Improving the LP bound of a MILP by branching concurrently
Abstract
We'll measure the differences of the dual variables and the gain of the objective function when creating new problems, which each has one inequality more than the starting LPinstance. These differences of the dual variables are naturally connected to the branches. Then we'll choose those differences of dual variables, so that for all combinations of choices at the connected branches, all dual inequalities will hold for sure. By adding the gain of each chosen branching, we get a total gain, which gives a better limit of the original problem. By this technique it is also possible to create cuts.
 Publication:

arXiv eprints
 Pub Date:
 November 2007
 arXiv:
 arXiv:0711.0311
 Bibcode:
 2007arXiv0711.0311B
 Keywords:

 Computer Science  Discrete Mathematics;
 Computer Science  Data Structures and Algorithms
 EPrint:
 21 pages of a possibly new theory submitted by a hobby researcher. Uses algorithmic.sty