Conditional gradient type methods for composite nonlinear and stochastic optimization
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Publication:1717236
DOI10.1007/s10107-017-1225-5zbMath1410.90150arXiv1602.00961OpenAlexW2264518955MaRDI QIDQ1717236
Publication date: 7 February 2019
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1602.00961
nonconvex optimizationiteration complexitystrongly convex optimizationweakly smooth functionsconditional gradient type methodsunified methods
Analysis of algorithms and problem complexity (68Q25) Convex programming (90C25) Nonconvex programming, global optimization (90C26) Stochastic programming (90C15) Stochastic approximation (62L20)
Related Items (15)
On the Frank–Wolfe algorithm for non-compact constrained optimization problems ⋮ A stochastic primal-dual method for a class of nonconvex constrained optimization ⋮ Improved complexities for stochastic conditional gradient methods under interpolation-like conditions ⋮ Analysis of the Frank-Wolfe method for convex composite optimization involving a logarithmically-homogeneous barrier ⋮ Universal Conditional Gradient Sliding for Convex Optimization ⋮ Affine Invariant Convergence Rates of the Conditional Gradient Method ⋮ A unified analysis of stochastic gradient‐free Frank–Wolfe methods ⋮ A generalized Frank-Wolfe method with ``dual averaging for strongly convex composite optimization ⋮ Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization ⋮ Zeroth-order nonconvex stochastic optimization: handling constraints, high dimensionality, and saddle points ⋮ Inexact proximal stochastic second-order methods for nonconvex composite optimization ⋮ Conditional gradient method for multiobjective optimization ⋮ A unified convergence analysis of stochastic Bregman proximal gradient and extragradient methods ⋮ Accelerated first-order methods for large-scale convex optimization: nearly optimal complexity under strong convexity ⋮ Derivative-free optimization methods
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