Results 71 to 80 of about 1,615 (176)
A Hybrid Approach of Bundle and Benders Applied Large Mixed Linear Integer Problem
Journal of Applied Mathematics, 2013 Consider a large mixed integer linear problem where structure of the constraint matrix is sparse, with independent blocks, and coupling constraints and variables.
Placido Rogerio Pinheiro +1 more
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, 1992 In solving a mathematical program, the exact evaluation of the objective function and its subgradients can be computationally burdensome. For example, in a stochastic program, the objective function is typically defined through a multi-dimensional ...
Au, Kelly Thurston.
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Deflected Conditional Approximate Subgradient Methods
, 2007 Subgradient methods for constrained nondifferentiable problems benefit from projection of the search direction onto the (normal cone of) the feasible set prior to computing the steplength, that is, from the use of conditional subgradient techniques.
Frangioni, Antonio, d'Antonio, Giacomo
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The Jacobian Consistency of a One-Parametric Class of Smoothing Functions for SOCCP
Abstract and Applied Analysis, 2013 Second-order cone (SOC) complementarity functions and their smoothing functions have been much studied in the solution of second-order cone complementarity problems (SOCCP).
Xiaoni Chi, Zhongping Wan, Zijun Hao
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Subgradient methods in network resource allocation: Rate analysis
, 2008 We consider dual subgradient methods for solving (nonsmooth) convex constrained optimization problems. Our focus is on generating approximate primal solutions with performance guarantees and providing convergence rate analysis.
Asuman Ozdaglar, Angelia Nedić
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Bulletin of Computational Applied Mathematics, 2016 The Modified Spectral Projected Subgradient (MSPS) was proposed to solve Langrangen Dual Problems, and its convergence was shown when the momentum term was zero. The MSPS uses a momentum term in order to speed up its convergence.
Milagros Loreto +3 more
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On convergence properties of a subgradient method
, 2020 In this article, we consider convergence properties of the normalized subgradient method which employs the stepsize rule based on a priori knowledge of the optimal value of the cost function.
Konnov I.
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Inexact subgradient methods for semialgebraic functions
Motivated by the widespread use of approximate derivatives in machine learning and optimization, we study inexact subgradient methods with non-vanishing additive errors and step sizes. In the nonconvex semialgebraic setting, under boundedness assumptions,
Bolte, Jérôme +3 more
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MathematicsIn this work, we present a Krasnosel’skiǐ–Mann-type subgradient extragradient algorithm to solve variational inequalities and hierarchical fixed-point problems for nonexpansive and quasi-nonexpansive mappings in Hilbert spaces.
Monairah Alansari +2 more
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Simple Synchronous and Asynchronous Algorithms for Distributed Minimax Optimization
SICE Journal of Control, Measurement, and System Integration, 2017 Synchronous and asynchronous algorithms are presented for distributed minimax optimization. The objective here is to realize the minimization of the maximum of component functions over the standard multi-agent network, where each node of the network ...
Kenta Hanada +3 more
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