First Order Conditions for Semidefinite Representations of Convex Sets Defined by Rational or Singular Polynomials
Jiawang Nie

TL;DR
This paper establishes conditions under which convex sets defined by rational or singular polynomials can be represented as semidefinite programs, expanding the understanding of SDP representability for complex polynomial-defined sets.
Contribution
It provides explicit semidefinite representation conditions for convex sets defined by rational or singular polynomials, using Positivstellensatz certificates and perspective transformations.
Findings
Semidefinite representations are possible under preordering and q-module concavity conditions.
Explicit SDP representations are constructed for sets with boundary singularities using perspective transformations.
In two dimensions, sets with certain Laurent expansion properties always admit explicit SDP representations.
Abstract
A set is called semidefinite representable or semidefinite programming (SDP) representable if it can be represented as the projection of a higher dimensional set which is represented by some Linear Matrix Inequality (LMI). This paper discuss the semidefinite representability conditions for convex sets of the form S_D(f) = {x \in D: f(x) >= 0}. Here D={x\in R^n: g_1(x) >= 0, ..., g_m(x) >= 0} is a convex domain defined by some "nice" concave polynomials g_i(x) (they satisfy certain concavity certificates), and f(x) is a polynomial or rational function. When f(x) is concave over \mc{D}, we prove that S_D(f) has some explicit semidefinite representations under certain conditions called preordering concavity or q-module concavity, which are based on the Positivstellensatz certificates for the first order concavity criteria. When f(x) is a polynomial or rational function having singularities…
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Taxonomy
TopicsAdvanced Optimization Algorithms Research · Polynomial and algebraic computation · Matrix Theory and Algorithms
