, Kalman BibRef

Dulek, B., Ozturk, C., Gezici, S.,
Optimal Decision Rules for Simple Hypothesis Testing Under General Criterion Involving Error Probabilities,
SPLetters(27), 2020, pp. 261-265.
IEEE DOI 2002
Hypothesis testing, optimal tests, convexity, likelihood ratio, randomization BibRef

Zhang, T., Wang, S., Huang, X., Jia, L.,
Kernel Recursive Least Squares Algorithm Based on the Nystrom Method With k-Means Sampling,
SPLetters(27), 2020, pp. 361-365.
IEEE DOI 2004
Kernel adaptive filters, Nystrom method, kernel recursive least squares, k-means sampling BibRef

Peng, D., Dong, J., Peng, Q.,
Overloaded Branch Chains Induced by False Data Injection Attack in Smart Grid,
SPLetters(27), 2020, pp. 426-430.
IEEE DOI 2004
Bilevel optimization, chain, false data injection attack, overloading, smart grid BibRef

Zhou, H., Wang, X., Cui, N.,
Fuel-Optimal Multi-Impulse Orbit Transfer Using a Hybrid Optimization Method,
ITS(21), No. 4, April 2020, pp. 1359-1368.
IEEE DOI 2004
Multi impulse, orbit transfer, space transportation, hybrid optimization algorithm BibRef

Liu, Y.[Yong], Liao, S.Z.[Shi-Zhong], Jiang, S.L.[Sha-Li], Ding, L.Z.[Li-Zhong], Lin, H.L.[Hai-Lun], Wang, W.P.[Wei-Ping],
Fast Cross-Validation for Kernel-Based Algorithms,
PAMI(42), No. 5, May 2020, pp. 1083-1096.
IEEE DOI 2004
Approximation algorithms, Kernel, Training, Taylor series, Support vector machines, Upper bound, Computational modeling, kernel methods BibRef

Wang, Y.R.[Yi-Ru], Liu, Y.L.[Yin-Long], Li, X.C.[Xue-Chen], Wang, C.[Chen], Wang, M.N.[Man-Ning], Song, Z.J.[Zhi-Jian],
GORFLM: Globally Optimal Robust Fitting for Linear Model,
SP:IC(84), 2020, pp. 115834.
Elsevier DOI 2004
Robust linear model fitting, Globally optimization, Branch and bound, Gaussian function BibRef

Liu, Y.L.[Yin-Long], Wang, Y.R.[Yi-Ru], Wang, M.N.[Man-Ning], Chen, G.[Guang], Knoll, A.[Alois], Song, Z.J.[Zhi-Jian],
Globally Optimal Linear Model Fitting with Unit-Norm Constraint,
IJCV(130), No. 1, January 2022, pp. 933-946.
Springer DOI 2204
BibRef

Zhang, F., Hou, J., Wang, J., Wang, W.,
Uniqueness Guarantee of Solutions of Tensor Tubal-Rank Minimization Problem,
SPLetters(27), 2020, pp. 540-544.
IEEE DOI 2005
Tensors, Minimization, Manifolds, Transforms, Image reconstruction, Frequency measurement, Electron tubes, number of samples BibRef

Li, X.L.[Xiao-Lan], Gao, B.B.[Bing-Bo], Bai, Z.K.[Zhong-Ke], Pan, Y.C.[Yu-Chun], Gao, Y.B.[Yun-Bing],
An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Xu, Y., Wang, Q.,
Asymptotical Optimality of Change Point Detection With Unknown Discrete Post-Change Distributions,
SPLetters(27), 2020, pp. 695-699.
IEEE DOI 2005
Testing, Delays, Process control, Source coding, Control charts, Indexes, Quality control, Asymptotic optimality, universal source coding BibRef

Romaszko, L.[Lukasz], Williams, C.K.I.[Christopher K.I.], Winn, J.[John],
Learning Direct Optimization for scene understanding,
PR(105), 2020, pp. 107369.
Elsevier DOI 2006
Scene understanding, 3D Reconstruction, Inverse graphics, Object recognition, Scene graph, Graphics BibRef

Dey, T.K.[Tamal K.], Slechta, R.[Ryan],
Filtration Simplification for Persistent Homology via Edge Contraction,
JMIV(62), No. 5, June 2020, pp. 704-717.
Springer DOI 2007
BibRef
Earlier: DGCI19(89-100).
Springer DOI 1905
BibRef

Lee, C.H.[Ching-Hua], Rao, B.D.[Bhaskar D.], Garudadri, H.[Harinath],
A Sparse Conjugate Gradient Adaptive Filter,
SPLetters(27), 2020, pp. 1000-1004.
IEEE DOI 2007
Adaptive systems, Signal processing algorithms, Convergence, Markov processes, Optimization, Correlation, Estimation, sparse signal recovery BibRef

Darbon, J.[Jérôme], Meng, T.[Tingwei],
On Decomposition Models in Imaging Sciences and Multi-time Hamilton-Jacobi Partial Differential Equations,
SIIMS(13), No. 2, 2020, pp. 971-1014.
DOI Link 2007
BibRef

Gribonval, R.[Rémi], Nikolova, M.[Mila],
A Characterization of Proximity Operators,
JMIV(62), No. 6-7, July 2020, pp. 773-789.
Springer DOI 2007
Functions that map a vector to a solution of a penalized least-squares optimization problem. BibRef

Soubies, E.[Emmanuel], Blanc-Féraud, L.[Laure], Aubert, G.[Gilles],
New Insights on the Optimality Conditions of the L2-L0 Minimization Problem,
JMIV(62), No. 6-7, July 2020, pp. 808-824.
Springer DOI 2007
BibRef

Benning, M.[Martin], Riis, E.S.[Erlend Skaldehaug], Schönlieb, C.B.[Carola-Bibiane],
Bregman Itoh-Abe Methods for Sparse Optimisation,
JMIV(62), No. 6-7, July 2020, pp. 842-857.
Springer DOI 2007
BibRef

Jung, H., Kim, Y., Min, D., Jang, H., Ha, N., Sohn, K.,
Learning Deeply Aggregated Alternating Minimization for General Inverse Problems,
IP(29), 2020, pp. 8012-8027.
IEEE DOI 2008
Image restoration, Minimization, Noise reduction, Image resolution, Optimization, Training, Task analysis, proximal mapping BibRef

Candan, Ç.,
Chebyshev Center Computation on Probability Simplex With alpha-Divergence Measure,
SPLetters(27), 2020, pp. 1515-1519.
IEEE DOI 2009
Chebyshev approximation, Signal processing algorithms, Mutual information, Redundancy, Optimization, Minimization, error exponent calculation BibRef

Anirudh, R.[Rushil], Thiagarajan, J.J.[Jayaraman J.], Kailkhura, B.[Bhavya], Bremer, P.T.[Peer-Timo],
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking,
IJCV(128), No. 10-11, November 2020, pp. 2459-2477.
Springer DOI 2009
BibRef
Earlier: A1, A3, A2, A4:
Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization,
Diff-CVML17(690-698)
IEEE DOI 1709
Approximation algorithms, Manifolds, Measurement, Optimization, Principal component analysis, Silicon BibRef

Ye, S., Bar-Shalom, Y., Willett, P.,
Estimation of the Support Parameters of a Uniform PDF and the Cramér-Rao-Leibniz Lower Bound,
SPLetters(27), 2020, pp. 1765-1768.
IEEE DOI 2010
Covariance matrices, Estimation, Convergence, Indexes, Integral equations, Parameter estimation, Aging, Cramer-Rao bounds, parameter estimation BibRef

Rodríguez-Diez, V.[Vladímir], Martínez-Trinidad, J.F.[José Fco], Carrasco-Ochoa, J.A.[J. Ariel], Lazo-Cortés, M.S.[Manuel S.], Olvera-López, J.A.[J. Arturo],
MinReduct: A new algorithm for computing the shortest reducts,
PRL(138), 2020, pp. 177-184.
Elsevier DOI 2010
Rough sets, Shortest reducts, Binary cumulative operations BibRef

Jin, R.Y.[Ren-Yun], Qiu, H.F.[Hai-Feng], Weng, L.G.[Li-Guo],
Distributed discrete-time event-triggered algorithm for economic dispatch problem,
PRL(138), 2020, pp. 507-512.
Elsevier DOI 2010
Distributed discrete-time algorithm, Economic dispatch problem, Convex optimization, Event-triggered communication BibRef

Shi, X., Xing, F., Zhang, Z., Sapkota, M., Guo, Z., Yang, L.,
A Scalable Optimization Mechanism for Pairwise Based Discrete Hashing,
IP(30), 2021, pp. 1130-1142.
IEEE DOI 2012
Binary codes, Hash functions, Symmetric matrices, Semantics, Task analysis, Linear regression, Convolutional codes, Hashing, scalable BibRef

Chen, G.Y., Gan, M., Wang, S., Chen, C.L.P.,
Insights Into Algorithms for Separable Nonlinear Least Squares Problems,
IP(30), 2021, pp. 1207-1218.
IEEE DOI 2012
Jacobian matrices, Optimization, Signal processing algorithms, Partitioning algorithms, Bundle adjustment, Analytical models, bundle adjustment BibRef

Zhou, P.[Pan], Yuan, X.T.[Xiao-Tong], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi],
Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds,
PAMI(43), No. 2, February 2021, pp. 459-472.
IEEE DOI 2101
Optimization, Complexity theory, Manifolds, Convergence, Signal processing algorithms, Stochastic processes, Minimization, online learning BibRef

Kocak, M.A.[Mustafa A.], Ramirez, D.[David], Erkip, E.[Elza], Shasha, D.E.[Dennis E.],
SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness,
PAMI(43), No. 2, February 2021, pp. 663-678.
IEEE DOI 2101
Prediction algorithms, Waste materials, Error analysis, Machine learning, Machine learning algorithms, Reliability BibRef

Le, H.[Huu], Chin, T.J.[Tat-Jun], Eriksson, A.P.[Anders P.], Do, T.T.[Thanh-Toan], Suter, D.[David],
Deterministic Approximate Methods for Maximum Consensus Robust Fitting,
PAMI(43), No. 3, March 2021, pp. 842-857.
IEEE DOI 2102
Code, Robust Fitting.
WWW Link. Approximation algorithms, Optimization, Mathematical model, Estimation, Computational modeling, approximate algorithm BibRef

Nonomura, T.[Taku], Ono, S.[Shunsuke], Nakai, K.[Kumi], Saito, Y.[Yuji],
Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem,
SPLetters(28), 2021, pp. 284-288.
IEEE DOI 2102
Convergence, Sparse matrices, Signal processing algorithms, Linear programming, Newton method, Heuristic algorithms, data-driven sensor selection BibRef

