Zobrazeno 1 - 10
of 1 172
pro vyhledávání: '"Yuan, Xiaoming"'
We propose a new stochastic proximal quasi-Newton method for minimizing the sum of two convex functions in the particular context that one of the functions is the average of a large number of smooth functions and the other one is nonsmooth. The new m
Externí odkaz:
http://arxiv.org/abs/2409.16971
Optimal control problems with nonsmooth objectives and nonlinear partial differential equation (PDE) constraints are challenging, mainly because of the underlying nonsmooth and nonconvex structures and the demanding computational cost for solving mul
Externí odkaz:
http://arxiv.org/abs/2409.14417
Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising performance. However, existing pruning methods often necessitate ineffi
Externí odkaz:
http://arxiv.org/abs/2408.03728
Autor:
Zhao, Pengxiang, Li, Ping, Gu, Yingjie, Zheng, Yi, Kölker, Stephan Ludger, Wang, Zhefeng, Yuan, Xiaoming
As deep learning models exponentially increase in size, optimizers such as Adam encounter significant memory consumption challenges due to the storage of first and second moment data. Current memory-efficient methods like Adafactor and CAME often com
Externí odkaz:
http://arxiv.org/abs/2403.14958
Autor:
Wang, Zimeng, Dou, Zhiyang, Xu, Rui, Lin, Cheng, Liu, Yuan, Long, Xiaoxiao, Xin, Shiqing, Komura, Taku, Yuan, Xiaoming, Wang, Wenping
We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of the input or suffer from substantial computational costs, thereby limi
Externí odkaz:
http://arxiv.org/abs/2401.12946
We show that the physics-informed neural networks (PINNs), in combination with some recently developed discontinuity capturing neural networks, can be applied to solve optimal control problems subject to partial differential equations (PDEs) with int
Externí odkaz:
http://arxiv.org/abs/2308.06709
We introduce a new analytic framework to analyze the convergence of the Randomized Stochastic Gradient Descent Ascent (RSGDA) algorithm for stochastic minimax optimization problems. Under the so-called NC-PL condition on one of the variables, our ana
Externí odkaz:
http://arxiv.org/abs/2307.13880
We consider a general class of nonsmooth optimal control problems with partial differential equation (PDE) constraints, which are very challenging due to its nonsmooth objective functionals and the resulting high-dimensional and ill-conditioned syste
Externí odkaz:
http://arxiv.org/abs/2307.00296
Autor:
Wu, Hao-Ning, Yuan, Xiaoming
We propose a novel spectral method for the Allen--Cahn equation on spheres that does not necessarily require quadrature exactness assumptions. Instead of certain exactness degrees, we employ a restricted isometry relation based on the Marcinkiewicz--
Externí odkaz:
http://arxiv.org/abs/2305.04820
We study the combination of the alternating direction method of multipliers (ADMM) with physics-informed neural networks (PINNs) for a general class of nonsmooth partial differential equation (PDE)-constrained optimization problems, where additional
Externí odkaz:
http://arxiv.org/abs/2302.08309