Self-Regularity A New Paradigm for Primal-Dual Interior-Point Algorithms

Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap b...

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Bibliographische Detailangaben
Hauptverfasser:	Peng, Jiming, Roos, Cornelis, Terlaky, Tamás
Format:	E-Book Buch
Sprache:	Englisch
Veröffentlicht:	Princeton, N.J. ; Chichester Princeton University Press 2009
Ausgabe:	1
Schriftenreihe:	Princeton series in applied mathematics
Schlagworte:	Algorithm Analysis of algorithms Analytic function Associative property Barrier function Block matrix Combinatorial optimization Combinatorics Complexity Conic optimization Continuous optimization Control theory Convex optimization Delft University of Technology Derivative Duality gap Eigenvalues and eigenvectors Embedding Equation Estimation Explanation Feasible region Filter design Function (mathematics) General Topics for Engineers Implementation Instance (computer science) Invertible matrix Iteration Jacobian matrix and determinant Jordan algebra Karush–Kuhn–Tucker conditions Linear complementarity problem Linear programming Lipschitz continuity Local convergence Loss function Mathematical optimization MATHEMATICS MATHEMATICS / Applied MATHEMATICS / Optimization Matrix function McMaster University Multiplication operator Newton's method Nonlinear programming Optimal control Optimization problem Parameter Pattern recognition Polynomial Programming (Mathematics) Quadratic function Requirement Result Scientific notation Self-concordant function Sensitivity analysis Simplex algorithm Simultaneous equations Singular value Smoothness Solution set Solver Special case Theorem Theory Time complexity Variable (mathematics) Variational inequality Variational principle Without loss of generality Yurii Nesterov Polynomial Technical report McMaster University Yurii Nesterov Continuous optimization Delft University of Technology Explanation Self-concordant function Conic optimization Result Complexity Loss function Convex optimization Variational inequality Eigenvalues and eigenvectors Invertible matrix Nonlinear programming Without loss of generality Combinatorics Duality gap Sensitivity analysis Estimation Pattern recognition Filter design Algorithm Matrix function Function (mathematics) Requirement Feasible region Karush–Kuhn–Tucker conditions Derivative Special case Analysis of algorithms Mathematical optimization Time complexity Simplex algorithm Quadratic function Solver Monograph Theory Barrier function Singular value Embedding Iteration Combinatorial optimization Implementation Lipschitz continuity Solution set Associative property Theorem Local convergence Block matrix Jacobian matrix and determinant Linear programming Equation Analytic function Linear complementarity problem Jordan algebra Smoothness Instance (computer science) Variable (mathematics) Simultaneous equations Optimal control Multiplication operator Variational principle Newton's method Parameter Scientific notation Control theory Optimization problem
ISBN:	9780691091921, 0691091927, 0691091935, 9780691091938, 9781400825134, 140082513X
Online-Zugang:	Volltext
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