Concept

Semidefinite programming

Related publications (214)

New Graph Algorithms via Polyhedral Techniques

In this thesis we give new algorithms for two fundamental graph problems. We develop novel ways of using linear programming formulations, even exponential-sized ones, to extract structure from problem instances and to guide algorithms in making progress. S ...

EPFL2019

Safe Convex Learning under Uncertain Constraints

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

We address the problem of minimizing a convex smooth function f(x) over a compact polyhedral set D given a stochastic zeroth-order constraint feedback model. This problem arises in safety-critical machine learning applications, such as personalized medicin ...

PMLR2019

No Small Linear Program Approximates Vertex Cover Within a Factor 2-epsilon

Ola Nils Anders Svensson, Abbas Bazzi

The vertex cover problem is one of the most important and intensively studied combinatorial optimization problems. Khot and Regev [Khot S, Regev O (2008) Vertex cover might be hard to approximate to within 2 - epsilon. J. Comput. System Sci. 74(3): 335-349 ...

INFORMS2019

A Conditional Gradient-Based Augmented Lagrangian Framework

Volkan Cevher, Alp Yurtsever

This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing conditional gradient methods either do not apply to our template or are too slow ...

2019

An adaptive primal-dual framework for nonsmooth convex minimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new self-adaptive and double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov’s smoothing technique via general Bregman distance functions. It self-adaptiv ...

2019

MATHICSE Technical Report: Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial\ viscosity tuned by neural networks

Jan Sickmann Hesthaven, Niccolo' Discacciati, Deep Ray

High-order numerical solvers for conservation laws suer from Gibbs phenomenon close to discontinuities, leading to spurious oscillations and a detrimental effect on the solution accuracy. A possible strategy to reduce it comprises adding a suitable amount ...

MATHICSE2019

Distribution Network Restoration in a Multiagent Framework Using a Convex OPF Model

Hossein Sekhavatmanesh

The ever-increasing requirement for reliability and quality of supply suggests to enable the self-healing features of modern distribution networks employing the intelligent measurement, communication, and control facilities of smart grids. In this paper, t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Sampling can be faster than optimization

Nicolas Henri Bernard Flammarion

Optimization algorithms and Monte Carlo sampling algorithms have provided the computational foundations for the rapid growth in applications of statistical machine learning in recent years. There is, however, limited theoretical understanding of the relati ...

PNAS2019

On The Move: Localization With Kinetic Euclidean Distance Matrices

Martin Vetterli, Ivan Dokmanic, Puoya Tabaghi

In this paper, we propose kinetic Euclidean distance matrices (KEDMs) a new algebraic tool for localization of moving points from spatio temporal distance measurements. KEDMs are inspired by the well-known Euclidean distance matrices (EDM) which model stat ...

IEEE2019

Adjoint-based Optimization for Diffractive Beam-Splitters

Toralf Scharf, Dong Cheon Kim

Computational optimization of diffractive elements is often limited by a large number of design parameters. Adjoint method allows the gradient of target function respect to all design variables to be calculated with only two electromagnetic simulations. We ...

IEEE2019

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