Results 41 to 50 of about 24,658 (179)

Graph‐based imitation and reinforcement learning for efficient Benders decomposition

AIChE Journal, EarlyView.
Abstract This work introduces an end‐to‐end graph‐based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network, which takes as input a bipartite graph representation of the master problem and proposes a candidate solution.
Bernard T. Agyeman, Zhe Li, Ilias Mitrai, Prodromos Daoutidis   +3 more
wiley   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

A Global Optimization Algorithm for Signomial Geometric Programming Problem

Abstract and Applied Analysis, 2014
This paper presents a global optimization algorithm for solving the signomial geometric programming (SGP) problem. In the algorithm, by the straight forward algebraic manipulation of terms and by utilizing a transformation of variables, the initial ...
Xue-Ping Hou, Pei-Ping Shen, Yong-Qiang Chen   +2 more
doaj   +1 more source

A Cutting Plane Algorithm for Solving Bilinear Programs [PDF]

, 1974
Nonconvex programs which have either a nonconvex minimand and/or a nonconvex feasible region have been considered by most mathematical programmers as a hopelessly difficult area of research. There are, however, two exceptions where considerable effort to
Konno, H.
core  

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Novel Model for The Energy Minimisation of Natural Gas Transmission Networks

Chemical Engineering Transactions, 2017
Natural gas transmission through an extensive pipeline network is an energy consuming process required gas compression due to significant pressure drop. The main difficulties in optimising a natural gas transmission network are the nonconvexities of the ...
Y. Liang, E. Pahija, C.-W. Hui
doaj   +1 more source

Extended Formulations in Mixed-integer Convex Programming

, 2016
We present a unifying framework for generating extended formulations for the polyhedral outer approximations used in algorithms for mixed-integer convex programming (MICP).
A Ahmadi, A Ben-Tal, H Hijazi, I Harjunkoski, K Abhishek, L Hien, M Grant, M Lubin, M Tawarmalani, MS Lobo, N Goldberg, O Günlük, OK Gupta, P Belotti, P Bonami, P Bonami, R Fletcher, RH Byrd, S Drewes, T Achterberg   +19 more
core   +1 more source

Efficiency and Approachability of Nonconvex Bicriteria Programs

Journal of Mathematical Analysis and Applications, 2001
The paper presents some characterizations of efficient and proper efficient solutions of general bicriteria optimization problems via the concepts of modified lower envelopes and approachability [\textit{M. L. Tenhuisen} and \textit{M. M. Wiecek}, J. Glob. Optim. 11, 225-251 (1997; Zbl 0903.90147)]. By using two kinds of nonlinear Lagrangian functions,
Huang, X.X., Yang, X.Q.
openaire   +1 more source

Primal-Dual Interior Methods for Nonconvex Nonlinear Programming [PDF]

SIAM Journal on Optimization, 1998
Summary: This paper concerns large-scale general (nonconvex) nonlinear programming when first and second derivatives of the objective and constraint functions are available. A method is proposed that is based on finding an approximate solution of a sequence of unconstrained subproblems parameterized by a scalar parameter. The objective function of each
Forsgren, Anders, Gill, Philip E.
openaire   +2 more sources

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source