Results 1 to 10 of about 982,194 (337)

Variational methods [PDF]

, 2015
International audienceThis contribution presents derivative-based methods for local sensitivity analysis, called Variational Sensitivity Analysis (VSA). If one defines an output called the response function, its sensitivity to inputs variations around a ...
A Griewank, A Saltelli, A Sandu, A Sandu, B Ancell, BT Hoover, C Lauvernet, C Liu, CC Wu, DG Cacuci, DM Hamby, F Sévellec, G Desroziers, J Marotzke, J Thuburn, JF Sykes, JM Lellouche, L Hascoet, M Mu, N Ayoub, O Rivière, P Heimbach, R Giering, RM Errico, S Li, SG Chen, W Castaings, X Zou, Y Zhu   +28 more
core   +9 more sources

Metric Methods in Calculus of Variations [PDF]

Proceedings of the National Academy of Sciences, 1937
1. In each problem of calculus of variations there are four fundamental data: (I) A point-space S or a domain of a point-space. (II) A function φ from which there is derived a functional in a class P of entities of S whose dimensions are ≧ 1. With each element C of & there is associated a number, say λ>φ,(C), by a process of integration of ϕ along C ...
Karl Menger
openalex   +5 more sources

A Note on Variational Methods [PDF]

Proceedings of the American Mathematical Society, 1964
In this note a new development of the variational method due to G. M. Golusin will be given. The Golusin variational method, found in Geometrische Funktionentheorie [1, pp. 96-105], is established there only after rather lengthy and tedious considerations. Below, the interior variational formula of M. M.
J. T. Poole
openalex   +3 more sources

Quantum resources of quantum and classical variational methods [PDF]

Machine Learning: Science and Technology
Variational techniques have long been at the heart of atomic, solid-state, and many-body physics. They have recently extended to quantum and classical machine learning, providing a basis for representing quantum states via neural networks.
Thomas Spriggs, Arash Ahmadi, Bokai Chen, Eliska Greplova   +3 more
doaj   +2 more sources

Network element methods for linear elasticity

Comptes Rendus. Mécanique, 2023
We explain how to derive a network element for the linear elasticity problem. After presenting sufficient conditions on the network for the validity of a discrete Korn inequality, we also propose several variations of the presented method and in ...
Coatléven, Julien
doaj   +1 more source

The variational method of moments

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2023
Abstract The conditional moment problem is a powerful formulation for describing structural causal parameters in terms of observables, a prominent example being instrumental variable regression. We introduce a very general class of estimators called the variational method of moments (VMM), motivated by a variational minimax reformulation
Bennett, Andrew, Kallus, Nathan
openaire   +2 more sources

Inertial projection methods for solving general quasi-variational inequalities

AIMS Mathematics, 2021
In this paper, we consider a new class of quasi-variational inequalities, which is called the general quasi-variational inequality. Using the projection operator technique, we establish the equivalence between the general quasi-variational inequalities ...
Saudia Jabeen, Bandar Bin-Mohsin, Muhammad Aslam Noor, Khalida Inayat Noor   +3 more
doaj   +1 more source

Variational Methods in Optical Quantum Machine Learning

IEEE Access, 2023
The computing world is rapidly evolving and advancing, with new ground-breaking technologies emerging. Quantum Computing and Quantum Machine Learning have opened up new possibilities, providing unprecedented computational power and problem-solving ...
Marco Simonetti, Damiano Perri, Osvaldo Gervasi   +2 more
doaj   +1 more source

Variational Estimation Methods for Sturm–Liouville Problems

Mathematics, 2022
In this paper, we are concerned with approach solutions for Sturm–Liouville problems (SLP) using variational problem (VP) formulation of regular SLP.
Elena Corina Cipu, Cosmin Dănuţ Barbu
doaj   +1 more source

Inexact Model: A Framework for Optimization and Variational Inequalities [PDF]

, 2020
In this paper we propose a general algorithmic framework for first-order methods in optimization in a broad sense, including minimization problems, saddle-point problems and variational inequalities.
Agafonov, Artem, Dvinskikh, Darina, Dvurechensky, Pavel, Gasnikov, Alexander, Kroshnin, Alexey, Pasechnyuk, Dmitry, Piskunova, Victorya, Stonyakin, Fedor, Tyurin, Alexander   +8 more
core   +3 more sources