On computing the nonlinearity interval in parametric semidefinite optimization

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 …

参数化半定规划：解轨迹的几何性质

Parametric Semidefinite Programming: Geometry of the Trajectory of Solutions

本文研究参数化半定规划中解轨迹的几何性质，证明在轨迹上给定点附近仅存在六种不同行为并通过示例加以说明。

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 interv…

Approximating Pareto Curves using Semidefinite Relaxations

We consider the problem of constructing an approximation of the Pareto curve associated with the multiobjective optimization problem $\min_{\mathbf{x} \in \mathbf{S}}\{ (f_1(\mathbf{x}), f_2(\mathbf{x})) \}$, where $f_1$ and $f_2$ are two conflicting polynomial criteria and $\mathbf{S} \subset \mathbb{R}^n$ is a compact basic semialgebraic set. We provide a systematic numerical scheme to approximate the Pareto curve. We start by reducing the initial problem into a scalarized polynomial optimization problem (POP). Three scalarization methods lead to consider different parametric POPs, namely (a…

S-SOS：面向参数化多项式优化的随机平方和

S-SOS: Stochastic Sum-Of-Squares for Parametric Polynomial Optimization

本文提出随机平方和（S-SOS）算法，通过构造半定规划层级求解随机参数化多项式优化的严格下界与全局极小点候选解，并利用 n 体先验诱导稀疏性，成功处理高达 40 个变量、矩阵规模超过 800×800 的传感器网络定位问题。

A "joint+marginal" approach to parametric polynomial optimization

Given a compact parameter set $Y\subset R^p$, we consider polynomial optimization problems $(P_y$) on $R^n$ whose description depends on the parameter $y\inY$. We assume that one can compute all moments of some probability measure $ϕ$ on $Y$, absolutely continuous with respect to the Lebesgue measure (e.g. $Y$ is a box or a simplex and $ϕ$ is uniformly distributed). We then provide a hierarchy of semidefinite relaxations whose associated sequence of optimal solutions converges to the moment vector of a probability measure that encodes all information about all global optimal solutions $x^*(y)$…