The MOSEK Python optimizer API manual Version 7.0 (Revision 141)
Optimizer API for Python - Documentation - Mosek Optimizer API for Python - Documentation - Mosek
162 CHAPTER 12. THE OPTIMIZERS FOR MIXED-INTEGER PROBLEMS
Chapter 13 The analyzers 13.1 The problem analyzer The problem analyzer prints a detailed survey of the • linear constraints and objective • quadratic constraints • conic constraints • variables of the model. In the initial stages of model formulation the problem analyzer may be used as a quick way of verifying that the model has been built or imported correctly. In later stages it can help revealing special structures within the model that may be used to tune the optimizer’s performance or to identify the causes of numerical difficulties. The problem analyzer is run from the command line using the -anapro argument and produces something similar to the following (this is the problemanalyzer’s survey of the aflow30a problem from the MIPLIB 2003 collection, see Appendix G for more examples): Analyzing the problem Constraints Bounds Variables upper bd: 421 ranged : all cont: 421 fixed : 58 bin : 421 ------------------------------------------------------------------------------- Objective, min cx range: min |c|: 0.00000 min |c|>0: 11.0000 max |c|: 500.000 distrib: |c| vars 0 421 163
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Chapter 13<br />
<strong>The</strong> analyzers<br />
13.1 <strong>The</strong> problem analyzer<br />
<strong>The</strong> problem analyzer prints a detailed survey of the<br />
• linear constraints and objective<br />
• quadratic constraints<br />
• conic constraints<br />
• variables<br />
of the model.<br />
In the initial stages of model formulation the problem analyzer may be used as a quick way of verifying<br />
that the model has been built or imported correctly. In later stages it can help revealing special<br />
structures within the model that may be used to tune the <strong>optimizer</strong>’s performance or to identify the<br />
causes of numerical difficulties.<br />
<strong>The</strong> problem analyzer is run from the command line using the -anapro argument and produces something<br />
similar to the following (this is the problemanalyzer’s survey of the aflow30a problem from the<br />
MIPLIB 2003 collection, see Appendix G for more examples):<br />
Analyzing the problem<br />
Constraints Bounds Variables<br />
upper bd: 421 ranged : all cont: 421<br />
fixed : 58 bin : 421<br />
-------------------------------------------------------------------------------<br />
Objective, min cx<br />
range: min |c|: 0.00000 min |c|>0: 11.0000 max |c|: 500.000<br />
distrib: |c| vars<br />
0 421<br />
163
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