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Adaptive Sampling Stochastic Multigradient Algorithm for Stochastic Multiobjective Optimization
In this paper, we propose an adaptive sampling stochastic multigradient algorithm for solving stochastic multiobjective optimization problems....
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Stochastic Optimization Methods
This chapter introduces some methods aimed at solving difficult optimization problems arising in many engineering fields. By difficult optimization...
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Block Mirror Stochastic Gradient Method For Stochastic Optimization
In this paper, a block mirror stochastic gradient method is developed to solve stochastic optimization problems involving convex and nonconvex cases,...
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Optimal methods for convex nested stochastic composite optimization
Recently, convex nested stochastic composite optimization (NSCO) has received considerable interest for its applications in reinforcement learning...
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Distributed Heterogeneous Multi-Agent Optimization with Stochastic Sub-Gradient
This paper studies the optimization problem of heterogeneous networks under a time-varying topology. Each agent only accesses to one local objective...
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A Bayesian approach to data-driven multi-stage stochastic optimization
Aimed at sufficiently utilizing available data and prior distribution information, we introduce a data-driven Bayesian-type approach to solve...
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Stochastic subgradient algorithm for nonsmooth nonconvex optimization
In this paper, we study on a stochastic subgradient algorithm for the finite-sum optimization problems where the functions are not necessarily convex...
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Optimization and Identification of Stochastic Systems*
The author overviews some well-known scientific results from the theory of stochastic optimization and theory of risk, obtained by the Academician of...
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Algorithms with Gradient Clip** for Stochastic Optimization with Heavy-Tailed Noise
AbstractThis article provides a survey of the results of several research studies [12–14, 26], in which open questions related to the...
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Sample complexity analysis for adaptive optimization algorithms with stochastic oracles
Several classical adaptive optimization algorithms, such as line search and trust-region methods, have been recently extended to stochastic settings...
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Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization
We consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from...
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Distributed stochastic compositional optimization problems over directed networks
We study the distributed stochastic compositional optimization problems over directed communication networks in which agents privately own a...
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A Stochastic Nesterov’s Smoothing Accelerated Method for General Nonsmooth Constrained Stochastic Composite Convex Optimization
We propose a novel stochastic Nesterov’s smoothing accelerated method for general nonsmooth, constrained, stochastic composite convex optimization,...
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Vaidya’s Method for Convex Stochastic Optimization Problems in Small Dimension
AbstractThe paper deals with a general problem of convex stochastic optimization in a space of small dimension (for example, 100 variables). It is...
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Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization
A worst-case complexity bound is proved for a sequential quadratic optimization (commonly known as SQP) algorithm that has been designed for solving...
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A single cut proximal bundle method for stochastic convex composite optimization
This paper considers optimization problems where the objective is the sum of a function given by an expectation and a closed convex function, and...
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Sparse conic reformulation of structured QCQPs based on copositive optimization with applications in stochastic optimization
Recently, Bomze et al. introduced a sparse conic relaxation of the scenario problem of a two stage stochastic version of the standard quadratic...
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Convergence Rates of the Stochastic Alternating Algorithm for Bi-Objective Optimization
Stochastic alternating algorithms for bi-objective optimization are considered when optimizing two conflicting functions for which optimization steps...
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A randomized operator splitting scheme inspired by stochastic optimization methods
In this paper, we combine the operator splitting methodology for abstract evolution equations with that of stochastic methods for large-scale...
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Stochastic dual dynamic programming for multistage stochastic mixed-integer nonlinear optimization
In this paper, we study multistage stochastic mixed-integer nonlinear programs (MS-MINLP). This general class of problems encompasses, as important...
