Globally convergent algorithms for solving unconstrained optimization problems

DOI10.1080/02331934.2012.745529zbMath1311.90182OpenAlexW2059380220MaRDI QIDQ4981855

Sattar Seifollahi, Sona Taheri, Musa A. Mammadov

Publication date: 20 March 2015

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/60698

zbMATH Keywords

unconstrained optimizationglobal convergenceNewton methodgradient methodsuperlinear convergencequasi-Newton method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Newton-type methods (49M15) Methods of quasi-Newton type (90C53)

Related Items (3)

Unnamed Item ⋮ Learning the naive Bayes classifier with optimization models ⋮ General viscosity iterative approximation for solving unconstrained convex optimization problems

Cites Work

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• Newton-conjugate-gradient methods for solitary wave computations
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• A modified Newton method with cubic convergence: the multivariate case
• Testing Unconstrained Optimization Software
• A combined conjugate-gradient quasi-Newton minimization algorithm
• Extending the relationship between the conjugate gradient and BFGS algorithms
• Modified quasi-Newton methods for solving systems of linear equations

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