Sun, Y.[Yuan], Li, X.D.[Xiao-Dong], Ernst, A.[Andreas],
Using Statistical Measures and Machine Learning for Graph Reduction to Solve Maximum Weight Clique Problems,
PAMI(43), No. 5, May 2021, pp. 1746-1760.
IEEE DOI 2104
Machine learning, Optimization, Machine learning algorithms, Search problems, Heuristic algorithms, Atmospheric measurements, problem reduction BibRef

Pan, L.L.[Li-Li], Chen, X.J.[Xiao-Jun],
Group Sparse Optimization for Images Recovery Using Capped Folded Concave Functions,
SIIMS(14), No. 1, 2021, pp. 1-25.
DOI Link 2104
BibRef

He, F.[Fang], Wang, X.[Xiao], Chen, X.J.[Xiao-Jun],
A Penalty Relaxation Method for Image Processing Using Euler's Elastica Model,
SIIMS(14), No. 1, 2021, pp. 389-417.
DOI Link 2104
BibRef

Wang, Y.[Yiru], Liu, Y.L.[Yin-Long], Li, X.C.[Xue-Chen], Wang, C.[Chen], Wang, M.[Manning], Song, Z.J.[Zhi-Jian],
Practical globally optimal consensus maximization by Branch-and-bound based on interval arithmetic,
PR(115), 2021, pp. 107897.
Elsevier DOI 2104
Consensus maximization, Globally optimization, Robust model fitting, Branch-and-bound, Interval arithmetic BibRef

Zheng, B.[Baifu], Zeng, C.[Cao], Li, S.D.[Shi-Dong], Liao, G.S.[Gui-Sheng],
Joint Sparse Recovery for Signals of Spark-Level Sparsity and MMV Tail-L_2,1 Minimization,
SPLetters(28), 2021, pp. 1130-1134.
IEEE DOI 2106
Minimization, Signal processing algorithms, Coordinate measuring machines, Upper bound, tail minimization BibRef

Zhang, J.[Jing], Zhang, S.G.[Shu-Guang],
Null Space Property of L_1-2 Minimization With Prior Support Information,
SPLetters(28), 2021, pp. 1779-1783.
IEEE DOI 2109
Minimization, Null space, Standards, Robustness, Tools, Sensors, Video compression, Compressed sensing, L_1-2 minimization, null space property BibRef

Tan, X.J.[Xu-Jie], Shin, S.Y.[Seong-Yoon],
A clustering-based differential evolution with parapatric and cross-generation selection,
IJCVR(11), No. 5, 2021, pp. 497-511.
DOI Link 2109
evolutionary algorithms (EA) for continuous optimisation. BibRef

Karsznia, I.[Izabela], Golebiowska, I.M.[Izabela Malgorzata], Korycka-Skorupa, J.[Jolanta], Nowacki, T.[Tomasz],
Searching for an Optimal Hexagonal Shaped Enumeration Unit Size for Effective Spatial Pattern Recognition in Choropleth Maps,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Liu, R.S.[Ri-Sheng], Mu, P.[Pan], Zhang, J.[Jin],
Investigating Customization Strategies and Convergence Behaviors of Task-Specific ADMM,
IP(30), 2021, pp. 8278-8292.
IEEE DOI 2110
Alternating Direction Method of Multiplier. Task analysis, Convergence, Convex functions, Optimization, Plugs, Noise reduction, Linear programming, Task-specific ADMM, computer vision applications BibRef

Zhu, Y.N.[Ya-Nan], Zhang, X.Q.[Xiao-Qun],
A Stochastic Variance Reduced Primal Dual Fixed Point Method for Linearly Constrained Separable Optimization,
SIIMS(14), No. 3, 2021, pp. 1326-1353.
DOI Link 2110
BibRef

Chai, W.H., Ho, S.S., Quek, H.C.,
A Novel Quasi-Newton Method for Composite Convex Minimization,
PR(122), 2022, pp. 108281.
Elsevier DOI 2112
non-smooth, proximal mapping, quasi-Newton BibRef

Huang, Z.[Zeren], Wang, K.[Kerong], Liu, F.[Furui], Zhen, H.L.[Hui-Ling], Zhang, W.N.[Wei-Nan], Yuan, M.X.[Ming-Xuan], Hao, J.Y.[Jian-Ye], Yu, Y.[Yong], Wang, J.[Jun],
Learning to Select Cuts for Efficient Mixed-Integer Programming,
PR(123), 2022, pp. 108353.
Elsevier DOI 2112
Mixed-Integer programming, Cutting plane, Multiple instance learning, Generalization ability BibRef

Vural, M.[Metin], Aravkin, A.Y.[Aleksandr Y.], Stanczak, S.[Slawomir],
L_1-Norm Minimization With Regula Falsi Type Root Finding Methods,
SPLetters(28), 2021, pp. 2132-2136.
IEEE DOI 2112
Newton method, Mathematical models, Pareto optimization, Minimization, Convergence, Shape, Nonlinear equations, root-finding BibRef

Wei, D.[Dong], Shen, X.B.[Xiao-Bo], Sun, Q.S.[Quan-Sen], Gao, X.Z.[Xi-Zhan], Ren, Z.W.[Zhen-Wen],
Neighborhood preserving embedding on Grassmann manifold for image-set analysis,
PR(122), 2022, pp. 108335.
Elsevier DOI 2112
Neighborhood preserving embedding, Dimensionality reduction, Grassmann manifold, Twin learning BibRef

Lam, B.S.Y.[Benson Shu Yan], Liew, A.W.C.[Alan Wee-Chung],
A Fast Binary Quadratic Programming Solver Based on Stochastic Neighborhood Search,
PAMI(44), No. 1, January 2022, pp. 32-49.
IEEE DOI 2112
Search problems, Quadratic programming, Computational complexity, Image restoration, optimization BibRef

Driggs, D.[Derek], Tang, J.Q.[Jun-Qi], Liang, J.W.[Jing-Wei], Davies, M.[Mike], Schönlieb, C.B.[Carola-Bibiane],
A Stochastic Proximal Alternating Minimization for Nonsmooth and Nonconvex Optimization,
SIIMS(14), No. 4, 2021, pp. 1932-1970.
DOI Link 2112
BibRef

Sarada, B., Vinayaka Murthy, M., Udaya Rani, V.,
Combined secure approach based on whale optimization to improve the data classification for data analytics,
PRL(152), 2021, pp. 327-332.
Elsevier DOI 2112
Data clustering, Multi-layer propagation, Optimization, Attacks, DoS, DDoS, K means BibRef

Diepeveen, W.[Willem], Lellmann, J.[Jan],
An Inexact Semismooth Newton Method on Riemannian Manifolds with Application to Duality-Based Total Variation Denoising,
SIIMS(14), No. 4, 2021, pp. 1565-1600.
DOI Link 2112
BibRef

Helala, M.A.[Mohamed A.], Qureshi, F.Z.[Faisal Z.], Pu, K.Q.[Ken Q.],
A Stream Algebra for Performance Optimization of Large Scale Computer Vision Pipelines,
PAMI(44), No. 2, February 2022, pp. 905-923.
IEEE DOI 2201
Streaming media, Algebra, Pipelines, Approximation algorithms, Optimization, Buildings, Stream algebra, performance optimization BibRef

Zhang, L.H.[Lei-Hong], Wang, L.[Li], Bai, Z.J.[Zhao-Jun], Li, R.C.[Ren-Cang],
A Self-Consistent-Field Iteration for Orthogonal Canonical Correlation Analysis,
PAMI(44), No. 2, February 2022, pp. 890-904.
IEEE DOI 2201
Correlation, Feature extraction, Eigenvalues and eigenfunctions, Manifolds, Data visualization, orthogonal multiset canonical correlation analysis BibRef

Liu, R.S.[Ri-Sheng], Ma, L.[Long], Yuan, X.M.[Xiao-Ming], Zeng, S.Z.[Shang-Zhi], Zhang, J.[Jin],
Task-Oriented Convex Bilevel Optimization With Latent Feasibility,
IP(31), 2022, pp. 1190-1203.
IEEE DOI 2202
Task analysis, Optimization, Convergence, Convex functions, Data models, Computational modeling, Standards, image processing BibRef

Ali, A.[Anum], Moinuddin, M.[Muhammad], Al-Naffouri, T.Y.[Tareq Y.],
NLMS is More Robust to Input-Correlation Than LMS: A Proof,
SPLetters(29), 2022, pp. 279-283.
IEEE DOI 2202
Eigenvalues and eigenfunctions, Signal processing algorithms, Sensitivity, Random variables, Closed-form solutions, Standards, eigenvalue-spread BibRef

Yan, D.[Dong], Weng, J.Y.[Jia-Yi], Huang, S.Y.[Shi-Yu], Li, C.X.[Chong-Xuan], Zhou, Y.[Yichi], Su, H.[Hang], Zhu, J.[Jun],
Deep reinforcement learning with credit assignment for combinatorial optimization,
PR(124), 2022, pp. 108466.
Elsevier DOI 2203
Combinatorial optimization, Reinforcement learning, Credit assignment BibRef

Xue, C.[Chao], Hu, M.T.[Meng-Ting], Huang, X.Q.[Xue-Qi], Li, C.G.[Chun-Guang],
Automated search space and search strategy selection for AutoML,
PR(124), 2022, pp. 108474.
Elsevier DOI 2203
AutoML, Search space selection, Combinatorial optimization for AutoML BibRef

Deng, W.[Wu], Xu, J.J.[Jun-Jie], Zhao, H.M.[Hui-Min], Song, Y.J.[Ying-Jie],
A Novel Gate Resource Allocation Method Using Improved PSO-Based QEA,
ITS(23), No. 3, March 2022, pp. 1737-1745.
IEEE DOI 2203
Gate allocation at airport. Resource management, Airports, Atmospheric modeling, Sociology, Statistics, Optimization, Gate allocation model, QEA, PSO, multi-objective BibRef

Lai, Q.D.[Qi-Dong], Zhang, Z.Z.[Zi-Zhen], Yu, M.Z.[Ming-Zhu], Wang, J.[Jiahai],
Split-Delivery Capacitated Arc-Routing Problem With Time Windows,
ITS(23), No. 3, March 2022, pp. 2882-2887.
IEEE DOI 2203
Routing, Intelligent transportation systems, Heuristic algorithms, Computational modeling, Mathematical model, tabu search BibRef

Qing, Z.[Zhu], Ni, J.G.[Jin-Gen],
An Improved Least Stochastic Entropy Algorithm for Strong Noncircular Inputs and Noise,
SPLetters(29), 2022, pp. 1007-1011.
IEEE DOI 2205
Signal processing algorithms, Convergence, Steady-state, Noise measurement, Entropy, Prediction algorithms, performance analysis BibRef

