Derivative-free optimization methods

In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization. We provide a review and perspectives on developments in … Read more

On monotonic estimates of the norm of the minimizers of regularized quadratic functions in Krylov spaces

We show that the minimizers of regularized quadratic functions restricted to their natural Krylov spaces increase in Euclidean norm as the spaces expand. Citation Technical Report RAL-TR-2019-005, STFC-Rutherford Appleton Laboratory, Oxfordshire, England, April 5th 2019 Article Download View On monotonic estimates of the norm of the minimizers of regularized quadratic functions in Krylov spaces

The Quadratic Cycle Cover Problem: special cases and efficient bounds

The quadratic cycle cover problem is the problem of finding a set of node-disjoint cycles visiting all the nodes such that the total sum of interaction costs between incident arcs is minimized. In this paper we study the linearization problem for the quadratic cycle cover problem and related lower bounds. In particular, we derive various … Read more

Discrete Optimization Methods for Group Model Selection in Compressed Sensing

In this article we study the problem of signal recovery for group models. More precisely for a given set of groups, each containing a small subset of indices, and for given linear sketches of the true signal vector which is known to be group-sparse in the sense that its support is contained in the union … Read more

Logarithmic-Barrier Decomposition Interior-Point Methods for Stochastic Linear Optimization in a Hilbert Space

Several logarithmic-barrier interior-point methods are now available for solving two-stage stochastic optimization problems with recourse in the finite-dimensional setting. However, despite the genuine need for studying such methods in general spaces, there are no infinite-dimensional analogs of these methods. Inspired by this evident gap in the literature, in this paper, we propose logarithmic-barrier decomposition-based interior-point … Read more

Identifying Effective Scenarios for Sample Average Approximation

We introduce a method to improve the tractability of the well-known Sample Average Approximation (SAA) without compromising important theoretical properties, such as convergence in probability and the consistency of an independent and identically distributed (iid) sample. We consider each scenario as a polyhedron of the mix of first-stage and second-stage decision variables. According to John’s … Read more