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Global polynomial optimization methods typically rely on compactness of the feasible region in order to find solutions. These methods can incur considerable computational expense and most commercially available solvers do not verify the…

Optimization and Control · Mathematics 2026-05-12 Rohan Rele , Angelia Nedich

We consider systems of strict multivariate polynomial inequalities over the reals. All polynomial coefficients are parameters ranging over the reals, where for each coefficient we prescribe its sign. We are interested in the existence of…

Symbolic Computation · Computer Science 2018-09-06 Hoon Hong , Thomas Sturm

One can reduce the problem of proving that a polynomial is nonnegative, or more generally of proving that a system of polynomial inequalities has no solutions, to finding polynomials that are sums of squares of polynomials and satisfy some…

Logic · Mathematics 2011-07-01 David Monniaux , Pierre Corbineau

We study a class of polynomial optimization problems with a robust polynomial matrix inequality (PMI) constraint where the uncertainty set itself is defined also by a PMI. These can be viewed as matrix generalizations of semi-infinite…

Optimization and Control · Mathematics 2024-10-10 Feng Guo , Jie Wang

We derive some Positivstellensatz\"e for noncommutative rational expressions from the Positivstellensatz\"e for noncommutative polynomials. Specifically, we show that if a noncommutative rational expression is positive on a polynomially…

Functional Analysis · Mathematics 2017-03-22 J. E. Pascoe

A subgradient method is presented for solving general convex optimization problems, the main requirement being that a strictly-feasible point is known. A feasible sequence of iterates is generated, which converges to within user-specified…

Optimization and Control · Mathematics 2016-05-30 James Renegar

Semidefinite relaxations of polynomial optimization have become a central tool for addressing the non-convex optimization problems over non-commutative operators that are ubiquitous in quantum information theory and, more in general,…

Quantum Physics · Physics 2025-12-22 Younes Naceur , Jie Wang , Victor Magron , Antonio Acín

We develop a novel framework to study smooth and strongly convex optimization algorithms, both deterministic and stochastic. Focusing on quadratic functions we are able to examine optimization algorithms as a recursive application of linear…

Optimization and Control · Mathematics 2015-03-25 Yossi Arjevani , Shai Shalev-Shwartz , Ohad Shamir

In this paper we give a version of Krivine-Stengle's Positivstellensatz, Schweighofer's Positivstellensatz, Scheiderer's local-global principle, Scheiderer's Hessian criterion and Marshall's boundary Hessian conditions for polynomial…

Algebraic Geometry · Mathematics 2019-02-19 Công-Trình Lê

This paper introduces state polynomials, i.e., polynomials in noncommuting variables and formal states of their products. A state analog of Artin's solution to Hilbert's 17th problem is proved showing that state polynomials, positive over…

Functional Analysis · Mathematics 2024-12-04 Igor Klep , Victor Magron , Jurij Volčič , Jie Wang

We introduce a unified framework for computing approximately-optimal preconditioners for solving linear and non-linear systems of equations. We demonstrate that the condition number minimization problem, under structured transformations…

Optimization and Control · Mathematics 2025-12-09 M. Levent Doğan , Alperen Ergür , Elias Tsigaridas

We construct a convergent family of outer approximations for the problem of optimizing polynomial functions over convex bodies subject to polynomial constraints. This is achieved by generalizing the polarization hierarchy, which has…

Optimization and Control · Mathematics 2024-06-17 Martin Plávala , Laurens T. Ligthart , David Gross

In this article we provide an experimental algorithm that in many cases gives us an upper bound of the global infimum of a real polynomial on $\R^{n}$. It is very well known that to find the global infimum of a real polynomial on $\R^{n}$,…

Optimization and Control · Mathematics 2018-09-25 María López Quijorna

This paper studies first-order algorithms for solving fully composite optimization problems over convex and compact sets. We leverage the structure of the objective by handling its differentiable and non-differentiable components…

Optimization and Control · Mathematics 2023-07-13 Maria-Luiza Vladarean , Nikita Doikov , Martin Jaggi , Nicolas Flammarion

Polynomial optimization problems represent a wide class of optimization problems, with a large number of real-world applications. Current approaches for polynomial optimization, such as the sum of squares (SOS) method, rely on large-scale…

Optimization and Control · Mathematics 2025-07-04 Dimitris Bertsimas , Dick den Hertog , Thodoris Koukouvinos

Recently, non-SOS Positivstellens\"atze for polynomials on compact semialgebraic sets, following the general form of Schm\"{u}dgen's Positivstellensatz, have been derived by appropriately replacing the SOS polynomials with other classes of…

Classical Analysis and ODEs · Mathematics 2021-10-20 Lorenz M. Roebers , Juan C. Vera , Luis F. Zuluaga

We consider positive solutions to parametrized systems of generalized polynomial equations (with real exponents and positive parameters). By a fundamental result obtained in parallel work, polynomial systems are determined by geometric…

Algebraic Geometry · Mathematics 2024-10-07 Stefan Müller , Georg Regensburger

Recently, the second and the third author developed sums of nonnegative circuit polynomials (SONC) as a new certificate of nonnegativity for real polynomials, which is independent of sums of squares. In this article we show that the SONC…

Algebraic Geometry · Mathematics 2017-03-20 Mareike Dressler , Sadik Iliman , Timo de Wolff

Positivstellensatz is a fundamental result in real algebraic geometry providing algebraic certificates for positivity of polynomials on semialgebraic sets. In this article Positivstellens\"atze for trace polynomials positive on…

Rings and Algebras · Mathematics 2019-01-23 Igor Klep , Špela Špenko , Jurij Volčič

We address composite optimization problems, which consist in minimizing the sum of a smooth and a merely lower semicontinuous function, without any convexity assumptions. Numerical solutions of these problems can be obtained by proximal…

Optimization and Control · Mathematics 2024-02-14 Alberto De Marchi