Wang, S.J.[Shu-Jian], Xu, M.[Ming], Zhang, X.[Xunhe], Wang, Y.T.[Yu-Ting],
Fitting Nonlinear Equations with the Levenberg-Marquardt Method on Google Earth Engine,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Wang, K.X.[Kuang-Xu], Ye, S.J.[Si-Jing], Gao, P.C.[Pei-Chao], Yao, X.C.[Xiao-Chuang], Zhao, Z.L.[Zu-Liang],
Optimization of Numerical Methods for Transforming UTM Plane Coordinates to Lambert Plane Coordinates,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Béjar, B.[Benjamín], Dokmanic, I.[Ivan], Vidal, R.[René],
The Fastest L_1,inf Prox in the West,
PAMI(44), No. 7, July 2022, pp. 3858-3869.
IEEE DOI 2206
Convex functions, Sorting, Signal processing algorithms, Linear programming, Iterative methods, Complexity theory, block sparsity BibRef

Yu, H.[Hang], Lu, J.[Jie], Zhang, G.Q.[Guang-Quan],
Continuous Support Vector Regression for Nonstationary Streaming Data,
Cyber(52), No. 5, May 2022, pp. 3592-3605.
IEEE DOI 2206
Support vector machines, Prediction algorithms, Vegetation, Cybernetics, Quadratic programming, Training, Concept drift, support vector regression (SVR) BibRef

Mirjalili, S.[Seyedali], Gandomi, A.H.[Amir H.], Mirjalili, S.Z.[Seyedeh Zahra], Saremi, S.[Shahrzad], Faris, H.[Hossam], Mirjalili, S.M.[Seyed Mohammad],
Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,
AdvSoftEng(114), 2017, pp. 163-191.
Elsevier DOI Salp optimizing. BibRef 1700

Phuong, T.T.[Tran Thi], Phong, L.T.[Le Trieu],
Decentralized Stochastic Optimization With Random Attendance,
SPLetters(29), 2022, pp. 1322-1326.
IEEE DOI 2206
Training, Optimization, Signal processing algorithms, Convergence, Machine-to-machine communications, Quantization (signal), Costs, random attendance BibRef

Gonzalez, M.[Mario], Almansa, A.[Andres], Tan, P.[Pauline],
Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior,
SIIMS(15), No. 2, 2022, pp. 822-859.
DOI Link 2206
BibRef

Zhang, D.P.[Dao-Ping], Choi, G.P.T.[Gary P. T.], Zhang, J.P.[Jian-Ping], Lui, L.M.[Lok Ming],
A Unifying Framework for n-Dimensional Quasi-Conformal Mappings,
SIIMS(15), No. 2, 2022, pp. 960-988.
DOI Link 2207
BibRef

Huang, M.[Meng], Wang, Y.[Yang],
Linear Convergence of Randomized Kaczmarz Method for Solving Complex-Valued Phaseless Equations,
SIIMS(15), No. 2, 2022, pp. 989-1016.
DOI Link 2207
BibRef

Xiong, H.[Huan], Yu, M.Y.[Meng-Yang], Liu, L.[Li], Zhu, F.[Fan], Qin, J.[Jie], Shen, F.M.[Fu-Min], Shao, L.[Ling],
A Generalized Method for Binary Optimization: Convergence Analysis and Applications,
PAMI(44), No. 9, September 2022, pp. 4524-4543.
IEEE DOI 2208
Convergence, Optimization methods, Approximation algorithms, Quantization (signal), Image segmentation, Matrix decomposition, constrained image segmentation BibRef

Liu, L.[Liu], Liu, J.[Ji], Tao, D.C.[Da-Cheng],
Variance Reduced Methods for Non-Convex Composition Optimization,
PAMI(44), No. 9, September 2022, pp. 5813-5825.
IEEE DOI 2208
Complexity theory, Optimization, Radio frequency, Estimation, Convergence, Approximation algorithms, Acceleration, biased estimation BibRef

Bauermeister, H.[Hartmut], Laude, E.[Emanuel], Mollenhoff, T.[Thomas], Moeller, M.[Michael], Cremers, D.[Daniel],
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields,
SIIMS(15), No. 3, 2022, pp. 1253-1281.
DOI Link 2208
BibRef

Xia, Y.X.[Yu-Xin], Wang, W.[Wei], Han, B.[Bo],
A Fast Averaged Kaczmarz Iteration with Convex Penalty for Inverse Problems in Hilbert Spaces,
SIIMS(15), No. 3, 2022, pp. 1079-1103.
DOI Link 2208
BibRef

Zhao, J.Q.[Jia-Qi], Hu, S.[Shuai], Liu, X.C.[Xi-Chuan], Li, S.L.[Shu-Lei],
The Computational Optimization of the Invariant Imbedding T Matrix Method for the Particles with N-Fold Symmetry,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Lim, Y.C.[Yong Ching], Liu, Q.L.[Qing-Lai], Diniz, P.S.R.[Paulo S. R.], Saramäki, T.[Tapio],
Efficient Scaling of Window Function Expressed as Sum of Exponentials,
SPLetters(29), 2022, pp. 1814-1817.
IEEE DOI 2209
Fourier series, Chebyshev approximation, Computational efficiency, Laboratories, Iterative methods, Indexes, sidelobe magnitude BibRef

Zhou, P.[Pan], Yuan, X.T.[Xiao-Tong], Lin, Z.C.[Zhou-Chen], Hoi, S.C.H.[Steven C.H.],
A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization,
PAMI(44), No. 10, October 2022, pp. 5933-5946.
IEEE DOI 2209
Stochastic processes, Optimization, Computational modeling, Signal processing algorithms, Prediction algorithms, stochastic variance-reduced algorithm BibRef

Feng, K.Y.[Kai-Yuan], Fei, X.[Xia], Gong, M.[Maoguo], Qin, A.K., Li, H.[Hao], Wu, Y.[Yue],
An Automatically Layer-Wise Searching Strategy for Channel Pruning Based on Task-Driven Sparsity Optimization,
CirSysVideo(32), No. 9, September 2022, pp. 5790-5802.
IEEE DOI 2209
Task analysis, Knowledge engineering, Training, Cost function, Convolutional neural networks, Computational modeling, Tensors, compression BibRef

Dytso, A.[Alex], Cardone, M.[Martina], Zieder, I.[Ian],
High-Noise Asymptotics of the Ziv-Zakai Bound,
SPLetters(29), 2022, pp. 1933-1937.
IEEE DOI 2209
Noise level, Bayes methods, Random variables, Noise measurement, Upper bound, Probability density function, Estimation, MMSE BibRef

Zhang, X.J.[Xue-Jun], Feng, D.Z.[Da-Zheng],
An Efficient MUSIC Algorithm Enhanced by Iteratively Estimating Signal Subspace and Its Applications in Spatial Colored Noise,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
MUSIC -- multiple signal classification. BibRef

Bieker, K.[Katharina], Gebken, B.[Bennet], Peitz, S.[Sebastian],
On the Treatment of Optimization Problems With L1 Penalty Terms via Multiobjective Continuation,
PAMI(44), No. 11, November 2022, pp. 7797-7808.
IEEE DOI 2210
Optimization, Training, Neural networks, Mathematical models, Linear programming, Signal processing, Pareto optimization, sparsity BibRef

Pokala, P.K.[Praveen Kumar], Hemadri, R.V.[Raghu Vamshi], Seelamantula, C.S.[Chandra Sekhar],
Iteratively Reweighted Minimax-Concave Penalty Minimization for Accurate Low-rank Plus Sparse Matrix Decomposition,
PAMI(44), No. 12, December 2022, pp. 8992-9010.
IEEE DOI 2212
Sparse matrices, Optimization, Matrix decomposition, Minimization, Convergence, Image reconstruction, Costs, nonconvex penalty BibRef

Li, X.Y.[Xiao-Yong], Li, H.[Houpu], Liu, G.H.[Guo-Hui], Bian, S.F.[Shao-Feng],
Optimization of Complex Function Expansions for Gauss-Kruger Projections,
IJGI(11), No. 11, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Liu, R.S.[Ri-Sheng], Mu, P.[Pan], Yuan, X.M.[Xiao-Ming], Zeng, S.Z.[Shang-Zhi], Zhang, J.[Jin],
A General Descent Aggregation Framework for Gradient-Based Bi-Level Optimization,
PAMI(45), No. 1, January 2023, pp. 38-57.
IEEE DOI 2212
Optimization, Convergence, Task analysis, Heuristic algorithms, Dynamical systems, Approximation algorithms, meta-learning BibRef

Ma, J.[Jun], Cheng, Z.L.[Zi-Long], Zhang, X.X.[Xiao-Xue], Tomizuka, M.[Masayoshi], Lee, T.H.[Tong Heng],
Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 23031-23042.
IEEE DOI 2212
Trajectory, Autonomous vehicles, Optimization, Planning, Convex functions, Vehicle dynamics, Safety, Autonomous driving, non-convex optimization BibRef

Zheng, Z.Y.[Zi-Yang], Dai, W.R.[Wen-Rui], Xue, D.D.[Duo-Duo], Li, C.L.[Cheng-Lin], Zou, J.[Junni], Xiong, H.K.[Hong-Kai],
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks,
PAMI(45), No. 3, March 2023, pp. 3226-3244.
IEEE DOI 2302
Iterative Shrinkage Thresholding Algorithm. Convergence, Inverse problems, Neural networks, Deep learning, Logic gates, Iterative algorithms, Network architecture, theoretical convergence BibRef

Xie, X.Y.[Xing-Yu], Wang, Q.[Qiuhao], Ling, Z.[Zenan], Li, X.[Xia], Liu, G.C.[Guang-Can], Lin, Z.C.[Zhou-Chen],
Optimization Induced Equilibrium Networks: An Explicit Optimization Perspective for Understanding Equilibrium Models,
PAMI(45), No. 3, March 2023, pp. 3604-3616.
IEEE DOI 2302
Optimization, Mathematical models, Artificial neural networks, Training, Convex functions, Analytical models, Semantics, interpretability BibRef

Look, A.[Andreas], Kandemir, M.[Melih], Rakitsch, B.[Barbara], Peters, J.[Jan],
A Deterministic Approximation to Neural SDEs,
PAMI(45), No. 4, April 2023, pp. 4023-4037.
IEEE DOI 2303
Neural Stochastic Differential Equations. Kernel, Uncertainty, Monte Carlo methods, Stochastic processes, Calibration, Costs, Computational efficiency, uncertainty propagation BibRef

