Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice by Daniel Bienstock
This text draws on the research developments in three broad areas: linear and integer programming, numerical analysis and the computational architectures which enable speedy, high-level algorithm design. Since the 1990s, a new body of research within the field of optimization research has emerged, which seeks to develop good approximation algorithms for classes of linear programming problems. This work has roots in fundamental areas of mathematical programming and is also framed in the context of the modern theory of algorithms. The result of this work has been a family of algorithms with solid theoretical foundations and with growing experimental success. This book examines these algorithms, starting with some of the very earliest examples through to the latest theoretical and computational developments.-
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Optimal Search for Moving Targets
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An Introduction to Robust Combinatorial Optimization
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Handbook of Markov Decision Processes
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Computational Probability
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Handbook of Stochastic Models and Analysis of Manufacturing System Operations
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Modeling Uncertainty
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Analysis and Modeling of Manufacturing Systems
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Military Operations Research
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Stochastic Benchmarking
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Constraint-Based Scheduling
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Quantitative Models for Supply Chain Management
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Health Care Benchmarking and Performance Evaluation
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Elicitation
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Linear Programming
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Linear and Nonlinear Programming
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Markovian Demand Inventory Models
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Building Supply Chain Excellence in Emerging Economies
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Evaluation and Decision Models
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Mathematical Programming and Financial Objectives for Scheduling Projects
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Judgment in Predictive Analytics
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Outline of Complex Systems Management Theory— Based on Irreversibility of Reductionism Thinking
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Handbook of Semidefinite Programming
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Robust Optimization in Electric Energy Systems
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The Theory and Practice of Revenue Management
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Multicriteria Decision Making
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Optimal Inventory Modeling of Systems
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Queueing Networks
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Handbook on Data Envelopment Analysis
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Process Optimization
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Spare Parts Inventory Control under System Availability Constraints
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Applied Linear Regression for Business Analytics with R
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Economics of Power Systems
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Handbook of Quantitative Supply Chain Analysis
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Multicriterion Decision in Management
Bienstock, Daniel: - Daniel Bienstock is a Professor in the Department of Industrial Engineering and Operations Research, Columbia University, with a joint affiliation to the Department of Applied Physics and Applied Mathematics. Prior to joining Columbia University, Professor Bienstock was in the combinatorics and optimization research group at Bellcore. He has also participated in collaborative research with several industrial partners. He received the 2013 INFORMS Fellow Award, a Presidential Young Investigator Award, and an IBM Faculty Award and has given both a plenary address at the 2005 SIAM Conference on Optimization and a semi-plenary at the 2006 ISMP conference. His research focuses on optimization and high-performance computing, with a second focus on the use of computational mathematics in the analysis and control of power grids, especially the study of vulnerabilities and of cascading blackouts.
| SKU | Unavailable |
| ISBN 13 | 9781402071737 |
| ISBN 10 | 1402071736 |
| Title | Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice |
| Author | Daniel Bienstock |
| Series | International Series In Operations Research And Management Science Ser |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Kluwer Academic Publishers |
| Year published | 2002-08-31 |
| Number of pages | 111 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |