Tensor Methods for Large, Sparse Unconstrained Optimization

DOI10.1137/S1052623494267723zbMath0893.65042MaRDI QIDQ4377571

Ali Bouaricha

Publication date: 10 February 1998

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

zbMATH Keywords

unconstrained optimizationlarge-scale optimizationnumerical examplesNewton methodtrust region methodsingular problemstensor methodssparse problems

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30)

Related Items (10)

A new subspace minimization conjugate gradient method based on tensor model for unconstrained optimization ⋮ Tensor methods for finding approximate stationary points of convex functions ⋮ A type of modified BFGS algorithm with any rank defects and the local \(Q\)-superlinear convergence properties ⋮ An efficient gradient method with approximately optimal stepsize based on tensor model for unconstrained optimization ⋮ Tensor methods for solving symmetric \({\mathcal {M}}\)-tensor systems ⋮ On High-Order Multilevel Optimization Strategies ⋮ A curvilinear search algorithm for unconstrained optimization by automatic differentiation ⋮ An adaptive conic trust-region method for unconstrained optimization ⋮ Tensor Methods for Minimizing Convex Functions with Hölder Continuous Higher-Order Derivatives ⋮ A proximal point like method for solving tensor least-squares problems

Uses Software

• TENSOLVE

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