POLYNOMIAL OPTIMIZATION WITH SUMS-OF-SQUARES INTERPOLANTS

Transcription

1 POLYNOMIAL OPTIMIZATION WITH SUMS-OF-SQUARES INTERPOLANTS Sercan Yıldız in collaboration with Dávid Papp (NCSU) OPT Transition Workshop May 02, 2017

2 OUTLINE Polynomial optimization and sums of squares Sums-of-squares hierarchy Semidefinite representation of sums-of-squares constraints Interior-point methods and conic optimization Sums-of-squares optimization with interior-point methods Computational complexity Preliminary results

3 POLYNOMIAL OPTIMIZATION Polynomial optimization problem: min z R n s.t. where f (z) g i (z) 0 for i = 1,..., m f, g 1,..., g m are n-variate polynomials Example: min z R 2 z z 2 1 z 2 6z 1 z z 3 2 s.t. z z Some applications: Shape-constrained estimation Design of experiments Control theory Combinatorial optimization Computational geometry Optimal power flow Source: murray/amwiki/images/thumb/1/ 19/Doscpp.png/270px- Doscpp.png Source: org/wikipedia/commons/thumb/c/ cf/max- cut.svg/200px- Max- cut. svg.png Source: org/wikipedia/commons/thumb/d/ d2/kissing- 2d.svg/ 190px- Kissing- 2d.svg.png

4 SUMS-OF-SQUARES RELAXATIONS Let f be an n-variate degree-2d polynomial. Unconstrained polynomial optimization: min z R n f (z) NP-hard already for d = 2! Equivalent dual formulation: max y R s.t. y f (z) y P where P = {f : f (z) 0 z R n } Sums-of-squares cone: SOS = {f : f = N j=1 f 2 j for some degree-d polynomials f j } SOS relaxation: f SOS f P max y R s.t. y f (z) y SOS

5 SUMS-OF-SQUARES RELAXATIONS Let f, g 1,..., g m be n-variate polynomials. Constrained polynomial optimization: min z R n s.t. f (z) g i (z) 0 for i = 1,..., m Feasible set: G = {z R n : g i (z) 0 for i = 1,..., m}. Dual formulation: max y R y s.t. Weighted SOS cone of order r: f (z) y P G where P G = {f : f (z) 0 z G} SOS G,r = {f : f = m i=0 g is i, s i SOS, deg(g i s i ) r} where g 0 1 SOS relaxation of order r: f SOS G,r f P G max y R s.t. y f (z) y SOS G,r

6 WHY DO WE LIKE SOS? Polynomial optimization is NP-hard. Increasing r produces a hierarchy of SOS relaxations for polynomial optimization problems. Under mild assumptions: The lower bounds from SOS relaxations converge to the true optimal value as r (follows from Putinar s Positivstellensatz). SOS relaxations can be represented as semidefinite programs of size O(mL 2 ) where L = ( ) n+r/2 n (follows from the results of Shor, Nesterov, Parrilo, Lasserre). There are efficient and stable numerical methods for solving SDPs.

7 SOS CONSTRAINTS ARE SEMIDEFINITE REPRESENTABLE Consider the cone of degree-2d SOS polynomials: SOS 2d = {f R[z] 2d : f = N j=1 f 2 j for some f j R[z] d } Theorem (Nesterov, 2000) The univariate polynomial f (z) = 2d f u=0 uz u is SOS iff there exists a (d + 1) (d + 1) PSD matrix S such that fu = k+l=u S kl u = 0,..., 2d.

8 SOS CONSTRAINTS ARE SEMIDEFINITE REPRESENTABLE More generally, in the n-variate case: Let L := dim(r[z] d ) = ( ) n+d n and U := dim(r[z]2d ) = ( ) n+2d n. Fix bases {p l } L l=1 and {q u} U u=1 for the linear spaces R[z] d and R[z] 2d. Theorem (Nesterov, 2000) The polynomial f (z) = U f u=1 uq u(z) is SOS iff there exists a L L PSD matrix S such that f = Λ (S) where Λ : R U S L is the linear map satisfying Λ([q u] U u=1) = [p l ] L l=1[p l ] L l=1. If univariate polynomials are represented in the monomial basis: x 0 x 1 x 2... x d x 1 x 2 x 2 Λ(x) =. x 2d 1 x d x 2d 1 x 2d These results easily extend to the weighted case. Λ (S) = [ k+l=u S ] 2d kl. u=0

9 SOS CONSTRAINTS ARE SEMIDEFINITE REPRESENTABLE To keep things simple, we focus on optimization over a single SOS cone. SOS problem: max y R k,s S L s.t. b y A y + Λ (S) = c S 0 Moment problem: A y + s = c s SOS := {s R U : s = Λ (S), S 0} min x R U s.t. c x Ax = b Λ(x) 0 x SOS := {x R U : Λ(x) 0} Disadvantages of existing approaches Problem: SDP representation roughly squares the number of variables. Solution: We solve the SOS and moment problems in their original space. Problem: Standard basis choices lead to ill-conditioning. Solution: We use orthogonal polynomials and interpolation bases.

10 INTERIOR-POINT METHODS FOR CONIC PROGRAMMING Primal-dual pair of conic programs: min c x max b y s.t. Ax = b x K s.t. A y + s = c s K K : closed, convex, pointed cone with nonempty interior Examples: K = R n +, S n +, SOS, P Interior-point methods make use of self-concordant barriers (SCBs). Examples: K = R n +: F (x) = log x K = S n +: F(X) = log det X

11 INTERIOR-POINT METHODS FOR CONIC PROGRAMMING Given SCBs F and F, IPMs converge to the optimal solution by solving a sequence of equality-constrained barrier problems for µ 0: min s.t. Primal IPMs: c x + µf(x) Ax = b solve only the primal barrier problem, are not considered to be practical. Primal-dual IPMs: max s.t. b y µf (s) A y + s = c solve both the primal and dual barrier problems simultaneously, are preferred in practice.

