Výsledky vyhľadávania - "Prox-linear algorithm"

Solving (most) of a set of quadratic equalities: composite optimization for robust phase retrieval.

Autori: Duchi, John C Ruan, Feng

Zdroj: Information & Inference: A Journal of the IMA; Sep2019, Vol. 8 Issue 3, p471-529, 59p

Predmety: SMOOTHNESS of functions, ROBUST optimization, CONVEX functions, MATHEMATICAL equivalence, ALGORITHMS

Statistically optimal firstorder algorithms: a proof via orthogonalization.

Autori: Montanari, Andrea Wu, Yuchen

Zdroj: Information & Inference: A Journal of the IMA; Dec2024, Vol. 13 Issue 4, p1-24, 24p

Predmety: LOW-rank matrices, MATRIX multiplications, RANDOM matrices, ASYMPTOTIC analysis, ALGORITHMS

An inexactly accelerated algorithm for nonnegative tensor CP decomposition with the column unit constraints.

Autori: Wang, Zihao Bai, Minru

Zdroj: Computational Optimization & Applications; Jul2024, Vol. 88 Issue 3, p923-962, 40p

Predmety: ALGORITHMS, CHEMICAL systems, CHEMOMETRICS, EXTRAPOLATION

Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval

Autori: Duchi, John C. Ruan, Feng

Témy: Mathematics - Statistics Theory, Computer Science - Information Theory, Mathematics - Optimization and Control, text

URL: http://arxiv.org/abs/1705.02356

STOCHASTIC METHODS FOR COMPOSITE AND WEAKLY CONVEX OPTIMIZATION PROBLEMS.

Autori: DUCHI, JOHN C. FENG RUAN

Zdroj: SIAM Journal on Optimization. 2018, Vol. 28 Issue 4, p3229-3259. 31p.

Predmety: *SMOOTHNESS of functions, *CONVEX functions, *FUNCTIONALS, *DIFFERENTIAL inclusions, *CONVEX programming

Optimal inexactness schedules for tunable oracle-based methods.

Autori: Van Dessel, Guillaume Glineur, François

Zdroj: Optimization Methods & Software; Jun2024, Vol. 39 Issue 3, p664-698, 35p

Predmety: MATHEMATICAL optimization, PRICES, ALGORITHMS, SCHEDULING, MOTIVATION (Psychology)

Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-Order Convergence.

Autori: Luo, Yuetian Huang, Wen Li, Xudong a ďalší

Zdroj: Operations Research; Jan/Feb2024, Vol. 72 Issue 1, p237-256, 20p

Predmety: LEAST squares, GAUSS-Newton method, LOW-rank matrices, MACHINE learning, ALGORITHMS

Geografický termín: CHINA

Korporácia: NATIONAL Science Foundation (U.S.)

Large-Scale Distributed Sparse Class-Imbalance Learning.

Autori: Maurya, Chandresh Kumar Toshniwal, Durga

Zdroj: Information Sciences. Aug2018, Vol. 456, p1-12. 12p.

Predmety: *MACHINE learning, *DATA mining, *DISTRIBUTION (Probability theory), *GENETIC algorithms, *ERROR analysis in mathematics

Stochastic Composition Optimization of Functions Without Lipschitz Continuous Gradient.

Autori: Liu, Yin Davanloo Tajbakhsh, Sam

Zdroj: Journal of Optimization Theory & Applications; Jul2023, Vol. 198 Issue 1, p239-289, 51p

Predmety: OPTIMIZATION algorithms, SMOOTHNESS of functions

Linearized Proximal Algorithms with Adaptive Stepsizes for Convex Composite Optimization with Applications.

Autori: Hu, Yaohua Li, Chong Wang, Jinhua a ďalší

Zdroj: Applied Mathematics & Optimization; Jun2023, Vol. 87 Issue 3, p1-35, 35p

Predmety: WIRELESS sensor networks, SENSOR placement, CONVEX functions

Transportation Origin-Destination Demand Estimation with Quasi-Sparsity.

Autori: Wang, Jingxing Lu, Shu Liu, Hongsheng a ďalší

Zdroj: Transportation Science; Mar/Apr2023, Vol. 57 Issue 2, p289-312, 24p

Predmety: COMPRESSED sensing

Korporácia: NATIONAL Science Foundation (U.S.)

Stochastic Regularized Newton Methods for Nonlinear Equations.

Autori: Wang, Jiani Wang, Xiao Zhang, Liwei

Zdroj: Journal of Scientific Computing; Feb2023, Vol. 94 Issue 3, p1-21, 21p

IRS Empowered UAV Wireless Communication With Resource Allocation, Reflecting Design and Trajectory Optimization.

Autori: Zhang, Xiaoqi Zhang, Haijun Du, Wenbo a ďalší

Zdroj: IEEE Transactions on Wireless Communications; Oct2022, Vol. 21 Issue 10, p7867-7880, 14p

Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization.

Autori: Zhang, Junyu Xiao, Lin

Zdroj: Mathematical Programming. Sep2022, Vol. 195 Issue 1/2, p649-691. 43p.

Predmety: *NONSMOOTH optimization, *VECTOR data, *CONVEX functions, *JACOBIAN matrices, *ALGORITHMS, *PROBLEM solving

Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence.

Autori: Charisopoulos, Vasileios Chen, Yudong Davis, Damek a ďalší

Zdroj: Foundations of Computational Mathematics; Dec2021, Vol. 21 Issue 6, p1505-1593, 89p

Predmety: NONSMOOTH optimization, RESTRICTED isometry property, LOW-rank matrices, SUBGRADIENT methods, LENGTH measurement, MATHEMATICAL optimization

Composite optimization for robust rank one bilinear sensing.

Autori: Charisopoulos, Vasileios Davis, Damek Díaz, Mateo a ďalší

Zdroj: Information & Inference: A Journal of the IMA; Jun2021, Vol. 10 Issue 2, p333-396, 64p

Predmety: BILINEAR forms, SUBGRADIENT methods, SEARCH algorithms, ROBUST optimization, LIPSCHITZ continuity

Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria.

Autori: Drusvyatskiy, D. Ioffe, A. D. Lewis, A. S.

Zdroj: Mathematical Programming; 2021, Vol. 185 Issue 1/2, p357-383, 27p

Predmety: NONSMOOTH optimization, GAUSS-Newton method, SMOOTHNESS of functions, CONVEX functions, MATHEMATICAL optimization

A Proximal Atomic Coordination Algorithm for Distributed Optimization.

Autori: Romvary, Jordan J. Ferro, Giulio Haider, Rabab a ďalší

Zdroj: IEEE Transactions on Automatic Control; Feb2022, Vol. 67 Issue 2, p646-661, 16p

Predmety: DISTRIBUTED algorithms, ELECTRIC power distribution grids, MATHEMATICAL optimization, PICTURE archiving & communication systems, ELECTRICAL load

The nonsmooth landscape of phase retrieval.

Autori: Davis, Damek Drusvyatskiy, Dmitriy Paquette, Courtney

Zdroj: IMA Journal of Numerical Analysis; Oct2020, Vol. 40 Issue 4, p2652-2695, 44p

Predmety: SUBGRADIENT methods, ALGORITHMS

Efficiency of minimizing compositions of convex functions and smooth maps.

Autori: Drusvyatskiy, D. Paquette, C.

Zdroj: Mathematical Programming; Nov2019, Vol. 178 Issue 1/2, p503-558, 56p

Predmety: SMOOTHNESS of functions, ALGORITHMS