Results 91 to 100 of about 1,156,733 (201)

Learning-Augmented Quasi-Gradient Operators for Constrained Optimization: A Contraction–Bias–Variance Decomposition

Mathematics
This paper develops a rigorous operator-theoretic framework for learning-augmented quasi-gradient methods in constrained optimization. We consider the minimization of an objective function over a closed convex feasible set, where feasibility is enforced ...
Gilberto Pérez-Lechuga, Marco Antonio Coronel García, Ana Lidia Martínez Salazar   +2 more
doaj   +1 more source

Stability analysis of two stage stochastic mathematical programs with complementarity constraints via NLP-regularization

This paper presents numerical approximation schemes for a two stage stochastic programming problem where the second stage problem has a general nonlinear complementarity constraint: first, the complementarity constraint is approximated by a parameterized
Xu, Huifu, Lin, Gui-Hua, Liu, Yongchao
core   +1 more source

SDE WEAK APPROXIMATION LIBRARY (SDE WA) (VERSION 1.0) [PDF]

In application of mathematical finance to practical problems, weak approximation of stochastic differential equations (SDEs) is one of the most important themes.In probabilistic approach to this problem, the Euler-Maruyama scheme which is a first-order ...
Mariko Ninomiya
core  

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Path‐Decoupled Cation‐Eutaxy III–V van der Waals Memristive Semiconductors for Mitigating the Neuromorphic Accuracy‐Energy Trade‐off

Advanced Materials, EarlyView.
Path‐decoupled III–V van der Waals memtransistors spatially separate ionic and electronic transport to overcome the conventional trade‐off between accuracy and energy in neuromorphic hardware. Mobile K+ ions in the vdW gaps set a wide conductance window, Gmax/Gmin, while gate‐tunable hole conduction lowers programming energy, enabling reliable ...
Jihong Bae, Ji Hoon Han, Taeyoung Kim, Jongbum Won, Seokmin Park, Gyu Won Kim, Sungsoon Kim, Hong Choi, Taehoon Kim, Hong‐Sub Lee, Jae‐Hyun Lee, Young Jin Choi, Jinwoo Cheon, Wooyoung Shim   +13 more
wiley   +1 more source

"Pricing Barrier and Average Options under Stochastic Volatility Environment" [PDF]

This paper proposes a new approximation method of pricing barrier and average options under stochastic volatility environment by applying an asymptotic expansion approach.
Kenichiro Shiraya, Akihiko Takahashi, Masashi Toda   +2 more
core   +2 more sources

A novel family of generalized stochastic neural network operators with illustrations of their various capabilities

Ain Shams Engineering Journal
This study introduces a novel family of generalized stochastic neural network (GSNN) operators with enhanced modeling and approximation capabilities.
Abeer Aljohani
doaj   +1 more source

Mean-field approximation of stochastic population processes in games [PDF]

We here establish an upper bound on the probability for deviations of a Markov population process from its mean-field approximation.
Jörgen Weibull, Michel Benaïm
core  

“Smelltronics”—From Gas to Smell Sensing

Advanced Materials, EarlyView.
The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono, Tsunaki Takahashi, Jiangyang Liu, Haruka Honda, Zeng Jing, Wataru Tanaka, Takuro Hosomi, Takeshi Yanagida   +7 more
wiley   +1 more source

Statistical approximation of multicriteria problems of stochastic programming

Доповiдi Нацiональної академiї наук України
The article validates an approximation technique for solving multiobjective stochastic optimization problems. As a generalized model of a stochastic system to be optimized, a vector "input–random output" system is considered.
B.V. Norkin
doaj   +1 more source