
From:  Abhishek Shivakumar 
Subject:  Re: [Helpglpk] Condensing a sparse matrix by creating a subset of parameter data 
Date:  Thu, 31 Mar 2016 14:53:00 +0000 
Thanks for your message. That is indeed (almost) what I’m looking for. I would like to then apply a constraint to the subset of nodes that are being used. Specifically, I want to set a constraint on the ‘flow’
between the nodes. I agree that reducing the matrix size won’t have a significant effect on the optimization time. However, we’re generating an lp file from our model and the time to generate the lp file is likely to be reduced
significantly. I expect that there will be an optimal point between reducing the size of the matrix (and time to generate it) and the optimization time.
From: Meketon, Marc [mailto:address@hidden
Are you looking for something like the below that finds the subset of nodes being used? set nodes := {"A", "B", "C", "D", "E"}; set arcs within nodes cross nodes := {("A","B"), ("B","E")}; display nodes; display arcs; set nodes_that_are_used :=
setof{tail_node in nodes, head_node in nodes : (tail_node, head_node) in arcs } tail_node union setof{tail_node in nodes, head_node in nodes : (tail_node, head_node) in arcs } head_node;
display nodes_that_are_used; end; From:
helpglpkbounces+address@hidden [mailto:helpglpkbounces+address@hidden]
On Behalf Of Abhishek Shivakumar Hi, I have an lp model that produces an extremely sparse constraint matrix. I would like to reduce the number of nonzero entries in this matrix (make the matrix more dense). The main cause of this sparseness is the relation between two parameters.
They can be seen as ‘arcs’ and ‘nodes: not all nodes have arcs between them. Is there a way to create a subset of parameter data from the sets ‘arcs’ and ‘nodes’ based on whether they are connected? I would then be able to apply constraints only for this subset.
In other languages (such as python) such an operation is possible through nested conditional statements. Is there a similar/equivalent approach in glpk/GNU mathprog? Thanks! Abhishek This email and any attachments may be confidential or legally privileged. If you received this message in error or are not the intended recipient, you should destroy
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