Numerical comparisons of nonlinear programming algorithms on serial and vector processors using automatic differentiation

DOI10.1007/BF01589412zbMath0665.90081DBLPjournals/mp/GrandinettiC88WikidataQ62635512 ScholiaQ62635512MaRDI QIDQ1116896

Lucio Grandinetti, Domenico Conforti

Publication date: 1988

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

zbMATH Keywords

parallel optimizationvector processorshighly nonlinear constraintslarge-scale nonlinear optimizationreal-world optimal electrical power flow

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Operations research and management science (90B99)

Uses Software

• NLPQL
• ZQPCVX

Cites Work

• A numerically stable dual method for solving strictly convex quadratic programs
• Automatic differentiation: techniques and applications
• A globally convergent method for nonlinear programming
• On the convergence of a sequential quadratic programming method with an augmented lagrangian line search function
• Truncated-Newton algorithms for large-scale unconstrained optimization
• On the quadratic programming algorithm of Goldfarb and Idnani
• Superlinearly convergent variable metric algorithms for general nonlinear programming problems
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