Data Correcting Approaches in Combinatorial Optimization [electronic resource] / by Boris Goldengorin, Panos M. Pardalos.

By: Goldengorin, Boris [author.]
Contributor(s): Pardalos, Panos M [author.] | SpringerLink (Online service)
Material type: TextTextSeries: SpringerBriefs in Optimization: Publisher: New York, NY : Springer New York : Imprint: Springer, 2012Description: X, 114 p. 41 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781461452867Subject(s): Mathematics | Data structures (Computer science) | Algorithms | Mathematical optimization | Graph theory | Mathematics | Graph Theory | Optimization | Data Structures | Algorithm Analysis and Problem Complexity | AlgorithmsAdditional physical formats: Printed edition:: No titleDDC classification: 511.5 LOC classification: QA166-166.247Online resources: Click here to access online In: Springer eBooksSummary: Data Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems.  Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis  as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization  introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance  one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.
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Data Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems.  Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis  as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization  introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance  one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.

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