35 citations · 393 across the 62 of their papers we have counts for
63 papers
Toward the Optimal Regret-Instability Trade-off in Multi-Armed Bandits
Kaifei Wang, Yinyu Ye, Han Zhong
Multi-armed bandit algorithms are evaluated by regret, yet comparable regret can coexist with different allocations across independent runs. We study the trade-off between worst-ca…
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…
A homogeneous second-order descent method for nonconvex optimization
Chuwen Zhang, Dongdong Ge, Chang He +4
In this paper, we introduce a Homogeneous Second-Order Descent Method (HSODM) using the homogenized quadratic approximation to the original function. The merit of homogenization is…
SOLNP+: A Derivative-Free Solver for Constrained Nonlinear Optimization
Dongdong Ge, Tianhao Liu, Jinsong Liu +2
SOLNP+ is a derivative-free solver for constrained nonlinear optimization. It starts from SOLNP proposed in 1989 by Ye Ye with the main idea that uses finite difference to approxim…
An Enhanced ADMM-based Interior Point Method for Linear and Conic Optimization
Qi Deng, Qing Feng, Wenzhi Gao +8
The ADMM-based interior point (ABIP, Lin et al. 2021) method is a hybrid algorithm that effectively combines interior point method (IPM) and first-order methods to achieve a perfor…
Optimal Diagonal Preconditioning
Zhaonan Qu, Wenzhi Gao, Oliver Hinder +2
Preconditioning has long been a staple technique in optimization, often applied to reduce the condition number of a matrix and speed up the convergence of algorithms. Although ther…
Cardinal Optimizer (COPT) User Guide
Dongdong Ge, Qi Huangfu, Zizhuo Wang +2
Cardinal Optimizer is a high-performance mathematical programming solver for efficiently solving largescale optimization problem. This documentation provides basic introduction to…
HDSDP: Software for Semidefinite Programming
Wenzhi Gao, Dongdong Ge, Yinyu Ye
HDSDP is a numerical software solving the semidefinite programming problems. The main framework of HDSDP resembles the dual-scaling interior point solver DSDP [BY2008] and several…
Fine-grained Correlation Loss for Regression
Chaoyu Chen, Xin Yang, Ruobing Huang +10
Regression learning is classic and fundamental for medical image analysis. It provides the continuous mapping for many critical applications, like the attribute estimation, object…
Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements
Devansh Jalota, Yinyu Ye
Fisher markets are one of the most fundamental models for resource allocation. However, the problem of computing equilibrium prices in Fisher markets typically relies on complete k…
Fairer LP-based Online Allocation via Analytic Center
Guanting Chen, Xiaocheng Li, Yinyu Ye
In this paper, we consider an online resource allocation problem where a decision maker accepts or rejects incoming customer requests irrevocably in order to maximize expected rewa…
An Adaptive State Aggregation Algorithm for Markov Decision Processes
Guanting Chen, Johann Demetrio Gaebler, Matt Peng +2
Value iteration is a well-known method of solving Markov Decision Processes (MDPs) that is simple to implement and boasts strong theoretical convergence guarantees. However, the co…
Distributed stochastic optimization with large delays
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos +2
One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms…
Solving Linear Programs with Fast Online Learning Algorithms
Wenzhi Gao, Dongdong Ge, Chunlin Sun +1
This paper presents fast first-order methods for solving linear programs (LPs) approximately. We adapt online linear programming algorithms to offline LPs and obtain algorithms tha…
Fisher Markets with Linear Constraints: Equilibrium Properties and Efficient Distributed Algorithms
Devansh Jalota, Marco Pavone, Qi Qi +1
The Fisher market is one of the most fundamental models for resource allocation problems in economic theory, wherein agents spend a budget of currency to buy goods that maximize th…
Robustifying Conditional Portfolio Decisions via Optimal Transport
Viet Anh Nguyen, Fan Zhang, Shanshan Wang +3
We propose a data-driven portfolio selection model that integrates side information, conditional estimation and robustness using the framework of distributionally robust optimizati…
From an Interior Point to a Corner Point: Smart Crossover
Dongdong Ge, Chengwenjian Wang, Zikai Xiong +1
Identifying optimal basic feasible solutions to linear programming problems is a critical task for mixed integer programming and other applications. The crossover method, which aim…
The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
Xiaocheng Li, Chunlin Sun, Yinyu Ye
In this paper, we study the bandits with knapsacks (BwK) problem and develop a primal-dual based algorithm that achieves a problem-dependent logarithmic regret bound. The BwK probl…