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We consider linear optimization over a fixed compact convex feasible region that is semi-algebraic (or, more generally, tame). Generically, we prove that the optimal solution is unique and lies on a unique manifold, around which the feasible region is partly smooth, ensuring finite identification of the manifold by many optimization algorithms. Furthermore, second-order optimality conditions hold, guaranteeing smooth behavior of the optimal solution under small perturbations to the objective.
In this paper we study second-order optimality conditions for non-convex set-constrained optimization problems. For a convex set-constrained optimization problem, it is well-known that second-order optimality conditions involve the support function o
This paper provides an $H_2$ optimal scheme for reducing diffusively coupled second-order systems evolving over undirected networks. The aim is to find a reduced-order model that not only approximates the input-output mapping of the original system b
We describe an active-set method for the minimization of an objective function $phi$ that is the sum of a smooth convex function and an $ell_1$-regularization term. A distinctive feature of the method is the way in which active-set identification and
We maximize the production of biogas in a gradostat at steady state. The physical decision variables are the water, substrate, and biomass entering each tank and the flows through the interconnecting pipes. Our main technical focus is the nonconvex c
We design an algorithm which finds an $epsilon$-approximate stationary point (with $| abla F(x)|le epsilon$) using $O(epsilon^{-3})$ stochastic gradient and Hessian-vector products, matching guarantees that were previously available only under a stro