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Hi all,</div>
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I have a pricing algorithm which solves a MIQP which has a single SOC constraint, some SOS-1 constraints and a number of linear constraints.</div>
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I worked out some lower and upper bounds for some of my continuous variables (with a view to perhaps replacing the SOS-1 constraints by some bigM ones).</div>
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I have observed that merely putting these bounds on the variables in question, rather than having bounds of -infinity and infinity (and doing nothing else) leads to a big drop in performance, solving is slower.</div>
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I am not using an NLP solver. <br>
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Any ideas why this might happen? Possibly adding the bounds rules out some sub-optimal solutions which are helping somehow.
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The bounds are rather loose, but I did not expect them to be deleterious.</div>
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James<br>
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James Cussens</div>
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Dept of Computer Science, University of Bristol</div>
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<a href="https://jcussens.github.io/">https://jcussens.github.io/</a></div>
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<span style="font-size:11.5pt">Funded PhDs available in Bristol in the following areas: <a href="http://www.bristol.ac.uk/cdt/compass/" target="_blank" rel="noopener noreferrer"><span>Data Science</span></a>, <a href="http://www.bristol.ac.uk/cdt/interactive-ai/" target="_blank" rel="noopener noreferrer"><span>Interactive
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