In this paper, we study the discrete complex quadratic optimization problem, where the goal is to maximize a multivariate Hermitian quadratic objective function over complex m’th roots of unity. This problem is a generalization of the well-studied quadratic unconstrained binary optimization problem (QUBO), and has applications in signal processing, including in maximal likelihood estimation and multiple-input multiple-output detection, in addition to applications in statistical physics, in particular the computation of ground states of the Potts model. We study the sum-of-squares hierarchy for this problem, which is a sequence of semidefinite relaxations that is known to converge to the optimal solution after at most n steps. A previous result of Fawzi, Saunderson and Parrilo showed that in the the binary case (
m=2 ), at most
⌈n/2⌉ levels of the hierarchy are actually needed. In this paper we generalize this result and establish that the hierarchy converges after at most
⌊n/2⌋+1 level for any
m≥2 . Our proof generalizes the ideas used in the
m=2 case, and is based on constructing chordal covers of appropriate Cayley graphs.
Computing the optimal success probability for transmitting classical messages through a single use of a quantum channel is NP-hard, even for two messages. An existing semidefinite programming hierarchy based on symmetric extensions provides convergent upper bounds with an a priori error estimate that decays as the inverse square root of the extension level. In this work, we construct a Hermitian sum-of-squares hierarchy for an arbitrary number of messages and prove quadratic convergence in its level. The error bound is proportional to the advantage over random guessing. Our approach combines state-discrimination duality with positive polynomial kernels on products of spheres to construct feasible polynomial dual certificates. For binary messages, the resulting bounds give a multiplicative approximation from above of the trace-norm contraction coefficient.
Abstract We consider the problem of maximizing a homogeneous polynomial on the unit sphere and its hierarchy of sum-of-squares relaxations. Exploiting the polynomial kernel technique, we obtain a quadratic improvement of the known convergence rate by Reznick and Doherty and Wehner. Specifically, we show that the rate of convergence is no worse than
O(d2/ℓ2) O ( d 2 / ℓ 2 ) in the regime
ℓ=Ω(d) ℓ = Ω ( d ) where
ℓ ℓ is the level of the hierarchy and d the dimension, solving a problem left open in the recent paper by de Klerk and Laurent ( arXiv:1904.08828 ). Importantly, our analysis also works for matrix-valued polynomials on the sphere which has applications in quantum information for the Best Separable State problem. By exploiting the duality relation between sums of squares and the Doherty–Parrilo–Spedalieri hierarchy in quantum information theory, we show that our result generalizes to nonquadratic polynomials the convergence rates of Navascués, Owari and Plenio.
We study the problem of maximizing a complex-valued quadratic form over the
Kth roots of unity. We show that when the objective matrix
Q⋆∈Cn×n of the quadratic has rank
r, the global maximizer belongs to a candidate set of size
O(rn2r−1). This set can be constructed deterministically in
O(rn2r+1) time by enumerating the vertices of a hyperplane arrangement in
R2r. The algorithm is embarrassingly parallel; with~
P processors, the time complexity drops to
O(rn2r+1/P). For approximately low-rank settings, where the objective matrix is a noise-perturbed variant of a rank-
r matrix, we prove that applying our framework to a spectral truncation yields a multiplicative
(1−O(∥H∥2/δ⋆))-approximation guarantee, where
δ⋆ denotes the eigengap of the underlying rank-
r matrix and
H represents the perturbation. To scale to high-dimensional problems, we establish a randomized sampling variant. We prove that uniformly sampling
S≥O(1/εr−1) candidates achieves a
(1−ε)cos2(π/K)-approximation of the optimal rank-
r solution with high probability. Crucially, this sample size is entirely independent of
n, reducing the overall runtime to
O(S⋅n2). Computational experiments on synthetic benchmarks and large-scale graphs for \textsc{Max-3-Cut} confirm that our algorithms match or exceed semi-definite programming solution quality on structured instances while enabling massive parallelization across heterogeneous hardware and scaling seamlessly to problems where
n≥106.
We consider problem (QP) of minimizing a quadratic function subject to linear or quadratic constraints. Variables are integer and bounded. This very general problem can model many classical problems in Combinatorial Optimization.A major difference between (QP) and integer linear programs lies in the fact that, in general, its continuous relaxation is an NP-hard optimization problem. To overcome this difficulty, the Convex Quadratic Reformulation approach transforms (QP) into a problem (QP'), equivalent to (QP), but whose continuous relaxation is a convex problem. To compute a global optimal solution to (QP) it becomes possible to solve (QP') by an implicit enumeration algorithm based on continuous convex optimization.We make an overview of recent developments in this approach. We show in particular how positive semidefinite relaxations can be used to build the most interesting equivalent problems (QP'). In the case of binary variables, we give a vision of classical linearization as a special case of Convex Quadratic Reformulation.Finally, we illustrate the generality and the computational efficiency of the Quadratic Convex Reformulation approach for several combinatorial optimization problems.
The Quadratic Unconstrained Binary Optimization problem (QUBO) is the optimization of a quadratic function where the variables are binary; it is frequently used to model combinatorial problems in operational research and graph theory.QUBO is an NP-Complete problem and a challenge to exact methods.Therefore, the problem has been approached with heuristics.Here, we propose new ideas to solve QUBO with exact methods.We develop a new quadratic formulation that maps the binary problem into an equivalent continuous optimization problem with a single quadratic constraint.A further development delves into Lagrangian inspired concepts to obtain an equivalent unconstrained formulation.The case studies illustrate the benefit and limitations of both formulations.Also, the computational experiments show that problems with up to 2500 variables were solved with processing times under 500 seconds to achieve an average gap under 1%; these results also show that the approach allowed to solve, with an exact method, instances of 1500 more binary variables than the instances considered in previous papers.