Deshmukh, A.[Aditya], Liu, J.[Jing], Veeravalli, V.V.[Venugopal V.], Verma, G.[Gunjan],
Information Flow Optimization for Estimation in Linear Models Using a Sensor Network,
SPLetters(30), 2023, pp. 170-174.
IEEE DOI 2303
Optimization, Relays, Bayes methods, Estimation, Quantization (signal), Task analysis, Upper bound, Internet of Things BibRef

Wang, Z.[Zitai], Xu, Q.Q.[Qian-Qian], Yang, Z.Y.[Zhi-Yong], He, Y.[Yuan], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
Optimizing Partial Area Under the Top-k Curve: Theory and Practice,
PAMI(45), No. 4, April 2023, pp. 5053-5069.
IEEE DOI 2303
Measurement, Semantics, Benchmark testing, Training, Loss measurement, Fasteners, Upper bound, Machine learning, AUTKC optimization BibRef

Rusu, C.[Cristian], Gonzalez-Prelcic, N.[Nuria],
A Novel Approach for Unit-Modulus Least-Squares Optimization Problems,
SPLetters(30), 2023, pp. 224-228.
IEEE DOI 2303
Optimization, Iterative methods, Millimeter wave communication, Linear programming, Signal processing algorithms, hybrid precoding and combining BibRef

Yang, M.H.[Ming-Han], Xu, D.[Dong], Cui, Q.W.[Qi-Wen], Wen, Z.[Zaiwen], Xu, P.X.[Peng-Xiang],
An Efficient Fisher Matrix Approximation Method for Large-Scale Neural Network Optimization,
PAMI(45), No. 5, May 2023, pp. 5391-5403.
IEEE DOI 2304
Convergence, Stochastic processes, Loss measurement, Risk management, Residual neural networks, Task analysis, Shape, convergence BibRef

Lutter, M.[Michael], Belousov, B.[Boris], Mannor, S.[Shie], Fox, D.[Dieter], Garg, A.[Animesh], Peters, J.[Jan],
Continuous-Time Fitted Value Iteration for Robust Policies,
PAMI(45), No. 5, May 2023, pp. 5534-5548.
IEEE DOI 2304
Mathematical models, Optimization, Differential equations, Robots, Heuristic algorithms, Reinforcement learning, Costs, dynamic programming adversarial reinforcement learning BibRef

Paul, D.[Debolina], Chakraborty, S.[Saptarshi], Das, S.[Swagatam], Xu, J.[Jason],
Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach,
PAMI(45), No. 5, May 2023, pp. 5862-5871.
IEEE DOI 2304
Kernel, Annealing, Clustering algorithms, Optimization, Convergence, Unsupervised learning, Task analysis, Kernel clustering, strong consistency BibRef

Gao, Z.[Zhi], Wu, Y.W.[Yu-Wei], Fan, X.M.[Xiao-Meng], Harandi, M.[Mehrtash], Jia, Y.D.[Yun-De],
Learning to Optimize on Riemannian Manifolds,
PAMI(45), No. 5, May 2023, pp. 5935-5952.
IEEE DOI 2304
Optimization, Manifolds, Training, Task analysis, Geometry, Trajectory, Stochastic processes, Riemannian optimization, meta-optimization, Riemannian manifolds BibRef

Li, C.R.[Chao-Ran], Jiang, P.L.[Peng-Long], Zhou, H.[Hui], Wang, X.F.[Xiao-Feng], Zhao, X.B.[Xiong-Bo],
HAW: Hardware-Aware Point Selection for Efficient Winograd Convolution,
SPLetters(30), 2023, pp. 269-273.
IEEE DOI 2304
Convolution, Hardware, Quantization (signal), Kernel, Neural networks, Standards, Task analysis, Convolution, Winograd convolution BibRef

Bai, F.[Fang], Bartoli, A.[Adrien],
The Proxy Step-Size Technique for Regularized Optimization on the Sphere Manifold,
PAMI(45), No. 5, May 2023, pp. 6428-6444.
IEEE DOI 2304
Manifolds, Convergence, Costs, Cost function, Gradient methods, Minimization, Mathematical models, Proxy step-size, computer vision BibRef

Zhang, X.Q.[Xiao-Qin], Zheng, J.J.[Jing-Jing], Wang, D.[Di], Tang, G.Y.[Gui-Ying], Zhou, Z.Y.[Zheng-Yuan], Lin, Z.C.[Zhou-Chen],
Structured Sparsity Optimization With Non-Convex Surrogates of L_l2,0-Norm: A Unified Algorithmic Framework,
PAMI(45), No. 5, May 2023, pp. 6386-6402.
IEEE DOI 2304
Optimization, Convergence, Sparse matrices, Machine learning, Minimization, Mathematical models, Iterative algorithms, fixed-point algorithm BibRef

Wang, C.X.[Chen-Xin], Zhang, Z.W.[Zhen-Wei], Guo, Z.C.[Zhi-Chang], Zeng, T.Y.[Tie-Yong], Duan, Y.P.[Yu-Ping],
Efficient SAV Algorithms for Curvature Minimization Problems,
CirSysVideo(33), No. 4, April 2023, pp. 1624-1642.
IEEE DOI
WWW Link. 2304
Scalar Auxiliary Variable. Minimization, Mathematical models, Task analysis, Image restoration, TV, Numerical models, Mathematics, Mean curvature, image deblurring BibRef

Jiang, Y.[Yuan], Cao, Z.G.[Zhi-Guang], Zhang, J.[Jie],
Learning to Solve 3-D Bin Packing Problem via Deep Reinforcement Learning and Constraint Programming,
Cyber(53), No. 5, May 2023, pp. 2864-2875.
IEEE DOI 2305
Optimization, Decoding, Reinforcement learning, Search problems, Routing, Task analysis, Standards, Bin packing problem (BPP), multi-task learning BibRef

Dantas, C.F.[Cássio F.], Soubies, E.[Emmanuel], Févotte, C.[Cédric],
Sphere Refinement in Gap Safe Screening,
SPLetters(30), 2023, pp. 608-612.
IEEE DOI 2306
Iterative methods, Signal processing algorithms, Closed-form solutions, Optimization, Convergence, logistic regression BibRef

Ranaweera, S.[Sandushan], Weeraddana, C.[Chathuranga], Dharmawansa, P.[Prathapasinghe], Fischione, C.[Carlo],
On the Convergence of Inexact Gradient Descent With Controlled Synchronization Steps,
SPLetters(30), 2023, pp. 703-707.
IEEE DOI 2307
Signal processing algorithms, Convergence, Quantization (signal), Linear programming, Heuristic algorithms, Synchronization, Indexes, inexact algorithms BibRef

Granot, N.[Nerya], Diskin, T.[Tzvi], Dobigeon, N.[Nicolas], Wiesel, A.[Ami],
Probabilistic Simplex Component Analysis by Importance Sampling,
SPLetters(30), 2023, pp. 683-687.
IEEE DOI 2307
Signal to noise ratio, Monte Carlo methods, Approximation algorithms, Proposals, simplex-structured matrix factorization BibRef

Guo, J.[Jing], Moses, S.[Skip], Wang, Z.H.[Zhao-Hong],
Graph Learning From Signals With Smoothness Superimposed by Regressors,
SPLetters(30), 2023, pp. 942-946.
IEEE DOI 2308
Laplace equations, Signal processing algorithms, Topology, Optimization, Network topology, Predictive models, Laplacian matrix BibRef

Wang, Y.S.[Yin-Song], Ding, Y.[Yu], Shahrampour, S.[Shahin],
TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation,
PAMI(45), No. 11, November 2023, pp. 13831-13843.
IEEE DOI 2310
BibRef

Gnanasambandam, R.[Raghav], Shen, B.[Bo], Chung, J.[Jihoon], Yue, X.[Xubo], Kong, Z.Y.[Zhen-Yu],
Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks,
PAMI(45), No. 12, December 2023, pp. 15588-15603.
IEEE DOI 2311
BibRef

Goehle, G.[Geoff], Cowen, B.[Benjamin],
Joint Sparse Coding and Frame Optimization,
SPLetters(31), 2024, pp. 301-305.
IEEE DOI 2402
Signal processing algorithms, Transforms, Machine learning, Convergence, Noise reduction, Minimization, Encoding, compressed-sensing BibRef

Joseph, G.[Geethu],
Convergence of Expectation-Maximization Algorithm With Mixed-Integer Optimization,
SPLetters(31), 2024, pp. 1229-1233.
IEEE DOI 2405
Convergence, Signal processing algorithms, Heuristic algorithms, Sparse matrices, Maximum likelihood estimation, Bayes methods, bursty missing data BibRef

Chen, J.H.[Jun-Hong], Li, H.[Hong], Chen, C.L.P.[C.L. Philip],
Boosting sharpness-aware training with dynamic neighborhood,
PR(153), 2024, pp. 110496.
Elsevier DOI 2405
Flat minima, Generalization, Optimization, Sharpness-aware minimization BibRef

Liu, H.[He], Wang, T.[Tao], Lang, C.[Congyan], Feng, S.[Songhe], Jin, Y.[Yi], Li, Y.D.[Yi-Dong],
GLAN: A graph-based linear assignment network,
PR(155), 2024, pp. 110694.
Elsevier DOI 2408
Linear assignment, Graph networks, Learning-based solver, Multi-object tracking BibRef

Guan, L.[Lei], Li, D.S.[Dong-Sheng], Shi, Y.Q.[Yan-Qi], Meng, J.[Jian],
XGrad: Boosting Gradient-Based Optimizers With Weight Prediction,
PAMI(46), No. 10, October 2024, pp. 6731-6747.
IEEE DOI 2409
Training, Artificial neural networks, Convergence, Computational modeling, Backpropagation, Proposals, weight prediction BibRef

Boquet-Pujadas, A.[Aleix], del Aguila-Pla, P.[Pol], Unser, M.[Michael],
Sensitivity-Aware Density Estimation in Multiple Dimensions,
PAMI(46), No. 11, November 2024, pp. 7120-7135.
IEEE DOI 2410
Splines (mathematics), Bandwidth, Sensitivity, Kernel, Imaging, Estimation, Standards, Weighted density estimation, PET BibRef

Akaishi, N.[Natsuki], Yamada, K.[Koki], Yatabe, K.[Kohei],
Harmonic/Percussive Source Separation Based on Anisotropic Smoothness of Magnitude Spectrograms via Convex Optimization,
SPLetters(31), 2024, pp. 2575-2579.
IEEE DOI 2410
Spectrogram, Anisotropic, Time-frequency analysis, Harmonic analysis, Optimization, Smoothing methods, primal-dual splitting BibRef

