Minimization algorithms based on supervisor and searcher cooperation

DOI10.1023/A:1011986402461zbMath1032.90020MaRDI QIDQ5956435

Yu-Hong Dai, Wen-bin Liu

Publication date: 2001

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

zbMATH Keywords

stochastic approximationsgradient algorithmsnoisy optimizationrobust algorithms

Mathematics Subject Classification ID

Stochastic programming (90C15) Methods of reduced gradient type (90C52)

Related Items (13)

Stable equilibrium configuration of two bar truss by an efficient nonmonotone global Barzilai-Borwein gradient method in a fuzzy environment ⋮ Novel algorithms for noisy minimization problems with applications to neural networks training ⋮ LMBOPT: a limited memory method for bound-constrained optimization ⋮ Adaptive finite element methods for the identification of elastic constants ⋮ The Uzawa-MBB type algorithm for nonsymmetric saddle point problems ⋮ Batch gradient method with smoothing \(L_{1/2}\) regularization for training of feedforward neural networks ⋮ Prediction-correction method with BB step sizes ⋮ Fast methods for computing centroidal Laguerre tessellations for prescribed volume fractions with applications to microstructure generation of polycrystalline materials ⋮ A new spectral method for \(l_1\)-regularized minimization ⋮ On the asymptotic behaviour of some new gradient methods ⋮ Alternate step gradient method* ⋮ A descent algorithm without line search for unconstrained optimization ⋮ Inertial projection and contraction algorithms with larger step sizes for solving quasimonotone variational inequalities

Uses Software

• DFO
• minpack

Cites Work

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• A stochastic quasigradient algorithm with variable metric
• Stochastic approximation methods for constrained and unconstrained systems
• Optimization algorithm with probabilistic estimation
• Recent progress in unconstrained nonlinear optimization without derivatives
• Optimization via simulation: A review
• A method of trust region type for minimizing noisy functions
• R-linear convergence of the Barzilai and Borwein gradient method
• The Barzilai and Borwein Gradient Method for the Large Scale Unconstrained Minimization Problem
• Two-Point Step Size Gradient Methods
• Testing Unconstrained Optimization Software
• Nelder-Mead Simplex Modifications for Simulation Optimization
• A Nonmonotone Line Search Technique for Newton’s Method
• On the Barzilai and Borwein choice of steplength for the gradient method
• Stochastic Estimation of the Maximum of a Regression Function
• A Stochastic Approximation Method

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