Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Phan, Duy Nhat"'
In this paper, we mainly study solution uniqueness of some convex optimization problems. Our characterizations of solution uniqueness are in terms of the radial cone. This approach allows us to know when a unique solution is a strong solution or even
Externí odkaz:
http://arxiv.org/abs/2401.10346
In this paper, we introduce several geometric characterizations for strong minima of optimization problems. Applying these results to nuclear norm minimization problems allows us to obtain new necessary and sufficient quantitative conditions for this
Externí odkaz:
http://arxiv.org/abs/2308.09224
Developing effective Multi-Agent Systems (MAS) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of Multi-Agent Deep Reinforcement Learning (MADRL) in cooperative MAS, one major challeng
Externí odkaz:
http://arxiv.org/abs/2308.09219
We study a class of nonconvex nonsmooth optimization problems in which the objective is a sum of two functions: One function is the average of a large number of differentiable functions, while the other function is proper, lower semicontinuous and ha
Externí odkaz:
http://arxiv.org/abs/2305.06848
Instance-Based Learning Theory (IBLT) is a comprehensive account of how humans make decisions from experience during dynamic tasks. Since it was first proposed almost two decades ago, multiple computational models have been constructed based on IBLT
Externí odkaz:
http://arxiv.org/abs/2111.10268
Publikováno v:
SIAM J. on Mathematics of Data Science 4 (1), pp. 1-25, 2022
In this paper, we consider a class of nonsmooth nonconvex optimization problems whose objective is the sum of a block relative smooth function and a proper and lower semicontinuous block separable function. Although the analysis of block proximal gra
Externí odkaz:
http://arxiv.org/abs/2107.04395
Autor:
Phan, Duy Nhat, Thi, Hoai An Le
In this paper, we focus on the problem of minimizing the sum of a nonconvex differentiable function and a DC (Difference of Convex functions) function, where the differentiable function is not restricted to the global Lipschitz gradient continuity as
Externí odkaz:
http://arxiv.org/abs/2106.04743
Publikováno v:
Computational Optimization and Applications 83, pp. 247-285, 2022
In this paper, we propose an algorithmic framework, dubbed inertial alternating direction methods of multipliers (iADMM), for solving a class of nonconvex nonsmooth multiblock composite optimization problems with linear constraints. Our framework emp
Externí odkaz:
http://arxiv.org/abs/2102.05433
Publikováno v:
Journal on Machine Learning Research 24 (18), pp. 1-41, 2023
In this paper, we introduce TITAN, a novel inerTIal block majorizaTion minimizAtioN framework for non-smooth non-convex optimization problems. To the best of our knowledge, TITAN is the first framework of block-coordinate update method that relies on
Externí odkaz:
http://arxiv.org/abs/2010.12133
We consider the large sum of DC (Difference of Convex) functions minimization problem which appear in several different areas, especially in stochastic optimization and machine learning. Two DCA (DC Algorithm) based algorithms are proposed: stochasti
Externí odkaz:
http://arxiv.org/abs/1911.03992