Zobrazeno 1 - 10
of 69 386
pro vyhledávání: '"Descent algorithm"'
Dynamic optimization problems involving discrete decisions have several applications, yet lead to challenging optimization problems that must be addressed efficiently. Combining discrete variables with potentially nonlinear constraints stemming from
Externí odkaz:
http://arxiv.org/abs/2409.09237
This paper studies the problem of learning Bayesian networks from continuous observational data, generated according to a linear Gaussian structural equation model. We consider an $\ell_0$-penalized maximum likelihood estimator for this problem which
Externí odkaz:
http://arxiv.org/abs/2408.11977
Autor:
Zhao, Jinwei, Gori, Marco, Betti, Alessandro, Melacci, Stefano, Zhang, Hongtao, Liu, Jiedong, Hei, Xinhong
Gradient descent (GD) and stochastic gradient descent (SGD) have been widely used in a large number of application domains. Therefore, understanding the dynamics of GD and improving its convergence speed is still of great importance. This paper caref
Externí odkaz:
http://arxiv.org/abs/2409.06542
Autor:
Blomquist, Solvay, Choi, Heejoo, Kang, Hyukmo, Derby, Kevin, Nicolas, Pierre, Douglas, Ewan S., Kim, Daewook
When a telescope doesn't reach a reasonable point spread function on the detector or detectable wavefront quality after initial assembly, a coarse phase alignment on-sky is crucial. Before utilizing a closed loop adaptive optics system, the observato
Externí odkaz:
http://arxiv.org/abs/2409.04640
Autor:
Wang, Zishuo1 (AUTHOR), Chen, Beichen1,2 (AUTHOR), Sun, Hongliang1 (AUTHOR), Liang, Shuning1 (AUTHOR) liangshuning2019@163.com
Publikováno v:
Scientific Reports. 12/28/2024, Vol. 14 Issue 1, p1-17. 17p.
We develop an accelerated gradient descent algorithm on the Grassmann manifold to compute the subspace spanned by a number of leading eigenvectors of a symmetric positive semi-definite matrix. This has a constant cost per iteration and a provable ite
Externí odkaz:
http://arxiv.org/abs/2406.18433
Publikováno v:
Physical Review B (2024)
This paper is concerned with $\textit{ab initio}$ crystal structure relaxation under a fixed unit cell volume, which is a step in calculating the static equations of state and forms the basis of thermodynamic property calculations for materials. The
Externí odkaz:
http://arxiv.org/abs/2405.02934
This letter investigates the convergence and concentration properties of the Stochastic Mirror Descent (SMD) algorithm utilizing biased stochastic subgradients. We establish the almost sure convergence of the algorithm's iterates under the assumption
Externí odkaz:
http://arxiv.org/abs/2407.05863
Saddle point problems, ubiquitous in optimization, extend beyond game theory to diverse domains like power networks and reinforcement learning. This paper presents novel approaches to tackle saddle point problem, with a focus on continuous-time conte
Externí odkaz:
http://arxiv.org/abs/2404.04907