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- Newsgroups: sci.math.num-analysis
- Path: sparky!uunet!munnari.oz.au!uniwa!bilby.cs.uwa.oz.au!mardo!gordon
- From: gordon@cs.uwa.oz.au (Gordon Royle)
- Subject: Linear Programming problem - NAG routine E04MBF
- Message-ID: <gordon.714813945@mardo>
- Sender: usenet@bilby.cs.uwa.edu.au
- Nntp-Posting-Host: mardo
- Organization: Dept. Computer Science, University of Western Australia.
- Date: Wed, 26 Aug 1992 07:25:45 GMT
- Lines: 48
-
-
- I have a linear programming problem (in fact an integer programming
- problem, but I thought I would experiment with the relaxation first to
- get some feel for these things).
-
- It is very simple:
-
- Minimize Sum x_i
-
- subject to 0 <= x_i <= 1
- Ax > j
-
-
- where A is a symmetric matrix with all row and column sums
- equal, and j is the all-ones vector.
-
-
- Thing is, the solution of this is obvious - the best thing is to put
-
- x_i = 1/r where r is the row sum of A
-
-
- So, I took a 243 x 243 example of this problem and gave it to
- NAG routine E04MBF.
-
- Funny thing was though - instead of quickly giving me the obvious
- solution, it gave a far-from-optimal INTEGRAL feasible point and then
- stopped, saying that it had made too many iterations without changing
- the trial solution.
-
-
- Three questions:
-
- 1. Why does it behave like this?
-
- 2. What do I do to make it give the right answer
-
- and thirdly...
-
- 3. when I get down to actually trying to solve the
- integer linear program, is there any theory around
- that says that symmetric As etc are particularly hard
- (or better still, particularly easy)
-
-
- Cheers
-
- Gordon
-