Zobrazeno 1 - 10
of 2 001
pro vyhledávání: '"Luo, Luo"'
We study a typical optimization model where the optimization variable is composed of multiple probability distributions. Though the model appears frequently in practice, such as for policy problems, it lacks specific analysis in the general setting.
Externí odkaz:
http://arxiv.org/abs/2407.14839
This paper addresses the challenge of solving large-scale nonlinear equations with H\"older continuous Jacobians. We introduce a novel Incremental Gauss--Newton (IGN) method within explicit superlinear convergence rate, which outperforms existing met
Externí odkaz:
http://arxiv.org/abs/2407.03195
This paper considers the distributed convex-concave minimax optimization under the second-order similarity. We propose stochastic variance-reduced optimistic gradient sliding (SVOGS) method, which takes the advantage of the finite-sum structure in th
Externí odkaz:
http://arxiv.org/abs/2405.16126
Publikováno v:
Paramasastra. 9:194-210
Besides being used for exchanging information, language is also used by humans to refer to one thing in a context. Without context, the interlocutor will find difficulties to understand the meaning of the designation expressed by the speaker. In prag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2217ad7269f54bd843cc4221243791a2
https://doi.org/10.26740/paramasastra.v9n2.p194-210
https://doi.org/10.26740/paramasastra.v9n2.p194-210
This paper considers the optimization problem of the form $\min_{{\bf x}\in{\mathbb R}^d} f({\bf x})\triangleq \frac{1}{n}\sum_{i=1}^n f_i({\bf x})$, where $f(\cdot)$ satisfies the Polyak--{\L}ojasiewicz (PL) condition with parameter $\mu$ and $\{f_i
Externí odkaz:
http://arxiv.org/abs/2402.02569
We consider the finite-sum optimization problem, where each component function is strongly convex and has Lipschitz continuous gradient and Hessian. The recently proposed incremental quasi-Newton method is based on BFGS update and achieves a local su
Externí odkaz:
http://arxiv.org/abs/2402.02359
We consider decentralized gradient-free optimization of minimizing Lipschitz continuous functions that satisfy neither smoothness nor convexity assumption. We propose two novel gradient-free algorithms, the Decentralized Gradient-Free Method (DGFM) a
Externí odkaz:
http://arxiv.org/abs/2310.11973
Publikováno v:
Cell Death and Disease, Vol 15, Iss 11, Pp 1-16 (2024)
Abstract The mitochondrial dynamic imbalance is an important cause of myocardial ischaemia/reperfusion (I/R) injury and dysfunction. Psmb8, as one of the immunoproteasome catalytic subunits, is a key regulator of protein homoeostasis, inflammation an
Externí odkaz:
https://doaj.org/article/c42f94fde93b42ac9f26e9046785c5cc
This paper considers stochastic first-order algorithms for minimax optimization under Polyak--{\L}ojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem of the form $\min_x \max_y f(x,y)\triangleq \frac{1}{n} \sum_{i=1}^
Externí odkaz:
http://arxiv.org/abs/2307.15868
We present a method for solving general nonconvex-strongly-convex bilevel optimization problems. Our method -- the \emph{Restarted Accelerated HyperGradient Descent} (\texttt{RAHGD}) method -- finds an $\epsilon$-first-order stationary point of the o
Externí odkaz:
http://arxiv.org/abs/2307.00126