MINQ8: general definite and bound constrained indefinite quadratic programming

DOI10.1007/s10589-017-9949-yzbMath1400.90240OpenAlexW2762271916WikidataQ59607599 ScholiaQ59607599MaRDI QIDQ1744884

Arnold Neumaier, Waltraud Huyer

Publication date: 20 April 2018

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

Full work available at URL: https://doi.org/10.1007/s10589-017-9949-y

zbMATH Keywords

dual programcertificate of infeasibilitybound constrained indefinite quadratic programmingdefinite quadratic programming

Mathematics Subject Classification ID

Quadratic programming (90C20)

Related Items (9)

Inexact variable metric method for convex-constrained optimization problems ⋮ An extended projected residual algorithm for solving smooth convex optimization problems ⋮ LMBOPT: a limited memory method for bound-constrained optimization ⋮ Solving nearly-separable quadratic optimization problems as nonsmooth equations ⋮ Quadratic maximization of reachable values of affine systems with diagonalizable matrix ⋮ MINQ8 ⋮ MINQ ⋮ A predictor-corrector affine scaling method to train optimized extreme learning machine ⋮ A reduced proximal-point homotopy method for large-scale non-convex BQP

Uses Software

• Matlab
• CUTEr
• SifDec
• NewtonKKTqp
• SNOBFIT
• MCS

Cites Work

• Newton-KKT interior-point methods for indefinite quadratic programming
• A collection of test problems for constrained global optimization algorithms
• Global optimization by multilevel coordinate search
• A non-interior path following method for convex quadratic programming problems with bound constraints
• Iterative Methods for Large Convex Quadratic Programs: A Survey
• Simultaneous Scatterer Shape Estimation and Partial Aperture Far-Field Pattern Denoising
• Global and Finite Termination of a Two-Phase Augmented Lagrangian Filter Method for General Quadratic Programs
• Projected filter trust region methods for a semismooth least squares formulation of mixed complementarity problems
• CUTEr and SifDec
• Introduction to global optimization.

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