Complexity of Testing Existence of Solutions in Polynomial Optimization + A New Positivstellensatz Amir Ali Ahmadi Princeton, ORFE Affiliated member of PACM, COS, MAE, CSML 1 ICERM Meeting on Nonlinear Algebra: Core Computational Methods, Septβ18 Jeffrey Zhang Princeton, ORFE Georgina Hall PrincetonINSEAD Joint work with
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Complexity of Testing Existence of Solutions in Polynomial Optimization
+A New Positivstellensatz
Amir Ali AhmadiPrinceton, ORFE
Affiliated member of PACM, COS, MAE, CSML
1ICERM Meeting on Nonlinear Algebra: Core Computational Methods, Septβ18
Jeffrey Zhang Princeton, ORFE
Georgina HallPrincetonINSEAD
Joint work with
Existence of solutions
2
Consider a polynomial optimization problem (POP):
Suppose the optimal value is finite (i.e., POP is feasible and bounded below). We would like to test if there exists an optimal solution, i.e.,
a feasible point π₯β such that π π₯β β€ π π¦ , βπ¦ feasible.
Informally: βCan we replace the `infβ with a `minβ?β
infπ₯ββπ
π(π₯)
s.t. ππ π₯ β₯ 0, π = 1,β¦ ,π.
Remarks:β’ If feasible set is bounded, a solution always exists.β’ If π = 1, a solution always exists.β’ Finiteness of optimal value comes as a βpromiseβ.
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Motivation
[Nie, Demmel, Sturmfels, βMinimizing polynomials via sum of squares over the gradient idealβ, Math. Prog. 2005]:
βThis assumption [existence of minimizers] is nontrivial, and we do not address the (important and difficult) question of how to verify that a given polynomial π π₯ has this property.β
- An exact algorithm cannot return a solution if there is none!- Existence of solutions essential for algorithms that exploit optimality conditions.
There are algothims that check existence of solutions:β’ Greuet, Safey El Din, βDeciding reachability of the infimum of a multivariate polynomialβ, International
Symposium on Symbolic and Algebraic Computation, 2011β’ Boucero, Mourrain, βBorder basis relaxation for polynomial optimizationβ, Journal of Symbolic
Computation, 2016β’ Greuet, Safey El Din, βProbabilistic algorithm for polynomial optimization over a real algebraic setβ,
SIAM J. on Optimization, 2014β’ Quantifier eliminationβ’ β¦
All have running time at least exponential in dimensionβ¦Can there be a faster algorithm?
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Existence of a solution guaranteed?
0 1 2
1 Yes Yes
Linear ProgrammingNP-hard to test
(This work)
2 Yes
Linear Algebra
Yes
Frank, Wolfe (1956)
3 Yes Yes
Andronov, Belousov, Shironin (1982)
4 NP-hard to test (This work)
Degree of constraints
Deg
ree
of
ob
ject
ive
minπ₯ββπ
π(π₯)
s.t. ππ π₯ β₯ 0, π = 1,β¦ ,π
Outline
1) NP-hardness of testing existence of solutions
2) Sufficient conditions for existence of solutions
a. Review of SOS and Positivstellensatze
b. An SOS hierarchy for coercivity
3) An optimization-free Positivstellensatz(brief and independent)
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Outline
1) NP-hardness of testing existence of solutions
2) Sufficient conditions for existence of solutions
a. Review of SOS and Positivstellensatze
b. An SOS hierarchy for coercivity
3) An optimization-free Positivstellensatz(brief and independent)
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Main hardness results
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Theorem (AAA, Zhang)
Testing whether a degree-4 polynomial attains its unconstrainedinfimum is strongly NP-hard.
Theorem (AAA, Zhang)
Testing whether a degree-1 polynomial attains its infimum on a set defined by degree-2 inequalities is strongly NP-hard.
Proof: Reduction from 1-in-3 3SAT.
6 (for this presentation)/
1-in-3 3SAT
- Input: A CNF formula with three literals per clause.- Goal: Find a Boolean assignment so that each clause has exactly one true literal.
β’ Infimum is always zero (ππ is a sum of squares; take π = 0, π¦ β 0, π§ =1
π¦).
β’ If π satisfiable, take π = 1, and π₯ the satisfying assignment Infimum attained.β’ If π not satisfiable, ππ does not vanish Infimum not attained.
Outline
1) NP-hardness of testing existence of solutions
2) Sufficient conditions for existence of solutions
a. Review of SOS and Positivstellensatze
b. An SOS hierarchy for coercivity
3) An optimization-free Positivstellensatz(brief and independent)
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Sufficient conditions for existence of solutions
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Existence of solutions
Compactness of the feasible set
Coercivity of the objective function
Convexity of the of the objective function and concavity of the constraints
Archimedean Condition
Stable Compactness
SDP hierarchy
SDP hierarchy
Theorem
- Testing whether a polynomial optimization problem satisfies any of these conditions is strongly NP-hard.- Our results are minimal in the degree.
minπ₯ββπ
π(π₯)
s.t. ππ π₯ β₯ 0, π = 1,β¦ ,π
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Review of sum of squaresand Positivstellensatze
How to prove positivity?
β’ (Tight) lower bounds for polynomial minimization problems
Is π π₯ > 0 on {π1 π₯ β₯ 0,β¦ , ππ π₯ β₯ 0}?
Why prove positivity?
β’ Infeasibility certificates for systems of polynomial inequalities
Search for these sos certificates of infeasibility (when deg. is fixed) ---> SDP.
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Back to coercivity
Coercivity
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Definition: A function π is coercive if for every sequence {π₯π} such that ||π₯π|| β β, we have π π₯ β β.β’ A coercive function attains its infimumβ’ Checking whether a polynomial is coercive is NP-hard
We provide a condition which is (i) both necessary and sufficient for a polynomial to be coercive and (ii) amenable to an SDP hierarchy
A polynomial π of degree π is coercive if and only if for some integer π β₯ 1 the following SDP is feasible
An sos hierarchy for testing coercivity (2/2)
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Equivalently, p is coercive if and only if there exist an even integer πβ² and a
scalar πβ² for which the set {(π₯, πΎ)|π π₯ β€ πΎ, π₯π2 β₯ πΎπ
β²+ πβ²} is empty.
Theorem (AAA, Zhang)
A polynomial p is coercive if and only if there exist an even integer π > 0and a scalar π β₯ 0 such that for all πΎ β β, the πΎ-sublevel set of p is contained within a ball of radius πΎπ + π.
A function is coercive if and only if all its sublevel sets are compact.