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A new polynomially solvable class of quadratic optimization problems with box constraints

Milan Hladík, Michal Černý, Miroslav Rada

TL;DR

The paper proves that the quadratic maximization problem over a box with a fixed-rank matrix $Q$ can be solved in polynomial time, even for arbitrary $Q$ and linear term $q^T x$, by reducing the problem to enumeration of faces of a zonotope in dimension $2r$, where $r=\mathrm{rank}(Q)=O(1)$. The method hides the linear term, performs a dimension reduction via a rank factorization $Q=U^T V$, and reformulates the problem as a maximization of a quadratic form over a zonotope, leveraging the duality between zonotopes and hyperplane arrangements to enumerate faces efficiently. For each face, a small linear program yields a stationary point, and the maximum over all faces is the solution; the approach scales as $O(n^{2r-1})$ face enumerations and polynomial-time LPs per face when $q=0$ (with an additional factor when $q\neq 0$). A key contribution is extending polynomial-time solvability beyond PSD $Q$ and absent linear terms, providing a larger class of tractable instances and insights into low-rank representations of quadratic forms. The results are memory-intensive due to storing the face lattice but establish a clear, constructive route to polynomial solvability for fixed-rank cases with potential impacts on related binary/psd settings.

Abstract

We consider the quadratic optimization problem $\max_{x \in C}\ x^T Q x + q^T x$, where $C\subseteq\mathbb{R}^n$ is a box and $r := \mathrm{rank}(Q)$ is assumed to be $\mathcal{O}(1)$ (i.e., fixed). We show that this case can be solved in polynomial time for an arbitrary $Q$ and $q$. The idea is based on a reduction of the problem to enumeration of faces of a certain zonotope in dimension $O(r)$. This paper generalizes previous results where $Q$ had been assumed to be positive semidefinite and no linear term was allowed in the objective function. Positive definiteness was a strong restriction and it is now relaxed. Generally, the problem is NP-hard; this paper describes a new polynomially solvable class of instances, larger than those known previously.

A new polynomially solvable class of quadratic optimization problems with box constraints

TL;DR

The paper proves that the quadratic maximization problem over a box with a fixed-rank matrix can be solved in polynomial time, even for arbitrary and linear term , by reducing the problem to enumeration of faces of a zonotope in dimension , where . The method hides the linear term, performs a dimension reduction via a rank factorization , and reformulates the problem as a maximization of a quadratic form over a zonotope, leveraging the duality between zonotopes and hyperplane arrangements to enumerate faces efficiently. For each face, a small linear program yields a stationary point, and the maximum over all faces is the solution; the approach scales as face enumerations and polynomial-time LPs per face when (with an additional factor when ). A key contribution is extending polynomial-time solvability beyond PSD and absent linear terms, providing a larger class of tractable instances and insights into low-rank representations of quadratic forms. The results are memory-intensive due to storing the face lattice but establish a clear, constructive route to polynomial solvability for fixed-rank cases with potential impacts on related binary/psd settings.

Abstract

We consider the quadratic optimization problem , where is a box and is assumed to be (i.e., fixed). We show that this case can be solved in polynomial time for an arbitrary and . The idea is based on a reduction of the problem to enumeration of faces of a certain zonotope in dimension . This paper generalizes previous results where had been assumed to be positive semidefinite and no linear term was allowed in the objective function. Positive definiteness was a strong restriction and it is now relaxed. Generally, the problem is NP-hard; this paper describes a new polynomially solvable class of instances, larger than those known previously.

Paper Structure

This paper contains 13 sections, 3 theorems, 23 equations, 1 algorithm.

Key Result

Theorem 1

If $q=0$, alg:algorithm works in time $O(n^{2r-1}\cdot \text{lp}(Q,\underline{x},\overline{x}))$, where $\text{lp}(Q,\underline{x},\overline{x})$ is time needed to solve a linear program with $O(n)$ variables, $O(n)$ constraints and data with bit-size polynomially bounded by the bit-sizes of $Q,\und

Theorems & Definitions (6)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof