arXiv subjects

Ali Mohammad-Nezhad

Publications and source records attributed to Ali Mohammad-Nezhad.

7 recordsLinked to original sources

arXiv2026

On the convergence of critical points on real algebraic sets and applications to optimization

Let $F \in \R[X_1,\ldots,X_n]$ and the zero set $V=\zero(\mathcal{P},\R^n)$, where $\mathcal{P}:=\{P_1,\ldots,P_s\} \subset \R[X_1,\ldots,X_n]$ is a finite set of polynomials. We investigate existence of critical points of $F$ on an infinitesimal perturbation $V_ξ = \zero(\{P_1-ξ_1,\ldots,P_s-ξ_s\},\R\la ξ\ra^n)$. Our main motivation is to understand the limiting behavior of local minimizers of the log-barrier function (and central paths) in polynomial optimization, whose existence plays a fundamental role, in theory and practice, for modern interior point methods. We establish different sets of conditions that ensure existence, finiteness, boundedness, and non-degeneracy of critical points of $F$ on $V_ξ$, respectively. These lead to new conditions for the existence, convergence, and smoothness of central paths of polynomial optimization and its extension to non-linear optimization problems involving definable sets and functions in an o-minimal structure. In particular, for non-linear programs defined by real globally analytic functions, our extension provides a stronger form of the convergence result obtained by Drummond and Peterzil.

math.AG↗

arXiv2023

On the complexity of analyticity in semi-definite optimization

It is well-known that the central path of semi-definite optimization, unlike linear optimization, has no analytic extension to $μ= 0$ in the absence of the strict complementarity condition. In this paper, we show the existence of a positive integer $ρ$ by which the reparametrization $μ\mapsto μ^ρ$ recovers the analyticity of the central path at $μ= 0$. We investigate the complexity of computing $ρ$ using algorithmic real algebraic geometry and the theory of complex algebraic curves. We prove that the optimal $ρ$ is bounded by $2^{O(m^2+n^2m+n^4)}$, where $n$ is the matrix size and $m$ is the number of affine constraints. Our approach leads to a symbolic algorithm, based on the Newton-Puiseux algorithm, which computes a feasible $ρ$ using $2^{O(m+n^2)}$ arithmetic operations.

math.AG↗

arXiv2022

Improved effective Łojasiewicz inequality and applications

Let $\mathrm{R}$ be a real closed field. Given a closed and bounded semi-algebraic set $A \subset \mathrm{R}^n$ and semi-algebraic continuous functions $f,g:A \rightarrow \mathrm{R}$, such that $f^{-1}(0) \subset g^{-1}(0)$, there exist $N$ and $c \in \mathrm{R}$, such that the inequality (Łojasiewicz inequality) $|g(x)|^N \le c \cdot |f(x)|$ holds for all $x \in A$. In this paper we consider the case when $A$ is defined by a quantifier-free formula with atoms of the form $P = 0, P >0, P \in \mathcal{P}$ for some finite subset of polynomials $\mathcal{P} \subset \mathrm{R}[X_1,\ldots,X_n]_{\leq d}$, and the graphs of $f,g$ are also defined by quantifier-free formulas with atoms of the form $Q = 0, Q >0, Q \in \mathcal{Q}$, for some finite set $\mathcal{Q} \subset \mathrm{R}[X_1,\ldots,X_n,Y]_{\leq d}$. We prove that the Łojasiewicz exponent $N$ in this case is bounded by $(8 d)^{2(n+7)}$. Our bound depends on $d$ and $n$, but is independent of the combinatorial parameters, namely the cardinalities of $\mathcal{P}$ and $\mathcal{Q}$. As a consequence we improve the current best error bounds for polynomial systems under some conditions. Finally, as an abstraction of the notion of independence of the Łojasiewicz exponent from the combinatorial parameters occurring in the descriptions of the given pair of functions, we prove a version of Łojasiewicz inequality in polynomially bounded o-minimal structures. We prove the existence of a common Łojasiewicz exponent for certain combinatorially defined infinite (but not necessarily definable) families of pairs of functions.

math.AG↗

arXiv2021

On computing the nonlinearity interval in parametric semidefinite optimization

This paper revisits the parametric analysis of semidefinite optimization problems with respect to the perturbation of the objective function along a fixed direction. We review the notions of invariancy set, nonlinearity interval, and transition point of the optimal partition, and we investigate their characterizations. We show that the set of transition points is finite and the continuity of the optimal set mapping, on the basis of Painlevé-Kuratowski set convergence, might fail on a nonlinearity interval. Under a local nonsingularity condition, we then develop a methodology, stemming from numerical algebraic geometry, to efficiently compute nonlinearity intervals and transition points of the optimal partition. Finally, we support the theoretical results by applying our procedure to some numerical examples.

math.OC↗

arXiv2021

On the central path of semidefinite optimization: Degree and worst-case convergence rate

In this paper, we investigate the complexity of the central path of semidefinite optimization through the lens of real algebraic geometry. To that end, we propose an algorithm to compute real univariate representations describing the central path and its limit point, where the limit point is described by taking the limit of central solutions, as bounded points in the field of algebraic Puiseux series. As a result, we derive an upper bound $2^{O(m+n^2)}$ on the degree of the Zariski closure of the central path, when $μ$ is sufficiently small, and for the complexity of describing the limit point, where $m$ and $n$ denote the number of affine constraints and size of the symmetric matrix, respectively. Furthermore, by the application of the quantifier elimination to the real univariate representations, we provide a lower bound $1/γ$, with $γ=2^{O(m+n^2)}$, on the convergence rate of the central path.

math.AG↗

arXiv2021

On the sensitivity of the optimal partition for parametric second-order conic optimization

In this paper, using an optimal partition approach, we study the parametric analysis of a second-order conic optimization problem, where the objective function is perturbed along a fixed direction. We characterize the notions of so-called invariancy set and nonlinearity interval, which serve as stability regions of the optimal partition. We then propose, under the strict complementarity condition, an iterative procedure to compute a nonlinearity interval of the optimal partition. Furthermore, under primal and dual nondegeneracy conditions, we show that a boundary point of a nonlinearity interval can be numerically identified from a nonlinear reformulation of the parametric second-order conic optimization problem. Our theoretical results are supported by numerical experiments.

math.OC↗

arXiv2019

Parametric analysis of semidefinite optimization

In this paper, we study parametric analysis of semidefinite optimization problems w.r.t. the perturbation of the objective function. We study the behavior of the optimal partition and optimal set mapping on a so-called nonlinearity interval. Furthermore, we investigate the sensitivity of the approximation of the optimal partition in a nonlinearity interval, which has been recently studied by Mohammad-Nezhad and Terlaky. The approximation of the optimal partition was obtained from a bounded sequence of interior solutions on, or in a neighborhood of the central path. We derive an upper bound on the distance between the approximations of the optimal partitions of the original and perturbed problems. Finally, we examine the theoretical bounds by way of experimentation.

math.OC↗