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tutorial:cola_dietcola [2014/12/03 01:03] aykutbulut |
tutorial:cola_dietcola [2015/01/20 11:24] sertalpbilal [How to use cola] |
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Cola inherits OsiClpSolverInterface class of Coin-OR Osi project. It uses Clp to solve linear optimization problems. | Cola inherits OsiClpSolverInterface class of Coin-OR Osi project. It uses Clp to solve linear optimization problems. | ||
==== How to use cola ==== | ==== How to use cola ==== | ||
- | It is pretty straightforward. Just run 'cola input.mps' | + | It is pretty straightforward. Just run 'cola input.mps' |
===== DietCOLA ===== | ===== DietCOLA ===== | ||
DietCOLA (Discrete COLA) uses branch and bound to solve second order cone optimization problems. DietCOLA is available in polyps (command dietcola). Source code of DietCOLA is available on [[https:// | DietCOLA (Discrete COLA) uses branch and bound to solve second order cone optimization problems. DietCOLA is available in polyps (command dietcola). Source code of DietCOLA is available on [[https:// | ||
==== Installation ==== | ==== Installation ==== | ||
- | DietCOLA depends on COLA and ALPS. Once COLA and ALPS are installed and their .pc file is in your PKG_CONFIG_PATH, | + | DietCOLA depends on COLA and ALPS. Once COLA and ALPS are installed and their .pc file is in your PKG_CONFIG_PATH, |
==== Using DietCOLA ==== | ==== Using DietCOLA ==== | ||
DietCOLA accepts inputs in extended mps format. See [[http:// | DietCOLA accepts inputs in extended mps format. See [[http:// | ||
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To run cola in polyps on CBLIB problems you can use, | To run cola in polyps on CBLIB problems you can use, | ||
<code bash> cola / | <code bash> cola / | ||
+ | to run DietCOLA you can use the following command; | ||
+ | <code bash> dietcola / | ||
+ | COLA and DietCOLA can also solve LP and MILP problems. When a given mps file does not have cones, COLA just reduces to CLP and DietCOLA does a branch and bound search solving LP problems at each node. You can check this with; | ||
+ | <code bash> cola / | ||
+ | <code bash> dietcola / | ||
+ | COLA solves the LP relaxation of the problem (problem 10teams includes discrete variables). |