The integer quadratic moment hull is the closed convex hull of the pairs \((z,zz^{\top})\) with \(z\in\mathbb{Z}^n\). We prove that one round of Chvátal–Gomory (CG) cuts applied to its basic semidefinite relaxation recovers this hull exactly if and only if \(n\le6\). Our approach identifies its facets with perfect Delaunay polyhedra and characterizes their recovery through depth, … Read more
Linear inequalities can generate large constraint systems in semidefinite relaxations of binary quadratic programs, making the quadratic subproblems of splitting methods expensive to solve. We develop a box-cone representation of bound-factor reformulation-linearization technique (RLT) constraints in semidefinite relaxations of binary quadratic programs. These constraints arise by multiplying each original linear inequality by every binary variable … Read more
In a predictor-corrector arc-search method, a point on the predictor arc is first selected and a corrector is then computed at that point. Since the Karush-Kuhn-Tucker (KKT) matrix changes with the selected point, this correction may require a second factorization in each iteration. We propose a one-factorization arc-search method (OFAS) for semidefinite programming (SDP) that … Read more
We show that unless \(\mathrm P=\mathrm{NP}\), there cannot be a polynomial-time (or even pseudo-polynomial-time) algorithm for output feedback stabilization of a linear dynamical system with a linear controller. This settles one of the best-known open problems in control theory. The result holds in both continuous and discrete time. We also present a family of stabilizable … Read more
State-of-the-art exact methods for the Maximum Cut problem are based on solving linear and semidefinite programming relaxations embedded into a branch-and-bound algorithm. For linear programming formulations, it was shown recently that it is thereby sufficient to enforce the integrality of the variables associated with the edges of a spanning tree. Our first contribution is to … Read more
We study the convergence rate of the moment-SOS (sum-of-squares) hierarchy for polynomial optimization problems (POPs) on a bounded subset of the real line described by arbitrary polynomial inequalities. We prove that, for every fixed univariate POP, the relaxation error is bounded by $O(1/r^2)$, where $r$ is the relaxation order. In particular, boundary degeneracies in the … Read more
Singularity degree is an important measure of semidefinite programming (SDP) degeneracy, but it is generally unavailable a priori from the problem data. We augment the Shor relaxation of binary sets \(\{x\in\{0,1\}^n:Ax=b\}\) with the first-level Reformulation–Linearization Technique (RLT) equations generated by the defining linear equalities. For the resulting equality-generated SDP–RLT relaxation, we determine the exact worst-case … Read more
We systematically investigate the potentials of improving the lower bound obtained with a linear relaxation of the Quadratic Unconstrained Binary Optimization problem by integrating curvature information from an accompanying quadratic convex underestimator via gradient inequalities. On the one hand, we exemplify to which extent this hybrid approach may provide a lower bound that is strictly … Read more
We consider the design of optimal fixed-step first-order methods for $M$-Lipschitz convex optimization given $\|x_0-x_\star\|\leq D$. Prior works have identified several distinct fixed-step methods, parameterized by a matrix of stepsizes $W$, with the (information-theoretic) minimax optimal rate $MD/\sqrt{N+1}$ of objective gap convergence. We provide a complete characterization of every optimal fixed-step method. Moreover, we show … Read more
The stable set problem admits various binary linear and quadratic formulations. The Shor relaxation of a particular quadratic formulation is the well-known theta body. We consider aggregations of quadratic constraints of this formulation, yielding exact and inexact quadratic formulations of the stable set problem, and then establish conditions under which the aggregated quadratic formulation is … Read more