An investigation of stochastic trust-region based algorithms for finite-sum minimization

DOI10.1080/10556788.2024.2346834MaRDI QIDQ6644989

S. Bellavia, Benedetta Morini, Simone Rebegoldi

Publication date: 28 November 2024

Published in: Optimization Methods \& Software (Search for Journal in Brave)

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56) Stochastic programming (90C15)

Cites Work

• Sample size selection in optimization methods for machine learning
• Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
• Stochastic optimization using a trust-region method and random models
• A stochastic first-order trust-region method with inexact restoration for finite-sum minimization
• Inexact restoration with subsampled trust-region methods for finite-sum minimization
• Nonlinear programming
• Convergence of Trust-Region Methods Based on Probabilistic Models
• Trust Region Methods
• Adaptive Sampling Strategies for Stochastic Optimization
• Optimization Methods for Large-Scale Machine Learning
• A fully stochastic second-order trust region method
• A Stochastic Approximation Method
• Trust-region algorithms: probabilistic complexity and intrinsic noise with applications to subsampling techniques

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