Efficiently Converging Minimization Methods Based on the Reduced Gradient

DOI10.1137/0314004zbMath0342.90043OpenAlexW2022780604MaRDI QIDQ4110806

Daniel Gabay, David G. Luenberger

Publication date: 1976

Published in: SIAM Journal on Control and Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/0314004

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Rate of convergence, degree of approximation (41A25) Numerical methods in optimal control (49Mxx)

Related Items (11)

Partial spectral projected gradient method with active-set strategy for linearly constrained optimization ⋮ Two-phase generalized reduced gradient method for constrained global optimization ⋮ On the convergence of a generalized Reduced gradient algorithm for nonlinear programming problems ⋮ Minimizing a differentiable function over a differential manifold ⋮ Stochastic perturbation of reduced gradient \& GRG methods for nonconvex programming problems ⋮ Minimum problems on differentiable manifolds ⋮ On the local and global convergence of a reduced Quasi-Newton method¹ ⋮ Variants of the reduced Newton method for nonlinear equality constrained optimization problems ⋮ A computational method for minimization with nonlinear constraints ⋮ Une methode de gradient conjugue sur des varietes application a certains problemes de valeurs propres non lineaires ⋮ An appreciation of Professor David G. Luenberger

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