Yang, R.H.[Rui-Heng], Chen, Z.[Zhikun], Hu, L.T.[Ling-Tong], Cui, X.[Xihang], Guo, Y.F.[Yun-Fei],
An FPGA-Based Approach for Compressing and Accelerating Depthwise Separable Convolution,
SPLetters(31), 2024, pp. 2590-2594.
IEEE DOI 2410
Convolution, Optimization, Throughput, Resource management, Quantization (signal), Parallel processing, hardware accelerator BibRef

Wang, H.B.[Hui-Bing], Yao, M.Z.[Ming-Ze], Chen, Y.[Yawei], Xu, Y.Q.[Yun-Qiu], Liu, H.P.[Hai-Peng], Jia, W.[Wei], Fu, X.P.[Xian-Ping], Wang, Y.[Yang],
Manifold-Based Incomplete Multi-View Clustering via Bi-Consistency Guidance,
MultMed(26), 2024, pp. 10001-10014.
IEEE DOI 2410
Task analysis, Manifolds, Kernel, Clustering algorithms, Optimization, Data models, Tensors, Biconsistency guidance, reverse regularization BibRef

Wang, J.[Jie], Wang, Z.H.[Zhi-Hai], Li, X.[Xijun], Kuang, Y.F.[Yu-Fei], Shi, Z.H.[Zhi-Hao], Zhu, F.Z.[Fang-Zhou], Yuan, M.X.[Ming-Xuan], Zeng, J.[Jia], Zhang, Y.D.[Yong-Dong], Wu, F.[Feng],
Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming,
PAMI(46), No. 12, December 2024, pp. 9697-9713.
IEEE DOI 2411
Training, Linear programming, Learning systems, Benchmark testing, Vehicle routing, Task analysis, Production planning, sequence/set to sequence learning BibRef

Chen, H.[Hao], Barthel, T.[Thomas],
Machine Learning With Tree Tensor Networks, CP Rank Constraints, and Tensor Dropout,
PAMI(46), No. 12, December 2024, pp. 7825-7832.
IEEE DOI 2411
Tensors, Vectors, Costs, Physics, Machine learning, Quantum state, Quantum entanglement, Machine learning, image classification, tensor dropout BibRef

Farzaneh, A.[Amirmohammad], Park, S.[Sangwoo], Simeone, O.[Osvaldo],
Quantile Learn-Then-Test: Quantile-Based Risk Control for Hyperparameter Optimization,
SPLetters(31), 2024, pp. 3044-3048.
IEEE DOI 2411
Reliability, Calibration, Artificial intelligence, Testing, Optimization, Wireless communication, quantiles BibRef

Rydell, F.[Felix], Torres, A.[Angélica], Larsson, V.[Viktor],
Revisiting Sampson Approximations for Geometric Estimation Problems,
CVPR24(4990-4998)
IEEE DOI 2410
Measurement, Geometry, Computational modeling, Perturbation methods, Fitting, Estimation, Sampson approximation, geometric estimation BibRef

Jeong, W.[Wooseong], Yoon, K.J.[Kuk-Jin],
Quantifying Task Priority for Multi-Task Optimization,
CVPR24(363-372)
IEEE DOI 2410
Training, Backpropagation, Pareto optimization, Multitasking, Linear programming, Boosting, Multi-Task Learning (MTL), Task Priority BibRef

Abraham, S.[Sophia], Maduranga, K.D.G.[Kehelwala Dewage Gayan], Kinnison, J.[Jeffery], Hauenstein, J.[Jonathan], Scheirer, W.[Walter],
NCQS: Nonlinear Convex Quadrature Surrogate Hyperparameter Optimization,
REDLCV23(1187-1195)
IEEE DOI 2401
BibRef

Chiaroni, F.[Florent], Dolz, J.[Jose], Masud, Z.I.[Ziko Imtiaz], Mitiche, A.[Amar], Ben Ayed, I.[Ismail],
Parametric Information Maximization for Generalized Category Discovery,
ICCV23(1729-1739)
IEEE DOI Code:
WWW Link. 2401
BibRef

Xu, J.Y.[Jing-Yi], Vaidya, T.[Tushar], Wu, Y.F.[Yu-Fei], Chandra, S.[Saket], Lai, Z.[Zhangsheng], Chong, K.F.E.[Kai Fong Ernest],
Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices,
CVPR23(6715-6724)
IEEE DOI 2309
BibRef

Zhang, T.Y.[Tian-Yu], Banitalebi-Dehkordi, A.[Amin], Zhang, Y.[Yong],
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch,
ICPR22(3105-3111)
IEEE DOI 2212
Deep learning, Systematics, Monte Carlo methods, NP-hard problem, Input variables, Reinforcement learning BibRef

Lin, G.X.[Gang-Xuan], Hu, S.W.[Shih-Wei], Lu, C.S.[Chun-Shien],
QISTA-ImageNet: A Deep Compressive Image Sensing Framework Solving Lq-Norm Optimization Problem,
ECCV22(XXIII:406-422).
Springer DOI 2211
BibRef

Xi, T.[Teng], Sun, Y.F.[Yi-Fan], Yu, D.L.[De-Li], Li, B.[Bi], Peng, N.[Nan], Zhang, G.[Gang], Zhang, X.Y.[Xin-Yu], Wang, Z.G.[Zhi-Gang], Chen, J.W.[Jin-Wen], Wang, J.[Jian], Liu, L.[Lufei], Feng, H.C.[Hao-Cheng], Han, J.Y.[Jun-Yu], Liu, J.[Jingtuo], Ding, E.[Errui], Wang, J.D.[Jing-Dong],
UFO: Unified Feature Optimization,
ECCV22(XXVI:472-488).
Springer DOI 2211
BibRef

Yurtsever, A.[Alp], Birdal, T.[Tolga], Golyanik, V.[Vladislav],
Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization,
ECCV22(XXIII:352-369).
Springer DOI 2211
BibRef

Lin, Y.[Yong], Dong, H.[Hanze], Wang, H.[Hao], Zhang, T.[Tong],
Bayesian Invariant Risk Minimization,
CVPR22(16000-16009)
IEEE DOI 2210
Representation learning, Deep learning, Computational modeling, Training data, Feature extraction, Data models, Statistical methods BibRef

Hruby, P.[Petr], Duff, T.[Timothy], Leykin, A.[Anton], Pajdla, T.[Tomas],
Learning to Solve Hard Minimal Problems,
CVPR22(5522-5532)
IEEE DOI 2210
Codes, Machine learning, Cameras, Resource description framework, Sensors, 3D from multi-view and sensors, Photogrammetry and remote sensing BibRef

Yeh, R.A.[Raymond A.], Hu, Y.T.[Yuan-Ting], Ren, Z.Z.[Zhong-Zheng], Schwing, A.G.[Alexander G.],
Total Variation Optimization Layers for Computer Vision,
CVPR22(701-711)
IEEE DOI 2210
Location awareness, TV, Smoothing methods, Image edge detection, Minimization, Optimization methods BibRef

Xu, N.[Nuo], Chang, J.L.[Jian-Long], Nie, X.[Xing], Huo, C.L.[Chun-Lei], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
AME: Attention and Memory Enhancement in Hyper-Parameter Optimization,
CVPR22(480-489)
IEEE DOI 2210
Training, Performance evaluation, Visualization, Semantics, Reinforcement learning, Search problems, Transformers, grouping and shape analysis BibRef

Kong, K.Z.[Ke-Zhi], Li, G.H.[Guo-Hao], Ding, M.[Mucong], Wu, Z.[Zuxuan], Zhu, C.[Chen], Ghanem, B.[Bernard], Taylor, G.[Gavin], Goldstein, T.[Tom],
Robust Optimization as Data Augmentation for Large-scale Graphs,
CVPR22(60-69)
IEEE DOI 2210
Training, Privacy, Social networking (online), Perturbation methods, Pipelines, Data models, Stability analysis BibRef

El Hajjami, I.[Imad], Benhala, B.[Bachir],
Simulation Experiments of Different Metaheuristics Algorithms using Benchmark Functions: A Performance Study,
ISCV22(1-6)
IEEE DOI 2208
Computational modeling, Metaheuristics, Benchmark testing, Search problems, Robustness, Problem-solving, Invasive Weed Optimization (IWO) BibRef

Nakano, G.[Gaku],
Algebraic Constraint for Preserving Convexity of Planar Homography,
3DV21(126-135)
IEEE DOI 2201
Transmission line matrix methods, Estimation, Eigenvalues and eigenfunctions, Linear matrix inequalities, Computational efficiency BibRef

Vajargah, B.F.[Behrouz Fathi], Yousefpanah, K.[Kolsoum],
Efficient scramble for quasi-random numbers in Monte Carlo computations,
IPRIA21(1-6)
IEEE DOI 2201
Monte Carlo methods, Image analysis, Generators, Computational efficiency, Matlab, p-quasi random numbers BibRef

Hasan, M.R.[Mohammad Rakib], Mondal, D.[Debajyoti], Tasnim, J.[Jarin], Schneider, K.A.[Kevin A.],
Putting Table Cartograms into Practice,
ISVC21(I:91-102).
Springer DOI 2112
BibRef

Sun, C.X.[Chen-Xiang], Gan, Y.H.[Yan-Hai], Zhang, J.H.[Jian-Hui], Dong, J.Y.[Jun-Yu],
InterAdam: Interpolating Dull Intervention to Adaptive Gradient Method,
ICIVC21(215-221)
IEEE DOI 2112
Training, Interpolation, Gradient methods, Adaptive learning, Robustness, Convergence, Adaptive learning rate, saddle point BibRef

Zhang, E.[Erchuan], Suter, D.[David], Tennakoon, R.[Ruwan], Chin, T.J.[Tat-Jun], Bab-Hadiashar, A.[Alireza], Truong, G.[Giang], Gilani, S.Z.[Syed Zulqarnain],
Maximum Consensus by Weighted Influences of Monotone Boolean Functions,
CVPR22(8954-8962)
IEEE DOI 2210
Weight measurement, Boolean functions, Atmospheric measurements, Machine vision, Estimation, Gain measurement, Vision applications and systems BibRef

Tennakoon, R.[Ruwan], Suter, D.[David], Zhang, E.[Erchuan], Chin, T.J.[Tat-Jun], Bab-Hadiashar, A.[Alireza],
Consensus Maximisation Using Influences of Monotone Boolean Functions,
CVPR21(2865-2874)
IEEE DOI 2111
For fitting. Visualization, Boolean functions, Runtime, Computational modeling, Fitting, Memory management BibRef

Wang, P.[Pei], Nagrecha, K.[Kabir], Vasconcelos, N.M.[Nuno M.],
Gradient-based Algorithms for Machine Teaching,
CVPR21(1387-1396)
IEEE DOI 2111
Training, Machine learning algorithms, Education, Sociology, Classification algorithms, Risk management BibRef

