article · 2015
LINEARIZING QUADRATIC ASSIGNMENT PROBLEM
Continuous Optimization, Knowledge-based Technologies, L. Sakalauskas, G. W. Weber, Selected Papers Vilnius, Surya Prakash Singh
Cited by 0Abstract and details only
Abstract
Abstract: In past several linearization of the Quadratic Assignment Problem (QAP) which is a NP-hard problem have been given (See Lawler (1962) and Christofides et al. (1980)). Here, a new set of con-straints that perfectly linearize the QAP is proposed. The new linearization is compared with other lin-earization and it is found that proposed linearization performs better in time complexity. We have also shown that linear programming relaxations of these linearizations give optimal solution for the 0-1 integer linearization of QAP. Empirical evidence shows that this results in substantial savings in computational time. T-test has been also carried out to show the significance statistically.
Cite this paper
Optimization, C., Technologies, K.-B., Sakalauskas, L., Weber, G. W., Vilnius, S. P., & Singh, S. P. (2015). LINEARIZING QUADRATIC ASSIGNMENT PROBLEM.
Optimization, Continuous, et al. “LINEARIZING QUADRATIC ASSIGNMENT PROBLEM.” 2015.
Optimization, Continuous, Knowledge-based Technologies, L. Sakalauskas, G. W. Weber, Selected Papers Vilnius, and Surya Prakash Singh. 2015. “LINEARIZING QUADRATIC ASSIGNMENT PROBLEM.”
Optimization, C. et al. (2015) ‘LINEARIZING QUADRATIC ASSIGNMENT PROBLEM’.
@article{optimization2015linearizing,
title = {{LINEARIZING QUADRATIC ASSIGNMENT PROBLEM}},
author = {Optimization, Continuous and Technologies, Knowledge-based and Sakalauskas, L. and Weber, G. W. and Vilnius, Selected Papers and Singh, Surya Prakash},
year = {2015}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.