an introduction to semidefinite programming for ...umich.edu › ~ipco2019conf › burer2.pdf ·...
TRANSCRIPT
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An Introduction to Semidefinite Programming for Combinatorial
Optimization (Lecture 2)Sam Burer
University of Iowa
IPCO Summer SchoolMay 20-21, 2019
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Outline
1. How to Convexify Nonconvex QP
2. Continuous Case
3. Mixed Binary Case
4. Mixed Integer Case
5. Final Thoughts
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How to Convexify Nonconvex QP
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What is
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What is ?
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Source: B, "A Gentle, Geometric Introduction to Copositive Optimization", Mathematical Programming Series B, 151(1), 89–116, 2015 8
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Source: B and Letchford, "Unbounded Convex Sets for Non-Convex Mixed-Integer Quadratic Programming", Mathematical Programming, 143(1-2), 231-256, 2014 9
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• is not polyhedral, and taking the closure is necessary
• This approach may not be the most practical approach
• Certainly characterizing is a hard problem
• If your QP doesn't contain all variables, it may make sense to convexify just over your variables
• In fact, it might be better to lift to an even larger set of variables
• But understanding is our goal today
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Observation. Fully characterizing is equivalent to identifying all quadratics , which are nonnegative for all
Which is equivalent to identifying all matrices
whose associated quadratic is nonnegative over . Such matrices are called copositive over
In fact, is essentially the dual of "copositive over "11
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Three Types of Valid Inequalities
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• Given , we will identify valid inequalities for
• Each will come from one of three classes...
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Type 1: Explicit Quadratics
If
constrains , then
is a valid linear inequality for
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Type 2: Linear Conjunctions
If
are valid for , then
is a valid linear inequality for
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Type 3: Linear Disjunctions
If every satisfies
then
is a valid linear inequality for
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Continuous Nonconvex QP
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1. Unconstrained
2. Linear equations
3. Nonnegative orthant
4. Nonnegative orthant, linear equations, and complementarities
5. Linear inequalities
6. Half-ellipsoid
7. Swiss cheese
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Unconstrained
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Not hard to prove from first principles 24
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Proposition. is generated by all disjunctions of the form
where .
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Linear Equations
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B 2009 28
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Theorem (B 2009). is generated by
1. all PSD disjunctions
2. conjuctions of the form for all pairs of constraints in .
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Nonnegative Orthant
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Not hard to prove from first principles 31
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Theorem (Maxfiled-Minc 1962). if and only if .
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• In the literature, "copositive over the nonnegative orthant" is typically just called "copositive"
• Regarding , is the dual cone of the copositive matrices, i.e.,
• Generating a valid inequality for is hard, but we can do better with a sums-of-squares approach
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Nonnegative Orthant, Linear Equations, and Complementarities
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B 2009 35
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Linear Inequalities
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(equality when )
Anstreicher-B 2010, B 2015 37
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The linear constraints
are known as RLT constraints
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Half-Ellipsoid
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Ye-Zhang 2003 41
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The constraint
is known as an SOCRLT constraint
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Theorem (Yang-Anstreicher-B 2018). Consider the intersection of
• ("ball")
• ("cuts")
• ("holes") for all , where each
If none of the cuts and holes touch each other, then
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Mixed Binary QP
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B 2009 46
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As long as ensures
• for all
• bounded for all
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and
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Proposition. Since ,
I.e., any valid inequality for is automatically valid for
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A partial converse holds...
Theorem (B-Letchford 2009). Adding to captures
Corollary. Any valid inequality for —which has no terms—is automatically valid for
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Mixed Integer QP
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Integer Lattice
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Theorem (B-Letchford 2014). For ,
The first inclusion holds with equality if but is strict for
Note. The cases are unresolved
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Nonnegative Integer Lattice
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Source: B and Letchford, "Unbounded Convex Sets for Non-Convex Mixed-Integer Quadratic Programming", Mathematical Programming, 143(1-2), 231-256, 2014 56
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Theorem (B-Letchford 2014). For ,
But not much else is known !
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Remark. Buchheim-Traversi showed how, in practice, to separate:
•
• for the ternary case
They also demonstrated the effectiveness of these cuts in terms of closing the gap
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Crazy Observation. For both cases and , every extreme point of lies on a countably infinite number of facets
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Final Thoughts
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• For nonconvex QP, there is still lots to do, especially the integer case
• Convexification involves aspects of
• convex analysis
• polyhedral theory
• SDP
• polynomial optimization
• Hence, a very interesting area to study
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Thank You
And good luck with your research!
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