Tancik, M.[Matthew], Mildenhall, B.[Ben], Wang, T.[Terrance], Schmidt, D.[Divi], Srinivasan, P.P.[Pratul P.], Barron, J.T.[Jonathan T.], Ng, R.[Ren],
Learned Initializations for Optimizing Coordinate-Based Neural Representations,
CVPR21(2845-2854)
IEEE DOI 2111
Solid modeling, Limiting, Shape, Trajectory, Task analysis BibRef

Pamplona, D.[Daniela], Manzanera, A.[Antoine],
Naturally Constrained Online Expectation Maximization,
ICPR21(5429-5435)
IEEE DOI 2105
Protocols, Transfer learning, Training data, Interference, Probabilistic logic, Time-varying systems BibRef

Pande, N.[Nilay], Awate, S.P.[Suyash P.],
Generative Deep-Neural-Network Mixture Modeling with Semi-Supervised MinMax+EM Learning,
ICPR21(5666-5673)
IEEE DOI 2105
Neural networks, Mixture models, Transforms, Semisupervised learning, Generative adversarial networks, image clustering BibRef

Kawashima, T.[Takumi], Yu, Q.[Qina], Asai, A.[Akari], Ikami, D.[Daiki], Aizawa, K.[Kiyoharu],
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals,
ICPR21(1438-1445)
IEEE DOI 2105
Measurement, Deep learning, Estimation error, Uncertainty, Estimation, Data models BibRef

Petunin, A.[Alexander], Khalyavka, A.[Alexander], Khachay, M.[Michael], Kudriavtsev, A.[Andrei], Chentsov, P.[Pavel], Polishchuk, E.[Efim], Ukolov, S.[Stanislav],
Library of Sample Image Instances for the Cutting Path Problem,
IMTA20(227-233).
Springer DOI 2103
Complex continuous and combinatorial optimization problem that is about finding an optimal tool path for CNC technologies equipment. BibRef

Li, N.[Na], Lei, T.F.[Teng-Fei],
Dynamics Analysis on T chaotic systems with absolute,
CVIDL20(291-296)
IEEE DOI 2102
bifurcation, chaos, Lyapunov methods, system dynamics, dynamics analysis, T chaotic systems, system parameters, bifurcation diagram BibRef

Basmassi, M.A., Benameur, L., Chentoufi, J.A.,
A Novel Greedy Genetic Algorithm to Solve Combinatorial Optimization Problem,
SmartCityApp20(117-120).
DOI Link 2012
BibRef

Qaraad, M., Amjad, S., El-Kafrawy, P., Fathi, H., Manhrawy, I.I.M.,
Parameters Optimization of Elastic NET for High Dimensional Data using PSO Algorithm,
ISCV20(1-7)
IEEE DOI 2011
bioinformatics, cancer, data analysis, feature selection, genetics, medical computing, particle swarm optimisation, Particle Swarm Optimization (PSO) and Parameter Optimization BibRef

Zach, C.[Christopher], Le, H.[Huu],
Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning,
ECCV20(XXVI:464-480).
Springer DOI 2011
BibRef

Kuo, N.I.H.[Nicholas I-Hsien], Harandi, M.[Mehrtash], Fourrier, N.[Nicolas], Walder, C.[Christian], Ferraro, G.[Gabriela], Suominen, H.[Hanna],
M2SGD: Learning to Learn Important Weights,
CLVision20(957-964)
IEEE DOI 2008

WWW Link. Training, Optimization, Recurrent neural networks, Computer architecture, Task analysis, Image recognition, Australia BibRef

Liu, J.J.[Jun-Jie], Wen, D.C.[Dong-Chao], Wang, D.Y.[De-Yu], Tao, W., Chen, T.W.[Tse-Wei], Osa, K.[Kinya], Kato, M.[Masami],
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model,
EDLCV20(2880-2889)
IEEE DOI 2008
Convergence, Quantization (signal), Adaptation models, Training, Optimization methods, Analytical models BibRef

Sadeghi, B., Boddeti, V.N.,
Imparting Fairness to Pre-Trained Biased Representations,
TCV20(75-82)
IEEE DOI 2008
Optimization, Task analysis, Kernel, Neural networks, Closed-form solutions, Stochastic processes BibRef

Sun, W., Jiang, W., Trulls, E., Tagliasacchi, A., Yi, K.M.,
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning,
CVPR20(11283-11292)
IEEE DOI 2008
Robustness, Neural networks, Kernel, Optimization, Standards BibRef

Kato, Z.[Zoltan], Nagy, G.[Gabor], Humenberger, M.[Martin], Csurka, G.[Gabriela],
Detecting Low-Rank Regions in Omnidirectional Images,
OmniCV21(3677-3687)
IEEE DOI 2109
Training, Adaptation models, Predictive models, Distortion BibRef

Csurka, G., Kato, Z., Juhasz, A., Humenberger, M.,
Estimating Low-Rank Region Likelihood Maps,
CVPR20(13773-13782)
IEEE DOI 2008
Cameras, Transmission line matrix methods, Windows, Sparse matrices, Optimization BibRef

Messaoud, S.[Safa], Kumar, M.[Maghav], Schwing, A.G.[Alexander G.],
Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning?,
CVPR20(7586-7596)
IEEE DOI 2008
BibRef
And: DeepVision20(3313-3323)
IEEE DOI 2008
Semantics, Optimization, Image segmentation, Task analysis, Inference algorithms, Approximation algorithms, Learning (artificial intelligence) BibRef

Rolínek, M.[Michal], Swoboda, P.[Paul], Zietlow, D.[Dominik], Paulus, A.[Anselm], Musil, V.[Vít], Martius, G.[Georg],
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers,
ECCV20(XXVIII:407-424).
Springer DOI 2011
BibRef

Rolínek, M.[Michal], Musil, V.[Vít], Paulus, A.[Anselm], Vlastelica, M., Michaelis, C., Martius, G.[Georg],
Optimizing Rank-Based Metrics With Blackbox Differentiation,
CVPR20(7617-7627)
IEEE DOI 2008
Measurement, Optimization, Task analysis, Object detection, Image retrieval, Interpolation BibRef

Li, D., Chen, Q.,
Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives,
CVPR20(7639-7648)
IEEE DOI 2008
Training, Optimization, Neural networks, Computational modeling, Probabilistic logic, Task analysis, Visualization BibRef

Gao, Z., Wu, Y., Jia, Y., Harandi, M.,
Learning to Optimize on SPD Manifolds,
CVPR20(7697-7706)
IEEE DOI 2008
Manifolds, Optimization, Symmetric matrices, Task analysis, Matrix decomposition, Geometry BibRef

Chen, B., Parra, Á., Cao, J., Li, N., Chin, T.,
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization,
CVPR20(8097-8106)
IEEE DOI 2008
Optimization, Cameras, Task analysis, Pose estimation, Machine learning, Neural networks BibRef

Valtonen Örnhag, M., Olsson, C.,
A Unified Optimization Framework for Low-Rank Inducing Penalties,
CVPR20(8471-8480)
IEEE DOI 2008
Optimization, Principal component analysis, Standards, Image reconstruction, Linear programming BibRef

Benkner, M.S., Golyanik, V., Theobalt, C., Moeller, M.,
Adiabatic Quantum Graph Matching with Permutation Matrix Constraints,
3DV20(583-592)
IEEE DOI 2102
computational complexity, graph theory, matrix algebra, quadratic programming, quantum computing, Shape BibRef

Golyanik, V., Theobalt, C.,
A Quantum Computational Approach to Correspondence Problems on Point Sets,
CVPR20(9179-9188)
IEEE DOI 2008
Quantum computing, Annealing, Adiabatic, Stationary state, Optimization BibRef

Werner, T.[Tomáš], Pruša, D.[Daniel], Dlask, T.[Tomáš],
Relative Interior Rule in Block-Coordinate Descent,
CVPR20(7556-7564)
IEEE DOI 2008
Convex functions, Linear programming, Message passing, Graphical models, Standards BibRef

Bhayani, S.[Snehal], Kukelova, Z.[Zuzana], Heikkilä, J.[Janne],
Computing stable resultant-based minimal solvers by hiding a variable,
ICPR21(6104-6111)
IEEE DOI 2105
BibRef
Earlier:
A Sparse Resultant Based Method for Efficient Minimal Solvers,
CVPR20(1767-1776)
IEEE DOI 2008
Geometry, Pose estimation, Tools, Cameras, Eigenvalues and eigenfunctions, multiple view geometry. Cameras, Estimation, Generators, Robustness BibRef

Li, C.M.[Chu-Ming], Yuan, X.[Xin], Lin, C.[Chen], Guo, M.H.[Ming-Hao], Wu, W.[Wei], Yan, J.J.[Jun-Jie], Ouyang, W.L.[Wan-Li],
AM-LFS: AutoML for Loss Function Search,
ICCV19(8409-8418)
IEEE DOI 2004
learning (artificial intelligence), optimisation, AutoML, hand-crafted heuristics, design space, Face BibRef

Volpi, R.[Riccardo], Murino, V.[Vittorio],
Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets,
ICCV19(7979-7988)
IEEE DOI 2004
combinatorial mathematics, image classification, image segmentation, optimisation, search problems, Learning systems BibRef

Zach, C., Bourmaud, G.,
Pareto Meets Huber: Efficiently Avoiding Poor Minima in Robust Estimation,
ICCV19(10242-10250)
IEEE DOI 2004
estimation theory, least squares approximations, optimisation, search problems, robust estimation, BibRef

Baráth, D., Matas, J.G.,
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm,
ICCV19(3779-3787)
IEEE DOI 2004
computational geometry, curve fitting, Prog-X, repetitive hypothesis proposal, any-time algorithm, Minimization BibRef

Zhang, Z.W.[Zai-Wei], Liang, Z.X.[Zhen-Xiao], Wu, L.[Lemeng], Zhou, X.W.[Xiao-Wei], Huang, Q.X.[Qi-Xing],
Path-Invariant Map Networks,
CVPR19(11076-11086).
IEEE DOI 2002
BibRef

Zisselman, E.[Ev], Sulam, J.[Jeremias], Elad, M.[Michael],
A Local Block Coordinate Descent Algorithm for the CSC Model,
CVPR19(8200-8209).
IEEE DOI 2002
CSC: Convolutional Sparse Coding. BibRef

Lee, K.[Kwonjoon], Maji, S.[Subhransu], Ravichandran, A.[Avinash], Soatto, S.[Stefano],
Meta-Learning With Differentiable Convex Optimization,
CVPR19(10649-10657).
IEEE DOI 2002
BibRef

