We study first-order optimality for constrained composite optimization problems whose objective is the sum of a locally Lipschitz function and a proper lower semicontinuous function. We focus on the case in which the limiting subdifferential of the latter function is empty at a point of interest, so that the usual KKT conditions are unavailable. We … Read more
Stochastic Gradient Descent (\(\textsf{SGD}\)) is one of the most classical optimization algorithms with favorable theoretical guarantees, yet its practical implementation differs subtly from its well-known form and is often referred to as Shuffling Stochastic Gradient Descent (\(\textsf{Shuffling SGD}\)). A particularly popular strategy in \(\textsf{Shuffling SGD}\) is Random Reshuffling (\(\textsf{RR}\)), which has achieved great empirical success. … Read more
We consider the problem of minimizing a sparse quadratic function over the unit hypercube. In binary quadratic programming, treewidth of the interaction graph is a central parameter for tractability: bounded treewidth yields polynomial-time solvability. Motivated by this fact, we investigate whether treewidth plays a similar role when the binary domain is replaced by the unit … Read more
This work studies how a generalized error bound (GEB) property in the ambient Euclidean space transfers to the active manifold under \(\mathcal{C}^1\) partial smoothness. The GEB generalizes the standard error bound, which upper bounds the distance to a subset of critical points using first-order stationarity residuals, by allowing the distance to be composed with a … Read more
We consider the Lorentz positive cone of \(n \times m\) matrices that map the Lorentz cone in \(R^m\) into the Lorentz cone in \(R^n\). The Lorentz positive cone and its dual, the cone of Lorentz separable matrices, are shown to provide polynomial-time algorithms for the problem of minimizing a bilinear objective over variables contained in … Read more
In this paper, we propose and analyze an augmented Lagrangian framework for solving stochastic nonconvex optimization problems with expectation-based equality constraints over a closed and convex constraint set. The framework generates a sequence of nonconvex primal subproblems, which are solved inexactly using stochastic second-order methods. We establish iteration complexity results for obtaining approximate second-order stationary … Read more
This work examines three different concepts of objective redundancy in multiobjective optimization. Multiobjective optimization is known to suffer from the curse of dimensionality and we aim at reducing the number of objective functions which need to be considered. In this context, a set of objectives is called redundant if the sets of weakly efficient, efficient, … Read more
We propose a novel branch‑and‑bound algorithm that constructs rigorous outer approximations of the optimal point mapping for parametric optimization problems with guaranteed feasibility and optimality tolerances. The method uses the improvement‑function reformulation to define discarding and inclusion tests on sub-boxes, constructing a rigorous outer approximation. Under the same regularity conditions that ensure exactness of this … Read more
We revisit the framework of generalized Benders decomposition over a compact but non-finite master domain. We show by counterexample that strong general dual functions alone may fail to guarantee convergence. We then define a condition of uniform local strongness and prove that strong general dual functions satisfying this condition guarantee finite \(\epsilon\)-termination. Finally, we show … Read more
We present a Julia computational toolbox for linear optimization problems with joint affine chance constraints under elliptically symmetric uncertainty. The toolbox combines a spherical–radial oracle for estimating the joint probability and its gradient with three structured optimization methods: Proximal, Feasible, and Penalty. The oracle supports several elliptically symmetric distributions and is integrated with these methods … Read more