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A New Successive Linearization Approach for Solving Nonlinear Programming Problems

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dc.contributor.authorAlbayrak, Inci
dc.contributor.authorSivri, Mustafa
dc.contributor.authorTemelcan, Gizem
dc.date.accessioned2026-06-27T14:17:43Z
dc.date.issued2019
dc.description.abstractIn this paper, we focused on general nonlinear programming (NLP) problems having m nonlinear (or linear) algebraic inequality (or equality or mixed) constraints with a nonlinear (or linear) algebraic objective function in n variables. We proposed a new two-phase-successive linearization approach for solving NLP problems. Aim of this proposed approach is to find a solution of the NLP problem, based on optimal solution of linear programming (LP) problems, satisfying the nonlinear constraints oversensitively. This approach leads to novel methods. Numerical examples are given to illustrate the approach.en
dc.identifier.endpage451
dc.identifier.issn1932-9466
dc.identifier.issue1
dc.identifier.startpage437
dc.identifier.urihttps://hdl.handle.net/20.500.14981/58920
dc.identifier.volume14
dc.identifier.wos000500471100030
dc.language.isoeng
dc.publisherPRAIRIE VIEW A & M UNIV, DEPT MATHEMATICS
dc.relation.ispartofAPPLICATIONS AND APPLIED MATHEMATICS-AN INTERNATIONAL JOURNAL
dc.subjectNonlinear programming problems
dc.subjectTaylor series
dc.subjectLinear programming problems
dc.subjectHessian matrix
dc.subjectMaclaurin series
dc.subjectLinearization approach
dc.subjectMathematics
dc.titleA New Successive Linearization Approach for Solving Nonlinear Programming Problems
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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