Jeong, Y.[Yeonwoo], Kim, Y.[Yoonsung], Song, H.O.[Hyun Oh],
End-To-End Efficient Representation Learning via Cascading Combinatorial Optimization,
CVPR19(11371-11379).
IEEE DOI 2002
BibRef

He, Z.Z.[Zhe-Zhi], Fan, D.L.[De-Liang],
Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network Using Truncated Gaussian Approximation,
CVPR19(11430-11438).
IEEE DOI 2002
BibRef

Probst, T.[Thomas], Paudel, D.P.[Danda Pani], Chhatkuli, A.[Ajad], Van Gool, L.J.[Luc J.],
Unsupervised Learning of Consensus Maximization for 3D Vision Problems,
CVPR19(929-938).
IEEE DOI 2002
BibRef

Xu, W., Hu, L., Tsakiris, M.C., Kneip, L.,
Online Stability Improvement of Gröbner Basis Solvers using Deep Learning,
3DV19(544-552)
IEEE DOI 1911
Cameras, Geometry, Generators, MONOS devices, Deep learning, Gold, Groebner basis, BibRef

Wang, D.[Dan], Ji, M.Q.[Meng-Qi], Wang, Y.[Yong], Wang, H.Q.[Hao-Qian], Fang, L.[Lu],
SPI-Optimizer: An Integral-Separated PI Controller for Stochastic Optimization,
ICIP19(2129-2133)
IEEE DOI 1910
BibRef

Tómasson, J.A.[Jón Arnar], Ochs, P.[Peter], Weickert, J.[Joachim],
AFSI: Adaptive Restart for Fast Semi-Iterative Schemes for Convex Optimisation,
GCPR18(669-681).
Springer DOI 1905
BibRef

Keller, S.M.[Sebastian Mathias], Murezzan, D.[Damian], Roth, V.[Volker],
Invexity Preserving Transformations for Projection Free Optimization with Sparsity Inducing Non-convex Constraints,
GCPR18(682-697).
Springer DOI 1905
BibRef

Malmberg, F.[Filip], Ciesielski, K.C.[Krzysztof Chris], Strand, R.[Robin],
Optimization of Max-Norm Objective Functions in Image Processing and Computer Vision,
DGCI19(206-218).
Springer DOI 1905
BibRef

Abdolshah, M.[Majid], Shilton, A.[Alistair], Rana, S.[Santu], Gupta, S.I.[Sun-Il], Venkatesh, S.[Svetha],
Expected Hypervolume Improvement with Constraints,
ICPR18(3238-3243)
IEEE DOI 1812
Optimization, Bayes methods, Linear programming, Benchmark testing, Gaussian processes, Evolutionary computation, Frequency modulation BibRef

Bernard, F.[Florian], Thunberg, J., Swoboda, P., Theobalt, C.[Christian],
HiPPI: Higher-Order Projected Power Iterations for Scalable Multi-Matching,
ICCV19(10283-10292)
IEEE DOI 2004
computer graphics, image matching, iterative methods, quadratic programming, Sparse matrices BibRef

Bernard, F.[Florian], Theobalt, C.[Christian], Moeller, M.[Michael],
DS*: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems,
CVPR18(4310-4319)
IEEE DOI 1812
Informatics, Runtime, Relaxation methods, Cost function BibRef

Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.,
Joint Optimization Framework for Learning with Noisy Labels,
CVPR18(5552-5560)
IEEE DOI 1812
Noise measurement, Training, Optimization, Entropy, Neural networks, Degradation BibRef

Mohapatra, P., Rolinek, M., Jawahar, C.V., Kolmogorov, V., Kumar, M.P.,
Efficient Optimization for Rank-Based Loss Functions,
CVPR18(3693-3701)
IEEE DOI 1812
Award, CVPR, HM. Loss measurement, Training, Inference algorithms, Upper bound, Minimization, Complexity theory, Fasteners BibRef

Roy, S.K., Mhammedi, Z., Harandi, M.,
Geometry Aware Constrained Optimization Techniques for Deep Learning,
CVPR18(4460-4469)
IEEE DOI 1812
Manifolds, Optimization, Linear programming, Geometry, Machine learning algorithms, Machine learning, Mathematical model BibRef

Zach, C.[Christopher], Bourmaud, G.[Guillaume],
Descending, Lifting or Smoothing: Secrets of Robust Cost Optimization,
ECCV18(XII: 558-574).
Springer DOI 1810
BibRef

Li, C.[Chong], Shi, C.J.R.[C. J. Richard],
Constrained Optimization Based Low-Rank Approximation of Deep Neural Networks,
ECCV18(X: 746-761).
Springer DOI 1810
BibRef

Rodriguez, P.,
Improved Solution to the L_0 Regularized Optimization Problem via Dictionary-Reduced Initial Guess,
IVMSP18(1-5)
IEEE DOI 1809
Optimization, Iterative methods, Convergence, Acceleration, Dictionaries, Approximation algorithms, Encoding, escape procedure. Nesterov's AGD BibRef

Grosche, S., Seiler, J., Kaup, A.,
Iterative Optimization of Quarter Sampling Masks for Non-Regular Sampling Sensors,
ICIP18(26-30)
IEEE DOI 1809
Image resolution, Image reconstruction, Sensors, Optimization, Energy resolution, Reconstruction algorithms, Image sensors, Image reconstruction BibRef

Purkait, P.[Pulak], Zach, C.[Christopher], Eriksson, A.P.[Anders P.],
Maximum Consensus Parameter Estimation by Reweighted L_1 Methods,
EMMCVPR17(312-327).
Springer DOI 1805
BibRef

Shen, X., Zou, D., Zhang, X.,
A Self-Adaptive Differential Evolution with Dynamic Selecting Mutation Strategy,
ICVISP17(5-10)
IEEE DOI 1712
Signal processing, algorithm improvements, differential evolution algorithm, stagnation solutions BibRef

Zhang, X., Zou, D., Shen, X.,
A Simplified and Efficient Gravitational Search Algorithm for Unconstrained Optimization Problems,
ICVISP17(11-17)
IEEE DOI 1712
Signal processing, benchmark function, gravitational search algorithm, search strategy, simplified, unconstrained problems BibRef

Haubold, C.[Carsten], Uhlmann, V.[Virginie], Unser, M.[Michael], Hamprecht, F.A.[Fred A.],
Diverse M-Best Solutions by Dynamic Programming,
GCPR17(255-267).
Springer DOI 1711
BibRef

Dani, V.[Vivek], Sarswat, A.[Aparna], Swaroop, V.[Vishnu], Domanal, S.[Shridhar], Guddeti, R.M.R.[Ram Mohana Reddy],
Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization,
PReMI17(282-288).
Springer DOI 1711
BibRef

Hong, B.W.[Byung-Woo], Koo, J.K.[Ja-Keoung], Dirks, H.[Hendrik], Burger, M.[Martin],
Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems,
GCPR17(268-280).
Springer DOI 1711
BibRef

Shanmugasundaram, K., Mohamed, A.S.A., Ruhaiyem, N.I.R.,
Hybrid Improved Bacterial Swarm (HIBS) Optimization Algorithm,
IVIC17(71-78).
Springer DOI 1711
BibRef

Yuan, G.Z.[Gan-Zhao], Zheng, W.S.[Wei-Shi], Ghanem, B.[Bernard],
A Matrix Splitting Method for Composite Function Minimization,
CVPR17(5310-5319)
IEEE DOI 1711
Convergence, Gradient methods, Linear systems, Minimization, Sparse matrices, Symmetric matrices BibRef

Ikami, D., Yamasaki, T., Aizawa, K.,
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems,
CVPR17(7206-7214)
IEEE DOI 1711
Clustering algorithms, Convergence, Image restoration, Linear programming, Optimization methods BibRef

Kannan, H., Komodakis, N.[Nikos], Paragios, N.,
Newton-Type Methods for Inference in Higher-Order Markov Random Fields,
CVPR17(7224-7233)
IEEE DOI 1711
Convergence, Markov random fields, Newton method, Optimization, Probability distribution, Smoothing methods BibRef

Speciale, P., Paudel, D.P., Oswald, M.R., Kroeger, T., Van Gool, L.J.[Luc J.], Pollefeys, M.,
Consensus Maximization with Linear Matrix Inequality Constraints,
CVPR17(5048-5056)
IEEE DOI 1711
Linear matrix inequalities, Optimization methods, Orbits, Robustness, Symmetric, matrices BibRef

Lange, J.H.[Jan-Hendrik], Andres, B.[Bjoern], Swoboda, P.[Paul],
Combinatorial Persistency Criteria for Multicut and Max-Cut,
CVPR19(6086-6095).
IEEE DOI 2002
BibRef

Swoboda, P., Kuske, J., Savchynskyy, B.,
A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems,
CVPR17(4950-4960)
IEEE DOI 1711
Computational modeling, Couplings, Inference algorithms, Linear programming, Optimization, Partitioning, algorithms BibRef

Levinkov, E.[Evgeny], Uhrig, J.[Jonas], Tang, S.[Siyu], Omran, M.[Mohamed], Insafutdinov, E.[Eldar], Kirillov, A.[Alexander], Rother, C.[Carsten], Brox, T.[Thomas], Schiele, B.[Bernt], Andres, B.[Bjoern],
Joint Graph Decomposition Node Labeling: Problem, Algorithms, Applications,
CVPR17(1904-1912)
IEEE DOI 1711
Cost function, Labeling, Object tracking, Semantics BibRef

Wang, Q., Gao, J., Li, H.,
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering,
CVPR17(3145-3153)
IEEE DOI 1711
Clustering algorithms, Eigenvalues and eigenfunctions, Geometry, Linear programming, Manifolds, Optimization BibRef

Le, H., Chin, T.J., Suter, D.,
RATSAC - Random Tree Sampling for Maximum Consensus Estimation,
DICTA17(1-8)
IEEE DOI 1804
BibRef
And:
An Exact Penalty Method for Locally Convergent Maximum Consensus,
CVPR17(379-387)
IEEE DOI 1711
Monte Carlo methods, random processes, sampling methods, tree searching, LP-type problems, Search problems. Computational modeling, Estimation, Minimization, Pollution measurement, Robustness BibRef

Rosa, G.H.[Gustavo H.], Afonso, L.C.S.[Luis C. S.], Baldassin, A.[Alexandro], Papa, J.P.[João P.], Yang, X.S.[Xin-She],
Quaternionic Flower Pollination Algorithm,
CAIP17(II: 47-58).
Springer DOI 1708
BibRef

