The quadratic assignment problem (QAP) has considered one of the most significant
combinatorial optimization problems due to its variant and significant applications in real life …

Inverse problems correspond to a certain type of optimization problems formulated over
appropriate input distributions. Recently, there has been a growing interest in understanding …

Semidefinite programming (SDP) is a powerful framework from convex optimization that has
striking potential for data science applications. This paper develops a provably correct …

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex
problems with nonlinear constraints. We characterize the total computational complexity of …

In this paper, we show that for a class of linearly constrained convex composite optimization
problems, an (inexact) symmetric Gauss–Seidel based majorized multi-block proximal …

For a symmetric positive semidefinite linear system of equations Q x= b Q x= b, where
x=(x_1, ..., x_s) x=(x 1,…, xs) is partitioned into s blocks, with s ≥ 2 s≥ 2, we show that each …

Optimizing expensive to evaluate black-box functions over an input space consisting of all
permutations of d objects is an important problem with many real-world applications. For …

In this work we study convex relaxations of quadratic optimisation problems over
permutation matrices. While existing semidefinite programming approaches can achieve …

Modeling unknown systems from data is a precursor of system optimization and sequential
decision making. In this paper, we focus on learning a Markov model from a single trajectory …

In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the
quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is …