activity
20172020
most citedOptimal Approximation of Doubly Stochastic Matrices

3 citations · 7 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2020

SafePILCO: a software tool for safe and data-efficient policy synthesis

Kyriakos Polymenakos, Nikitas Rontsis, Alessandro Abate +1

SafePILCO is a software tool for safe and data-efficient policy search with reinforcement learning. It extends the known PILCO algorithm, originally written in MATLAB, to support s…

math.OC2019★ 1 cited

Efficient Semidefinite Programming with approximate ADMM

Nikitas Rontsis, Paul J. Goulart, Yuji Nakatsukasa

Tenfold improvements in computation speed can be brought to the alternating direction method of multipliers (ADMM) for Semidefinite Programming with virtually no decrease in robust…

math.OC2019★ 3 cited

Optimal Approximation of Doubly Stochastic Matrices

Nikitas Rontsis, Paul J. Goulart

We consider the least-squares approximation of a matrix C in the set of doubly stochastic matrices with the same sparsity pattern as C. Our approach is based on applying the well-k…

math.NA2019

Accuracy of approximate projection to the semidefinite cone

Paul J. Goulart, Yuji Nakatsukasa, Nikitas Rontsis

When a projection of a symmetric or Hermitian matrix to the positive semidefinite cone is computed approximately (or to working precision on a computer), a natural question is to q…

math.OC2019

An active-set algorithm for norm constrained quadratic problems

Nikitas Rontsis, Paul J. Goulart, Yuji Nakatsukasa

We present an algorithm for the minimization of a nonconvex quadratic function subject to linear inequality constraints and a two-sided bound on the 2-norm of its solution. The alg…

stat.ML2017★ 3 cited

Distributionally Ambiguous Optimization Techniques for Batch Bayesian Optimization

Nikitas Rontsis, Michael A. Osborne, Paul J. Goulart

We propose a novel, theoretically-grounded, acquisition function for Batch Bayesian optimization informed by insights from distributionally ambiguous optimization. Our acquisition…