Results 31 to 40 of about 22,374 (199)
Mathematics, 2019 The aim of this article is to study new types of generalized nonsmooth exponential type vector variational-like inequality problems involving Mordukhovich limiting subdifferential operator.
Syed Shakaib Irfan +4 more
doaj +1 more source
Abstract and Applied Analysis, 2013 The nonlinear conjugate gradient method is of particular importance for solving unconstrained optimization. Finitely many maximum functions is a kind of very useful nonsmooth equations, which is very useful in the study of complementarity problems ...
Yuan-yuan Chen, Shou-qiang Du
doaj +1 more source
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees
, 2020 Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training.
Alistarh, Dan +3 more
core +2 more sources
Distributed stochastic nonsmooth nonconvex optimization
Operations Research Letters, 2022 Distributed consensus optimization has received considerable attention in recent years; several distributed consensus-based algorithms have been proposed for (nonsmooth) convex and (smooth) nonconvex objective functions. However, the behavior of these distributed algorithms on {\it nonconvex, nonsmooth and stochastic} objective functions is not ...
openaire +2 more sources
Fast Nonsmooth Regularized Risk Minimization with Continuation
, 2016 In regularized risk minimization, the associated optimization problem becomes particularly difficult when both the loss and regularizer are nonsmooth.
Kwok, James T. +2 more
core +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization
, 2017 Stochastic composition optimization draws much attention recently and has been successful in many emerging applications of machine learning, statistical analysis, and reinforcement learning.
Gu, Bin +3 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Journal of MathematicsThis paper defines a strong convertible nonconvex (SCN) function for solving the unconstrained optimization problems with the nonconvex or nonsmooth (nondifferentiable) function.
Min Jiang +3 more
doaj +1 more source
Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization [PDF]
, 2013 In this paper, we present a new stochastic algorithm, namely the stochastic block mirror descent (SBMD) method for solving large-scale nonsmooth and stochastic optimization problems. The basic idea of this algorithm is to incorporate the block-coordinate
Dang, Cong D., Lan, Guanghui
core