activity
20172023
most citedA parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems

27 citations · 34 across the 6 of their papers we have counts for

collaborators

6 papers

math.OC2023★ 1 cited

A novel dual-decomposition method for non-convex two-stage stochastic mixed-integer quadratically constrained quadratic problems

Nikita Belyak, Fabricio Oliveira

We propose the novel p-branch-and-bound method for solving two-stage stochastic programming problems whose deterministic equivalents are represented by non-convex mixed-integer qua…

math.SP2021

Criteria for the Absolutely Continuous Spectral Components of matrix-valued Jacobi operators

Fabricio Vieira Oliveira, Silas L. Carvalho

We extend in this work the Jitomirskaya-Last inequality and Last-Simoncriterion for the absolutely continuous spectral component of a half-line Schrödinger operator to the special…

math-ph2021★ 1 cited

Kotani Theory for ergodic matrix-like Jacobi operators

Fabrício Vieira Oliveira, Silas L. Carvalho

We extend the so-called Kotani Theory for a particular class of ergodic matrix-like Jacobi operators defined in by the law $[H_ω \textbf{u}]_{n}…

math.OC2017

Combining Progressive Hedging with a Frank-Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming

Natashia Boland, Jeffrey Christiansen, Brian Dandurand +3

We present a new primal-dual algorithm for computing the value of the Lagrangian dual of a stochastic mixed-integer program (SMIP) formed by relaxing its nonanticipativity constrai…

math.OC2017★ 27 cited

A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems

Brian Dandurand, Natashia Boland, Jeffrey Christiansen +2

We contribute improvements to a Lagrangian dual solution approach applied to large-scale optimization problems whose objective functions are convex, continuously differentiable and…

math.OC2017★ 5 cited

Combining Penalty-based and Gauss-Seidel Methods for solving Stochastic Mixed-Integer Problems

Fabricio Oliveira, Jeffrey Christiansen, Brian Dandurand +1

In this paper, we propose a novel decomposition approach for mixed-integer stochastic programming (SMIP) problems that is inspired by the combination of penalty-based Lagrangian an…