Branch-and-Bound Applications in Combinatorial Data Analysis
- Author
- Michael J. Brusco, Stephanie Stahl
- Publication Year
- 2005
- Publisher
- Springer
- Language
- English
- Document Type
- Book
- Faculty / Subject Heading
- Mathematics and Statistics
- Download Book Read book
There are a variety of combinatorial optimization problems that are relevant to the examination of statistical data. Combinatorial problems arise in the clustering of a collection of objects, the seriation (sequencing or ordering) of objects, and the selection of variables for subsequent multivariate statistical analysis such as regression. The options for choosing a solution strategy in combinatorial data analysis can be overwhelming. Because some problems are too large or intractable for an optimal solution strategy, many researchers develop an over-reliance on heuristic methods to solve all combinatorial problems. However, with increasingly accessible computer power and ever-improving methodologies, optimal solution strategies have gained popularity for their ability to reduce unnecessary uncertainty. In this monograph, optimality is attained for nontrivially sized problems via the branch-and-bound paradigm.
Keywords: Mathematics and Statistics / Analysis / Cluster analysis / Graph / Regression analysis / Sim / Statistical Analysis / Algorithms / Classification / Clustering / Data analysis / Operations research / Optimization
