embedding formulations, complexity and representability for unions of convex sets juan pablo vielma...
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Embedding Formulations, Complexity and Representability for Unions of Convex Sets
Juan Pablo VielmaMassachusetts Institute of Technology
CMO-BIRS Workshop: Modern Techniques in Discrete Optimization: Mathematics, Algorithms and Applications,Oaxaca, Mexico. November, 2015.
Supported by NSF grant CMMI-1351619
Minkowski Sums: Good or Evil?
Embedding Formulations
Nonlinear Mixed 0-1 Integer Formulations
• Modeling Finite Alternatives = Unions of Convex Sets
2 / 15
Extended and Non-Extended Formulations for
Large, but strong (ideal*)
Extended Non-Extended
Small, but weak?
Embedding Formulations*Integral y in extreme points of LP relaxation 3 / 15
Embedding Formulations
Constructing Non-extended Ideal Formulations
• Pure Integer : • Mixed Integer:
4 / 15
Embedding Formulations
Embedding Formulation = Ideal non-Extended
(Cayley) Embedding
5 / 15
Embedding Formulations
Alternative Encodings
• 0-1 encodings guarantee validity
• Options for 0-1 encodings:– Traditional or Unary encoding
– Binary encodings:– Others (e.g. incremental encoding unary)
6 / 15
Embedding Formulations
Unary Encoding, Minkowski Sum and Cayley Trick
For traditional or unary encoding:
7 / 15
Embedding Formulations
Encoding Selection Matters
• Size of unary formulation is: (Lee and Wilson ’01)
Variable Bounds
General Inequalities
• Size of one binary formulation: (V. and Nemhauser ’08)
• Right embedding = significant computational advantage over alternatives (Extended, Big-M, etc.)
8 / 15
Embedding Formulations
Complexity of Family of Polyhedra
• Embedding complexity = smallest ideal formulation
• Relaxation complexity = smallest formulation
9 / 15
Embedding Formulations
• Lower and Upper bounds for special structures:– e.g. for Special Order Sets of Type 2 (SOS2) on n variables
• Embedding complexity (ideal)
• Relaxation complexity (non-ideal)
• Relation to other complexity measures
• Still open questions (see V. 2015)
Complexity Results
General InequalitiesTotal
General InequalitiesTotal
10 / 15
Embedding Formulations
Example of Constant Sized Non-Ideal Formulation
• Polynomial sized coefficients:–
• 80 fractional extreme points for n = 5
3 4
11 / 15
Embedding Formulations
Faces for Ideal Formulation with Unary Encoding
• Two types of facets (or faces):–
–
– Not all combinations of faces
– Which ones are valid?
12 / 15
Embedding Formulations
Valid Combinations = Common Normals
13 / 15
Embedding Formulations
• Description of boundary of is easy if “normals condition” yields convex hull of 1 nonlinear constraint and point(s)
Unary Embedding for Unions of Convex Sets
14 / 15
Embedding Formulations
Bad Example: Representability Issues
can fail to be basic semi-algebraic
Description with finite number of (quadratic) polynomial inequalities?
Zariski closure of boundary
15 / 15
Embedding Formulations
Summary
• Embedding Formulations = Systematic procedure for strong (ideal) non-extended formulations– Encoding can significantly affect size
• Complexity of Union of Polyhedra beyond convex hull– Embedding Complexity (non-extended ideal formulation)– Relaxation Complexity (any non-extended formulation)– Still open questions on relations between complexity
(Embedding Formulations and Complexity for Unions of Polyhedra, arXiv:1506.01417)
• Embedding Formulations for Convex Sets– MINLP formulations– Can have representability issues
• Open question: minimum number of auxiliary variables for fixing this
Embedding Formulations
Example: Pizza Slices
17 / 24
= 4 conic + 4 linear inequalities
Embedding Formulations
Final Positive Results
18 / 24
• Unions of Homothetic Convex Bodies
(all extreme points exposed)
• Generalizes polyhedral results from Balas ‘85, Jeroslow ’88 and Blair ‘90
Embedding Formulations
Easy to Recover and Generalize Existing Results
19 / 24
• Isotone function results from Hijazi et al. ‘12 and Bonami et al. ’15 (n=1, 2): –
• Can generalize to n ≥ 3 and two functions per set:
• Other special cases (previous slide)
Embedding Formulations
Right Embedding = Significant Improvements
4 81 s
10 s
100 s
1000 s
10000 s
ExtendedGood "Big-M"Binary Embedding
• Results from Nemhauser, Ahmed and V. ’10 using CPLEX 11
20 / 24
• Non-extended and ideal formulations provide a significant computational advantage