Journal Article10.1137/S1052623497325107
An Interior Point Algorithm for Large-Scale Nonlinear Programming
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TL;DR: The design and implementation of a new algorithm for solving large nonlinear programming problems follows a barrier approach that employs sequential quadratic programming and trust regions to solve the subproblems occurring in the iteration.
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Abstract: The design and implementation of a new algorithm for solving large nonlinear programming problems is described. It follows a barrier approach that employs sequential quadratic programming and trust regions to solve the subproblems occurring in the iteration. Both primal and primal-dual versions of the algorithm are developed, and their performance is illustrated in a set of numerical tests.
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On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
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References
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Practical Methods of Optimization
Roger Fletcher
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TL;DR: The aim of this book is to provide a Discussion of Constrained Optimization and its Applications to Linear Programming and Other Optimization Problems.
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An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds
Thomas F. Coleman,Yuying Li +1 more
TL;DR: In this paper, a trust region approach for minimizing nonlinear functions subject to simple bounds is proposed, where the trust region is defined by minimizing a quadratic function subject only to an ellipsoidal constraint and the iterates generated by these methods are always strictly feasible.
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Stephen J. Wright
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TL;DR: This chapter discusses Primal Method Primal-Dual Methods, Path-Following Algorithm, and Infeasible-Interior-Point Algorithms, and their applications to Linear Programming and Interior-Point Methods.
2.6K