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20182022
most citedDAG-GNN: DAG Structure Learning with Graph Neural Networks

129 citations · 238 across the 10 of their papers we have counts for

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9 papers · 1 filter

math.OC20213 cited

Proportional-Integral Projected Gradient Method for Infeasibility Detection in Conic Optimization

Yue Yu, Ufuk Topcu

A constrained optimization problem is primal infeasible if its constraints cannot be satisfied, and dual infeasible if the constraints of its dual problem cannot be satisfied. We p…

math.OC2020

Proportional-Integral Projected Gradient Method for Model Predictive Control

Yue Yu, Purnanand Elango, Behçet Açikmeşe

Recently there has been an increasing interest in primal-dual methods for model predictive control (MPC), which require minimizing the (augmented) Lagrangian at each iteration. We…

math.OC2020

RC Circuits based Distributed Conditional Gradient Method

Yue Yu, Behçet Açıkmeşe

We consider distributed optimization on undirected connected graphs. We propose a novel distributed conditional gradient method with (O(1/\sqrt{k})) convergence. Compared with exis…

math.OC20191 cited

RLC Circuits based Distributed Mirror Descent Method

Yue Yu, Behçet Açıkmeşe

We consider distributed optimization with smooth convex objective functions defined on an undirected connected graph. Inspired by mirror descent mehod and RLC circuits, we propose…

math.OC2019

Stochastic Bregman Parallel Direction Method of Multipliers for Distributed Optimization

Yue Yu, Behçet Açıkmeşe

Bregman parallel direction method of multipliers (BPDMM) efficiently solves distributed optimization over a network, which arises in a wide spectrum of collaborative multi-agent le…

math.OC20192 cited

Exploring Fast and Communication-Efficient Algorithms in Large-scale Distributed Networks

Yue Yu, Jiaxiang Wu, Junzhou Huang

The communication overhead has become a significant bottleneck in data-parallel network with the increasing of model size and data samples. In this work, we propose a new algorithm…