Zobrazeno 1 - 10
of 974
pro vyhledávání: '"Tang Liping"'
Publikováno v:
Jixie qiangdu, Pp 1130-1140 (2023)
The vertical well inclinometer is widely used in downhole data measurement, wherein the mud pulse generator used for data transmission has substantial significance to the accuracy and stability of data. For this instrument, the performance of its mud
Externí odkaz:
https://doaj.org/article/d8c72d4c2c69479a8c10bf7c88083d94
In this paper, we deal with multiobjective composite optimization problems, where each objective function is a combination of smooth and possibly non-smooth functions. We first propose a parameter-dependent conditional gradient method to solve this p
Externí odkaz:
http://arxiv.org/abs/2410.18465
Stochastic multi-objective optimization (SMOO) has recently emerged as a powerful framework for addressing machine learning problems with multiple objectives. The bias introduced by the nonlinearity of the subproblem solution mapping complicates the
Externí odkaz:
http://arxiv.org/abs/2410.06632
Autor:
Fan, Zhuoxin, Tang, Liping
Recently the away-step Frank-Wolfe algoritm for constrained multiobjective optimization has been shown linear convergence rate over a polytope which is generated by finite points set. In this paper we design a decomposition-invariant pairwise frank-w
Externí odkaz:
http://arxiv.org/abs/2409.04671
In this paper, we develop a unified framework and convergence analysis of descent methods for vector optimization problems (VOPs) from a majorization-minimization perspective. By choosing different surrogate functions, the generic method reduces to s
Externí odkaz:
http://arxiv.org/abs/2407.13245
On the convergence of conditional gradient method for unbounded multiobjective optimization problems
This paper focuses on developing a conditional gradient algorithm for multiobjective optimization problems with an unbounded feasible region. We employ the concept of recession cone to establish the well-defined nature of the algorithm. The asymptoti
Externí odkaz:
http://arxiv.org/abs/2403.02671
Autor:
Liu, Guangyi, Wang, Yu, Feng, Zeyu, Wu, Qiyu, Tang, Liping, Gao, Yuan, Li, Zhen, Cui, Shuguang, McAuley, Julian, Yang, Zichao, Xing, Eric P., Hu, Zhiting
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and
Externí odkaz:
http://arxiv.org/abs/2402.19009
Autor:
Liu, Zhengzhong, Qiao, Aurick, Neiswanger, Willie, Wang, Hongyi, Tan, Bowen, Tao, Tianhua, Li, Junbo, Wang, Yuqi, Sun, Suqi, Pangarkar, Omkar, Fan, Richard, Gu, Yi, Miller, Victor, Zhuang, Yonghao, He, Guowei, Li, Haonan, Koto, Fajri, Tang, Liping, Ranjan, Nikhil, Shen, Zhiqiang, Ren, Xuguang, Iriondo, Roberto, Mu, Cun, Hu, Zhiting, Schulze, Mark, Nakov, Preslav, Baldwin, Tim, Xing, Eric P.
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs have only released partial artifacts, such as the final model weights
Externí odkaz:
http://arxiv.org/abs/2312.06550
This paper addresses unconstrained multiobjective optimization problems where two or more continuously differentiable functions have to be minimized. We delve into the conjugate gradient methods proposed by Lucambio P\'{e}rez and Prudente (SIAM J Opt
Externí odkaz:
http://arxiv.org/abs/2312.02461
In this paper, we propose a simple yet efficient strategy for improving the multi-objective steepest descent method proposed by Fliege and Svaiter (Math Methods Oper Res, 2000, 3: 479--494). The core idea behind this strategy involves incorporating a
Externí odkaz:
http://arxiv.org/abs/2311.08109