activity
20132023
most citedQuantum Interior Point Methods for Semidefinite Optimization

24 citations · 78 across the 15 of their papers we have counts for

collaborators

17 papers

math.OC2023

On relaxations of the max -cut problem formulations

Ramin Fakhimi, Hamidreza Validi, Illya V. Hicks +2

A tight continuous relaxation is a crucial factor in solving mixed integer formulations of many NP-hard combinatorial optimization problems. The (weighted) max -cut problem is a…

quant-ph2023★ 2 cited

Solving the semidefinite relaxation of QUBOs in matrix multiplication time, and faster with a quantum computer

Brandon Augustino, Giacomo Nannicini, Tamás Terlaky +1

Recent works on quantum algorithms for solving semidefinite optimization (SDO) problems have leveraged a quantum-mechanical interpretation of positive semidefinite matrices to deve…

quant-ph2021★ 24 cited

Quantum Interior Point Methods for Semidefinite Optimization

Brandon Augustino, Giacomo Nannicini, Tamás Terlaky +1

We present two quantum interior point methods for semidefinite optimization problems, building on recent advances in quantum linear system algorithms. The first scheme, more simila…

math.OC2021

Linear convergence of the Douglas-Rachford algorithm via a generic error bound condition

Javier Peña, Juan C. Vera, Luis F. Zuluaga

We provide new insight into the convergence properties of the Douglas-Rachford algorithm for the problem , where and are convex functions. Our approac…

q-fin.PM2021★ 22 cited

Computing near-optimal Value-at-Risk portfolios using Integer Programming techniques

Onur Babat, Juan C. Vera, Luis F. Zuluaga

Value-at-Risk (VaR) is one of the main regulatory tools used for risk management purposes. However, it is difficult to compute optimal VaR portfolios; that is, an optimal risk-rewa…

econ.TH2021★ 2 cited

Revenue Adequate Prices for Chance-Constrained Electricity Markets with Variable Renewable Energy Sources

Xin Shi, Alberto J. Lamadrid L., Luis F. Zuluaga

In a commodity market, revenue adequate prices refer to compensations that ensure that a market participant has a non-negative profit. In this article, we study the problem of deri…

math.OC2019

Reducing non-negativity over general semialgebraic sets to non-negativity over simple sets

Olga Kuryatnikova, Juan C. Vera, Luis F. Zuluaga

A non-negativity certificate (NNC) is a way to write a polynomial so that its non-negativity on a semialgebraic set becomes evident. Positivstellensätze (Psätze) guarantee the exis…

math.OC2019

Equivalence and invariance of the chi and Hoffman constants of a matrix

Javier F. Pena, Juan C. Vera, Luis F. Zuluaga

We show that the following two condition measures of a full column rank matrix are identical: the chi constant and a signed Hoffman constant. This id…

math.OC2019

New characterizations of Hoffman constants for systems of linear constraints

Javier Pena, Juan Vera, Luis Zuluaga

We give a characterization of the Hoffman constant of a system of linear constraints in {\em relative} to a {\em reference polyhedron} . The reference polyhe…

math.OC2018★ 3 cited

An algorithm to compute the Hoffman constant of a system of linear constraints

Javier Pena, Juan Vera, Luis Zuluaga

We propose a combinatorial algorithm to compute the Hoffman constant of a system of linear equations and inequalities. The algorithm is based on a characterization of the Hoffman c…

math.OC2017★ 2 cited

Positive polynomials on unbounded domains

Javer Pena, Juan C. Vera, Luis F. Zuluaga

Certificates of non-negativity such as Putinar's Positivstellensatz have been used to obtain powerful numerical techniques to solve polynomial optimization (PO) problems. Putinar's…

math.OC2017

Pricing in non-convex markets with quadratic deliverability costs

Xiaolong Kuang, Alberto J. Lamadrid, Luis F. Zuluaga

The problem of obtaining market-clearing prices for markets with non-convexities has been widely studied in the literature. This is particularly the case in electricity markets, wh…

math.OC2016★ 7 cited

Completely positive and completely positive semidefinite tensor relaxations for polynomial optimization

Xiaolong Kuang, Luis F. Zuluaga

Completely positive (CP) tensors, which correspond to a generalization of CP matrices, allow to reformulate or approximate a general polynomial optimization problem (POP) with a co…

q-fin.PR2016

Computing semiparametric bounds on the expected payments of insurance instruments via column generation

Robert Howley, Robert Storer, Juan Vera +1

It has been recently shown that numerical semiparametric bounds on the expected payoff of fi- nancial or actuarial instruments can be computed using semidefinite programming. Howev…

math.OC2015★ 9 cited

Globally solving Non-Convex Quadratic Programs via Linear Integer Programming techniques

Wei Xia, Juan Vera, Luis F. Zuluaga

Quadratic programming (QP) is a well-studied fundamental NP-hard optimization problem which optimizes a quadratic objective over a set of linear constraints. In this paper, we refo…

math.OC2015★ 7 cited

Alternative SDP and SOCP Approximations for Polynomial Optimization

Xiaolong Kuang, Bissan Ghaddar, Joe Naoum-Sawaya +1

In theory, hierarchies of semidefinite programming (SDP) relaxations based on sum of squares (SOS) polynomials have been shown to provide arbitrarily close approximations for a gen…

cs.GT2013

Copula-based Randomized Mechanisms for Truthful Scheduling on Two Unrelated Machines

Xujin Chen, Donglei Du, Luis F. Zuluaga

We design a Copula-based generic randomized truthful mechanism for scheduling on two unrelated machines with approximation ratio within , offering an improved up…