Efficient Methods for Large-scale Sparse Linear and Quadratic Programs PDF Download

Author: Seyoung Oh
Publisher:
ISBN:
Category :
Languages : en
Pages : 256

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Author: Yousef Saad
Publisher: SIAM
ISBN: 0898715342
Category : Mathematics
Languages : en
Pages : 537

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Book Description
Mathematics of Computing -- General.

Author: Gianni Pillo
Publisher: Springer Science & Business Media
ISBN: 0387300651
Category : Mathematics
Languages : en
Pages : 297

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Book Description
This book reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research. Individual chapters, contributed by eminent authorities, provide an up-to-date overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications.

Author: Thomas Frederick Coleman
Publisher: SIAM
ISBN: 9780898712681
Category : Mathematics
Languages : en
Pages : 278

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Book Description
Papers from a workshop held at Cornell University, Oct. 1989, and sponsored by Cornell's Mathematical Sciences Institute. Annotation copyright Book News, Inc. Portland, Or.

Author: Katta G Murty
Publisher: Springer
ISBN: 9781441913104
Category : Mathematics
Languages : en
Pages : 482

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Book Description
Linear programming (LP), modeling, and optimization are very much the fundamentals of OR, and no academic program is complete without them. No matter how highly developed one’s LP skills are, however, if a fine appreciation for modeling isn’t developed to make the best use of those skills, then the truly ‘best solutions’ are often not realized, and efforts go wasted. Katta Murty studied LP with George Dantzig, the father of linear programming, and has written the graduate-level solution to that problem. While maintaining the rigorous LP instruction required, Murty's new book is unique in his focus on developing modeling skills to support valid decision making for complex real world problems. He describes the approach as 'intelligent modeling and decision making' to emphasize the importance of employing the best expression of actual problems and then applying the most computationally effective and efficient solution technique for that model.

Author: Stanford University. Systems Optimization Laboratory
Publisher:
ISBN:
Category :
Languages : en
Pages : 40

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Book Description
Optimization algorithms typically require the solution of many systems of linear equations B sub Y sub = b sub. When large numbers of variables or constraints are present, these linear systems could account for much of the total computation time. Both direct and iterative equation solvers are needed in practice. Unfortunately, most of the off-the shelf solvers are designed for single systems, whereas optimization problems give rise to hundreds or thousands of systems. To avoid refactorization, or to speed the convergence of an iterative method, it is essential to note that B sub is related to B sub - 1. The authors review various sparse matrices that arise in optimization, and discuss compromises that are currently being made in dealing with them. Since significant advances continue to be made with single-system solvers they give special attention to methods that allow such solvers to be used repeatedly on a sequence of modified systems (e.g., the product-form update; use of the Schur complement). The speed of factorizing a matrix then becomes relatively less important than the efficiency of subsequent solves with very many right-hand sides. At the same time it is hoped that future improvements to linear-equation software will be oriented more specifically to the case of related matrices B sub k. (Author).

Author: Stanford University. Department of Operations Research. Systems Optimization Laboratory
Publisher:
ISBN:
Category :
Languages : en
Pages : 142

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Author: Philip E. Gill
Publisher:
ISBN:
Category : Mathematical optimization
Languages : en
Pages : 34

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Book Description
Several methods for nonlinearly constrained optimization have been suggested in recent years that are based on solving a quadratic programming (QP) subproblem to determine the direction of search. Even for dense problems, there is no consensus at present concerning the 'best' formulation of the QP subproblem. When solving large problems, many of the options possible for small problems become unreasonably expensive in terms of storage and/or arithmetic operations. This paper discusses the inherent difficulties of developing QP-based methods for large-scale nonlinearly constrained optimization, and suggests some possible approaches. (Author).

Author: Ciyou Zhu
Publisher:
ISBN:
Category : Quadratic programming
Languages : en
Pages : 180

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Author:
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 90

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Book Description