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Difference Between Cyclic and Distributed Approach in Stochastic Optimization for Multi-agent System

Many stochastic optimization problems in multi-agent systems can be decomposed into smaller subproblems or reduced decision subspaces. The cyclic and distributed approaches are two widely used strategies for solving such problems. In this manuscript,

Externí odkaz: http://arxiv.org/abs/2409.05155

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Modified Line Search Sequential Quadratic Methods for Equality-Constrained Optimization with Unified Global and Local Convergence Guarantees

Autor: Berahas, Albert S., Bollapragada, Raghu, Shi, Jiahao

In this paper, we propose a method that has foundations in the line search sequential quadratic programming paradigm for solving general nonlinear equality constrained optimization problems. The method employs a carefully designed modified line searc

Externí odkaz: http://arxiv.org/abs/2406.11144

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A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms

Autor: Larson, Jeffrey, Menickelly, Matt, Shi, Jiahao

A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on

Externí odkaz: http://arxiv.org/abs/2401.10121

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Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction

Autor: Berahas, Albert S., Shi, Jiahao, Yi, Zihong, Zhou, Baoyu

In this paper, we propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method based on the sequential quadratic programming paradigm that employs va

Externí odkaz: http://arxiv.org/abs/2204.04161

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