35 citations · 393 across the 62 of their papers we have counts for
7 papers · 2 filters
Learning to Pivot as a Smart Expert
Tianhao Liu, Shanwen Pu, Dongdong Ge +1
Linear programming has been practically solved mainly by simplex and interior point methods. Compared with the weakly polynomial complexity obtained by the interior point methods,…
Blessing of High-Order Dimensionality: from Non-Convex to Convex Optimization for Sensor Network Localization
Mingyu Lei, Jiayu Zhang, Yinyu Ye
This paper investigates the Sensor Network Localization (SNL) problem, which seeks to determine sensor locations based on known anchor locations and partially given anchors-sensors…
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
Jiyuan Tan, Chenyu Xue, Chuwen Zhang +3
Gradient dominance property is a condition weaker than strong convexity, yet sufficiently ensures global convergence even in non-convex optimization. This property finds wide appli…
Homogeneous second-order descent framework: a fast alternative to Newton-type methods
Chang He, Yuntian Jiang, Chuwen Zhang +3
This paper proposes a homogeneous second-order descent framework (HSODF) for nonconvex and convex optimization based on the generalized homogeneous model (GHM). In comparison to th…
Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching
Yanguang Chen, Wenzhi Gao, Wanyu Zhang +3
In this paper, we propose a Pre-trained Mixed Integer Optimization framework (PreMIO) that accelerates online mixed integer program (MIP) solving with offline datasets and machine…
A Riemannian Dimension-reduced Second Order Method with Application in Sensor Network Localization
Tianyun Tang, Kim-Chuan Toh, Nachuan Xiao +1
In this paper, we propose a cubic-regularized Riemannian optimization method (RDRSOM), which partially exploits the second order information and achieves the iteration complexity o…
A gradient descent akin method for constrained optimization: algorithms and applications
Long Chen, Kai-Uwe Bletzinger, Nicolas R. Gauger +1
We present a first-order method for solving constrained optimization problems. The method is derived from our previous work, a modified search direction method inspired by singular…