Nonlinear Programming 2 (eBook, PDF)
Proceedings of the Special Interest Group on Mathematical Programming Symposium Conducted by the Computer Sciences Department at the University of Wisconsin - Madison, April 15-17, 1974
Redaktion: Mangasarian, O. L.; Robinson, S. M.; Meyer, R. R.
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Nonlinear Programming 2 covers the proceedings of the Special Interest Group on Mathematical Programming Symposium conducted by the Computer Sciences Department at the University of Wisconsin, Madison, on April 15-17, 1974.This book is divided into 13 chapters and begins with a survey of the global and superlinear convergence of a class of algorithms obtained by imposing changing bounds on the variables of the problem. The succeeding chapters deal with the convergence of the well-known reduced gradient method under suitable conditions and a superlinearly convergent quasi-Newton method for unco...
Nonlinear Programming 2 covers the proceedings of the Special Interest Group on Mathematical Programming Symposium conducted by the Computer Sciences Department at the University of Wisconsin, Madison, on April 15-17, 1974.
This book is divided into 13 chapters and begins with a survey of the global and superlinear convergence of a class of algorithms obtained by imposing changing bounds on the variables of the problem. The succeeding chapters deal with the convergence of the well-known reduced gradient method under suitable conditions and a superlinearly convergent quasi-Newton method for unconstrained minimization. These topics are followed by discussion of a superlinearly convergent algorithm for linearly constrained optimization problems and the effective methods for constrained optimization, namely the method of augmented Lagrangians. Other chapters explore a method for handling minimization problems with discontinuous derivatives and the advantages of factorizations of updating for Jacobian-related matrices in minimization problems. The last chapters present the Newton-like methods for the solution of nonlinear equations and inequalities, along with the various aspects of integer programming.
This book will prove useful to mathematicians and computer scientists.
This book is divided into 13 chapters and begins with a survey of the global and superlinear convergence of a class of algorithms obtained by imposing changing bounds on the variables of the problem. The succeeding chapters deal with the convergence of the well-known reduced gradient method under suitable conditions and a superlinearly convergent quasi-Newton method for unconstrained minimization. These topics are followed by discussion of a superlinearly convergent algorithm for linearly constrained optimization problems and the effective methods for constrained optimization, namely the method of augmented Lagrangians. Other chapters explore a method for handling minimization problems with discontinuous derivatives and the advantages of factorizations of updating for Jacobian-related matrices in minimization problems. The last chapters present the Newton-like methods for the solution of nonlinear equations and inequalities, along with the various aspects of integer programming.
This book will prove useful to mathematicians and computer scientists.
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