12 INTERIOR-POINT METHODS FOR CONIC PROGRAMMING In principle, any closed convex cone admits a SCB (Nesterov and Nemirovski, 1994). However, the success of the IPM approach depends on the availability of a SCB whose gradient and Hessian can be computed efficiently. For the cone P, there are complexity-based reasons for suspecting that there are no computationally tractable SCBs. For the cone SOS, there is evidence suggesting that there may not exist any tractable SCBs. On the other hand, the cone SOS inherits a tractable SCB from the PSD cone.

13 INTERIOR-POINT METHOD OF SKAJAA AND YE Until recently: The practical success of primal-dual IPMs had been limited to optimization over symmetric cones: LP, SOCP, SDP. Existing primal-dual IPMs for non-symmetric conic programs required both the primal and dual SCBs (e.g., Nesterov, Todd, and Ye, 1999; Nesterov, 2012). Skajaa and Ye (2015) proposed a primal-dual IPM for non-symmetric conic programs which requires a SCB for only the primal cone, achieves the best-known iteration complexity.

14 SOLVING SOS PROGRAMS WITH INTERIOR-POINT METHODS Using Skajaa and Ye s IPM with the SCB for SOS, the SOS and moment problems can be solved without recourse to SDP. For any ɛ (0, 1), the algorithm finds a primal-dual solution that has ɛ times the duality gap of an initial solution in O( L log(1/ɛ)) iterations where L = dim(r[x] d ). Each iteration of the IPM requires the computation of the Hessian of the SCB for SOS, the solution of a Newton system. Solving the Newton system requires O(U 3 ) operations where U = dim(r[z] 2d ).

15 SOLVING SOS PROGRAMS WITH INTERIOR-POINT METHODS The choice of bases {p l } L l=1 and {q u} U u=1 for R[z] d and R[z] 2d has a significant effect on how efficiently the Newton system can be compiled. In general, computing the Hessian requires O(L 2 U 2 ) operations. If both bases are chosen to be monomial bases, the Hessian can be computed faster but requires specialized methods such as FFT and the inversion of Hankel-like matrices. Following Löfberg and Parrilo (2004), we choose {q u} U u=1: Lagrange interpolating polynomials, {p l } L l=1: orthogonal polynomials. With this choice, the Hessian can be computed in O(LU 2 ) operations.

16 SOLVING SOS PROGRAMS WITH INTERIOR-POINT METHODS Putting everything together: The algorithm runs in O( L log(1/ɛ)) iterations. At each iteration: Computing the Hessian requires O(LU 2 ) operations, Solving the Newton system requires O(U 3 ) operations. Overall complexity: O(U 3 L log(1/ɛ)) operations. This matches the best-known complexity bounds for LP! In contrast: Solving the SDP formulation with a primal-dual IPM requires O(L 6.5 log(1/ɛ)) operations. For fixed n: L 2 U = Θ(d n ).

17 SOLVING SOS PROGRAMS WITH INTERIOR-POINT METHODS The conditioning of the moment problem is directly related to the conditioning of the interpolation problem with {p k p l } L k,l=1. Good interpolation nodes are understood well only in few low-dimensional domains: Chebyshev points in [ 1, 1], Padua points in [ 1, 1] 2 (Caliari et al., 2005). For problems in higher dimensions, we follow a heuristic approach to choose interpolation nodes.

18 A TOY EXAMPLE Minimizing the Rosenbrock function on the square: min (z 1 1) (z 2 z 2 z R 2 1 ) 2 s.t. z [ 1, 1] 2 We can obtain a lower bound on the optimal value of this problem from the moment relaxation of order r. For r = 60: The moment relaxation has 1891 variables. The SDP representation requires one and two matrix inequalities. Sedumi quits with numerical errors after 948 seconds. Primal infeasibility: Our implementation solves the moment relaxation in 482 seconds.

19 FINAL REMARKS We have shown that SOS programs can be solved using a primal-dual IPM in O(U 3 L log(1/ɛ)) operations where L = ( ) ( n+d n and U = n+2d ) n. This improves upon the standard SDP-based approach which requires O(L 6.5 log(1/ɛ)) operations. In progress: Numerical stability. For multivariate problems, the conditioning of the problem formulation becomes important. How do we choose interpolation nodes for better-conditioned problems? Thank you!

20 REFERENCES M. Caliari, S. De Marchi, and M. Vianello. Bivariate polynomial interpolation on the square at new nodal sets. Applied Mathematics and Computation, 165: , J. Löfberg and P. A. Parrilo. From coefficients to samples: a new approach to sos optimization. In 43rd IEEE Conference on Decision and Control, volume 3, pages IEEE, Yu. Nesterov. Squared functional systems and optimization problems. In H. Frenk, K. Roos, T. Terlaky, and S. Zhang, editors, High Performance Optimization, volume 33 of Applied Optimization, chapter 17, pages Springer, Boston, MA, Yu. Nesterov. Towards non-symmetric conic optimization. Optimization Methods & Software, 27(4-5): , 2012.

21 REFERENCES Yu. Nesterov and A. Nemirovski. Interior-Point Polynomial Algorithms in Convex Programming, volume 13 of SIAM Studies in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, Yu. Nesterov, M. J. Todd, and Y. Ye. Infeasible-start primal-dual methods and infeasibility detectors for nonlinear programming problems. Mathematical Programming, 84: , A. Skajaa and Y. Ye. A homogeneous interior-point algorithm for nonsymmetric convex conic optimization. Mathematical Programming Ser. A, 150: , 2015.