| 期刊全稱 | Branch-and-Bound Applications in Combinatorial Data Analysis | | 影響因子2023 | Michael J. Brusco,Stephanie Stahl | | 視頻video | http://file.papertrans.cn/191/190346/190346.mp4 | | 發(fā)行地址 | Provides explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, pseudocode, and well-developed examples for (familiar as well as novel) applications of the | | 學(xué)科分類 | Statistics and Computing | | 圖書封面 |  | | 影響因子 | This monograph focuses on the application of the solution strategy known as branch-and-bound to problems of combinatorial data analysis. Combinatorial data analysis problems typically require either the sel- tion of a subset of objects from a larger (master) set, the grouping of a collection of objects into mutually exclusive and exhaustive subsets, or the sequencing of objects. To obtain verifiably optimal solutions for this class of problems, we must evaluate (either explicitly or implicitly) all feasible solutions. Unfortunately, the number of feasible solutions for problems of combinatorial data analysis grows exponentially with pr- lem size. For this reason, the explicit enumeration and evaluation of all solutions is computationally infeasible for all but the smallest problems. The branch-and-bound solution method is one type of partial enume- tion solution strategy that enables some combinatorial data analysis pr- lems to be solved optimally without explicitly enumerating all feasible solutions. To understand the operation of a branch-and-bound algorithm, we d- tinguish complete solutions from partial solutions. A complete solution is one for which a feasible solution to the | | Pindex | Book 2005 |
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