Zhang, Y.[Ying], Wang, H.W.[Hong-Wei], Wang, Y.B.[Yu-Bing], Guo, P.C.[Peng-Cheng],
A novel optimization algorithm for BP neural network based on RS-MEA,
ICIVC17(1073-1078)
IEEE DOI 1708
Convergence, Evolutionary computation, Indexes, Rough sets, BP neural network, attribute reduction, global optimal, mind evolutionary algorithm, rough, set BibRef

Zhu, M.K.[Ming-Kang], Chen, J.L.[Jian-Li],
Computational comparison of GRASP and DCTSP methods for the Traveling Salesman Problem,
ICIVC17(1044-1048)
IEEE DOI 1708
Annealing, GRASP, dynamic convexized method, traveling, salesman, problem BibRef

Dalmau-Cedeño, O.[Oscar], Oviedo, H.[Harry],
A Projection Method for Optimization Problems on the Stiefel Manifold,
MCPR17(84-93).
Springer DOI 1706
BibRef

Osmanlioglu, Y.[Yusuf], Ontañón, S.[Santiago], Hershberg, U.[Uri], Shokoufandeh, A.[Ali],
Efficient approximation of labeling problems with applications to immune repertoire analysis,
ICPR16(2410-2415)
IEEE DOI 1705
Approximation algorithms, Cost function, Distortion, Labeling, Linear programming, Measurement BibRef

Weissenberg, J., Riemenschneider, H., Dragon, R., Van Gool, L.J.,
Dilemma First Search for effortless optimization of NP-hard problems,
ICPR16(4154-4159)
IEEE DOI 1705
Algorithm design and analysis, Decision trees, Genetic algorithms, Inference algorithms, Optimization, Search problems, Vegetation BibRef

Awate, S.P., Dhar, M., Kulkarni, N.,
Robust kernel principal nested spheres,
ICPR16(402-407)
IEEE DOI 1705
Algorithm design and analysis, Data models, Kernel, Manifolds, Optimization, Principal component analysis, Robustness BibRef

Samejima, M.[Masaki], Matsushita, Y.[Yasuyuki],
Fast General Norm Approximation via Iteratively Reweighted Least Squares,
eHeritage16(II: 207-221).
Springer DOI 1704
BibRef

Shcherbatyi, I.[Iaroslav], Andres, B.[Bjoern],
Convexification of Learning from Constraints,
GCPR16(79-90).
Springer DOI 1611
BibRef

Matsuzawa, T.[Tomoki], Relator, R.[Raissa], Sese, J.[Jun], Kato, T.[Tsuyoshi],
Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors,
ECCV16(VI: 786-799).
Springer DOI 1611
BibRef

Hafner, D.[David], Ochs, P.[Peter], Weickert, J.[Joachim], Reißel, M.[Martin], Grewenig, S.[Sven],
FSI Schemes: Fast Semi-Iterative Solvers for PDEs and Optimisation Methods,
GCPR16(91-102).
Springer DOI 1611
Award, GCPR. BibRef

Swoboda, P., Andres, B.,
A Message Passing Algorithm for the Minimum Cost Multicut Problem,
CVPR17(4990-4999)
IEEE DOI 1711
Algorithm design and analysis, Image segmentation, Message passing, Optimization, Software algorithms, Wheels BibRef

Beier, T.[Thorsten], Andres, B.[Björn], Köthe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem,
ECCV16(II: 715-730).
Springer DOI 1611
BibRef

Kovnatsky, A.[Artiom], Glashoff, K.[Klaus], Bronstein, M.M.[Michael M.],
MADMM: A Generic Algorithm for Non-smooth Optimization on Manifolds,
ECCV16(V: 680-696).
Springer DOI 1611
BibRef

Talbot, H.[Hugues],
Discrete Calculus, Optimisation and Inverse Problems in Imaging,
DGCI16(18-27).
WWW Link. 1606
BibRef

Diamond, S., Boyd, S.,
Convex Optimization with Abstract Linear Operators,
ICCV15(675-683)
IEEE DOI 1602
Convex functions BibRef

Henry, M.[Morgane], Maitre, E.[Emmanuel], Perrier, V.[Valerie],
Optimal transport using Helmholtz-Hodge decomposition and first-order primal-dual algorithms,
ICIP15(4748-4752)
IEEE DOI 1512
Convex optimization BibRef

Zielosko, B.[Beata],
Global Optimization of Exact Association Rules Relative to Coverage,
PReMI15(428-437).
Springer DOI 1511
BibRef

Cheng, Y., Lopez, J.A., Camps, O., Sznaier, M.,
A convex optimization approach to robust fundamental matrix estimation,
CVPR15(2170-2178)
IEEE DOI 1510
BibRef

Dong, G.Z.[Guo-Zhi], Patrone, A.R.[Aniello Raffaele], Scherzer, O.[Otmar], Öktem, O.[Ozan],
Infinite Dimensional Optimization Models and PDEs for Dejittering,
SSVM15(678-689).
Springer DOI 1506
BibRef

Serra, J.[Jean], Kiran, B.R.[Bangalore Ravi],
Digitization of Partitions and Tessellations,
DGCI16(323-334).
WWW Link. 1606
BibRef
And:
Constrained Optimization on Hierarchies and Braids of Partitions,
ISMM15(229-240).
Springer DOI 1506
BibRef

Pavlovskaia, M.[Maira], Tu, K.W.[Ke-Wei], Zhu, S.C.[Song-Chun],
Mapping the Energy Landscape of Non-convex Optimization Problems,
EMMCVPR15(421-435).
Springer DOI 1504
BibRef

Liu, K.[Kangwei], Zhang, J.[Junge], Huang, K.Q.[Kai-Qi],
Improved Optimization Based on Graph Cuts for Discrete Energy Minimization,
ICPR14(2424-2429)
IEEE DOI 1412
Algorithm design and analysis BibRef

Eriksson, A.P.[Anders P.], Isaksson, M.[Mats],
Pseudoconvex Proximal Splitting for L-infinity Problems in Multiview Geometry,
CVPR14(4066-4073)
IEEE DOI 1409
Optimization. BibRef

Dolz, J., Ben Ayed, I.[Ismail], Desrosiers, C.,
DOPE: Distributed Optimization for Pairwise Energies,
CVPR17(4095-4104)
IEEE DOI 1711
Computational modeling, Convergence, Image segmentation, Optimization, Partitioning algorithms, BibRef

Tang, M.[Meng], Ben Ayed, I.[Ismail], Boykov, Y.Y.[Yuri Y.],
Pseudo-bound Optimization for Binary Energies,
ECCV14(V: 691-707).
Springer DOI 1408
BibRef

Xu, J.[Jia], Ithapu, V.K.[Vamsi K.], Mukherjee, L.[Lopamudra], Rehg, J.M.[James M.], Singh, V.[Vikas],
GOSUS: Grassmannian Online Subspace Updates with Structured-Sparsity,
ICCV13(3376-3383)
IEEE DOI 1403
Manifold optimization BibRef

Yang, S.[Shulin], Wang, J.[Jue], Shapiro, L.G.[Linda G.],
Supervised Semantic Gradient Extraction Using Linear-Time Optimization,
CVPR13(2826-2833)
IEEE DOI 1309
BibRef

Windheuser, T.[Thomas], Ishikawa, H.[Hiroshi], Cremers, D.[Daniel],
Generalized Roof Duality for Multi-Label Optimization: Optimal Lower Bounds and Persistency,
ECCV12(VI: 400-413).
Springer DOI 1210
BibRef

Mu, Y.D.[Ya-Dong], Wright, J.[John], Chang, S.F.[Shih-Fu],
Accelerated Large Scale Optimization by Concomitant Hashing,
ECCV12(I: 414-427).
Springer DOI 1210
BibRef

Kuang, Y.B.[Yu-Bin], Åström, K.[Kalle],
Numerically Stable Optimization of Polynomial Solvers for Minimal Problems,
ECCV12(III: 100-113).
Springer DOI 1210
BibRef

Brédif, M., Tournaire, O.,
Librjmcmc: An Open-source Generic C++ Library For Stochastic Optimization,
ISPRS12(XXXIX-B3:259-264).
DOI Link 1209
Code, Optimization. BibRef

Naroditsky, O.[Oleg], Daniilidis, K.[Kostas],
Optimizing polynomial solvers for minimal geometry problems,
ICCV11(975-982).
IEEE DOI 1201
BibRef

Akhtar, S.[Sohail], Abdel-Rahman, E.M.[Eihab M.], Ahmad, A.R.[Abdul-Rahim],
A New Fitness Based Adaptive Parameter Particle Swarm Optimizer,
CRV14(336-343)
IEEE DOI 1406
Acceleration BibRef

Akhtar, S.[Sohail], Ahmad, A.R.[Abdul-Rahim], Abdel-Rahman, E.M.[Eihab M.], Naqvi, T.,
A PSO Accelerated Immune Particle Filter for Dynamic State Estimation,
CRV11(72-79).
IEEE DOI 1105
PSO: Particle Swarm Optimization. Avoid local minima. BibRef

Chen, Y.S.[Yi-Song], Sun, J.W.[Jie-Wei], Wang, G.P.[Guo-Ping],
Minimizing Geometric Distance by Iterative Linear Optimization,
ICPR10(1-4).
IEEE DOI 1008
BibRef

Kohli, P.[Pushmeet], Lempitsky, V.[Victor], Rother, C.[Carsten],
Uncertainty Driven Multi-scale Optimization,
DAGM10(242-251).
Springer DOI 1009
multi-scale energy minimization BibRef

Olsson, C.[Carl], Kahl, F.[Fredrik], Hartley, R.I.[Richard I.],
Projective least-squares: Global solutions with local optimization,
CVPR09(1216-1223).
IEEE DOI 0906
BibRef

Olsson, C.[Carl], Byröd, M.[Martin], Kahl, F.[Fredrik],
Globally Optimal Least Squares Solutions for Quasiconvex 1D Vision Problems,
SCIA09(686-695).
Springer DOI 0906
BibRef

Kopylov, A.[Andrey],
Tree-serial dynamic programming for image processing,
ICPR08(1-4).
IEEE DOI 0812
Optimization technique. BibRef

Yang, F.Q.[Feng-Qin], Zhang, C.H.[Chang-Hai], Sun, T.[Tieli],
Comparison of Particle Swarm Optimization and Genetic Algorithm for HMM training,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Unger, M.[Markus], Pock, T.[Thomas], Bischof, H.[Horst],
Global Relabeling for Continuous Optimization in Binary Image Segmentation,
EMMCVPR11(104-117).
Springer DOI 1107

See also TVSeg: Interactive Total Variation Based Image Segmentation. BibRef

Huang, X.F.[Xiao-Fei],
Cooperative Optimization for Energy Minimization in Computer Vision: A Case Study of Stereo Matching,
DAGM04(302-309).
Springer DOI 0505
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Regression .