Publications related to Interior-point method

Differentially Private Stochastic Coordinate Descent

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this ve ...

AIAA2021

Implications of ANEC for SCFTs in four dimensions

Andrea Manenti, Alessandro Vichi

We explore consequences of the Averaged Null Energy Condition (ANEC) for scaling dimensions Delta of operators in four-dimensional N = 1 superconformal field theories. We show that in many cases the ANEC bounds are stronger than the corresponding unitarity ...

SPRINGER2020

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Ola Nils Anders Svensson, Ashkan Norouzi Fard, Justin Dean Ward

Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best-known algorithm for k-means in Euclidean space with a provable guarantee is a simple local ...

SIAM PUBLICATIONS2020

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

In this work, we revisit a classical incremental implementation of the primal-descent dual-ascent gradient method used for the solution of equality constrained optimization problems. We provide a short proof that establishes the linear (exponential) conver ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Topology-Finding of Tensegrity Structures Considering Global Stability Condition

Yafeng Wang

This study proposes a general framework for topology-finding or topology optimization of tensegrity structures. The existing topology-finding formulation of tensegrity structures based on mixed-integer linear programming (MILP) was improved and transformed ...

ASCE-AMER SOC CIVIL ENGINEERS2020

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 suffer 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 amoun ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

Data-Driven Distributed Reactive Power Sharing in Microgrids

Alireza Karimi, Seyed Sohail Madani

In this paper, reactive power sharing for Photovoltaic (PV) units in islanded microgrids has been formulated as a robust control design problem and is solved using convex optimization method. In addition to reactive power sharing, the disturbance rejection ...

IEEE2020

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Shaul Nadav Hallak

This paper introduces a method for computing points satisfying the second-order necessary optimality conditions for nonconvex minimization problems subject to a closed and convex constraint set. The method comprises two independent steps corresponding to t ...

SPRINGER/PLENUM PUBLISHERS2020

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Interior-point method

Differentially Private Stochastic Coordinate Descent

Implications of ANEC for SCFTs in four dimensions

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Topology-Finding of Tensegrity Structures Considering Global Stability Condition

Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Data-Driven Distributed Reactive Power Sharing in Microgrids

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

A Reinforcement Learning Approach to Train Routing and Scheduling

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A Reinforcement Learning Approach to Train Routing and Scheduling

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Topology-Finding of Tensegrity Structures Considering Global Stability Condition

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Data-Driven Distributed Reactive Power Sharing in Microgrids

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Differentially Private Stochastic Coordinate Descent

Implications of ANEC for SCFTs in four dimensions