Search Results - "prox-linear algorithm"

Hybrid SGD algorithms to solve stochastic composite optimization problems with application in sparse portfolio selection problems by Yang, Zhen-Ping, Zhao, Yong

“…In this paper, we study stochastic composite problems where the objective can be the composition of an outer single-valued function and an inner vector-valued…”
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Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization by Zhang, Junyu, Xiao, Lin

“… We propose a class of stochastic variance-reduced prox-linear algorithms for solving such problems and bound their sample complexities for finding an ϵ…”
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REAL-TIME POWER SYSTEM STATE ESTIMATION VIA DEEP UNROLLED NEURAL NETWORKS by Zhang, Liang, Wang, Gang, Giannakis, Georgios B.

“…Contemporary smart power grids are being challenged by rapid voltage fluctuations, due to large-scale deployment of electric vehicles, demand response…”
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Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence by Charisopoulos, Vasileios, Chen, Yudong, Davis, Damek, Díaz, Mateo, Ding, Lijun, Drusvyatskiy, Dmitriy

“…The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science. Smooth formulations of the problem…”
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Robust and Scalable Power System State Estimation via Composite Optimization by Wang, Gang, Giannakis, Georgios B., Chen, Jie

“…In today's cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area…”
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Stochastic Variance-Reduced Prox-Linear Algorithms for Nonconvex Composite Optimization by Zhang, Junyu, Lin, Xiao

“… We propose a class of stochastic variance-reduced prox-linear algorithms for solving such problems and bound their sample complexities for finding an \(\epsilon…”
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A Barzilai–Borwein-Like Iterative Half Thresholding Algorithm for the L1/2 Regularized Problem by Wu, Lei, Sun, Zhe, Li, Dong-Hui

“…In this paper, we propose a Barzilai–Borwein-like iterative half thresholding algorithm for the L 1 / 2 regularized problem. The algorithm is closely related…”
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Large-Scale Distributed Sparse Class-Imbalance Learning by Maurya, Chandresh Kumar, Toshniwal, Durga

“…Class-imbalance learning is a classic problem in data mining and machine learning community. In class-imbalance learning, the idea is to learn the model so…”
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The proximal point method revisited by Drusvyatskiy, Dmitriy

“… I focus on three recent examples: a proximally guided subgradient method for weakly convex stochastic approximation, the prox-linear algorithm for minimizing compositions of convex functions and smooth maps, and Catalyst generic…”
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Stochastic Methods for Composite and Weakly Convex Optimization Problems by Duchi, John, Ruan, Feng

“… We develop a family of stochastic methods---including a stochastic prox-linear algorithm and a stochastic (generalized…”
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Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval by Duchi, John C, Ruan, Feng

“… We show that the prox-linear algorithm we develop can solve phase retrieval problems---even with adversarially faulty measurements---with high probability as soon as the number of measurements \(m…”
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Stochastic algorithms with geometric step decay converge linearly on sharp functions by Davis, Damek, Drusvyatskiy, Dmitriy, Charisopoulos, Vasileios

“…Stochastic (sub)gradient methods require step size schedule tuning to perform well in practice. Classical tuning strategies decay the step size polynomially…”
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Parallel and distributed sparse optimization by Peng, Zhimin, Yan, Ming, Yin, Wotao

“… (i) distributed implementations of prox-linear algorithms and (ii) GRock, a parallel greedy block coordinate descent method…”
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Modified Gauss-Newton Algorithms under Noise by Pillutla, Krishna, Roulet, Vincent, Kakade, Sham M., Harchaoui, Zaid

“… Their nonsmooth counterparts, modified Gauss-Newton or prox-linear algorithms, can lead to contrasting outcomes when compared to gradient descent in large-scale statistical settings…”
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A Barzilai-Borwein-Like Iterative Half Thresholding Algorithm for the \(L_{1/2}\) Regularized Problem by Wu, Lei, Sun, Zhe, Li, Dong-Hui

“…In this paper, we propose a Barzilai-Borwein-like iterative half thresholding algorithm for the \(L_{1/2}\) regularized problem. The algorithm is closely…”
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Modified Gauss-Newton Algorithms under Noise by Pillutla, Krishna, Roulet, Vincent, Kakade, Sham, Harchaoui, Zaid

“… Their nonsmooth counterparts, modified Gauss-Newton or prox-linear algorithms, can lead to contrasting outcomes when compared to gradient descent in large-scale statistical settings…”
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Stochastic algorithms with geometric step decay converge linearly on sharp functions by Davis, Damek, Drusvyatskiy, Dmitriy, Charisopoulos, Vasileios

“…Stochastic (sub)gradient methods require step size schedule tuning to perform well in practice. Classical tuning strategies decay the step size polynomially…”
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Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers by Chen, Yifan, Sun, Yuejiao, Yin, Wotao

“…Many optimization algorithms converge to stationary points. When the underlying problem is nonconvex, they may get trapped at local minimizers and occasionally…”
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