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Books on the topic 'Invariant method'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Haro, Àlex, Marta Canadell, Jordi-Lluis Figueras, Alejandro Luque, and Josep Maria Mondelo. The Parameterization Method for Invariant Manifolds. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29662-3.

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2

Valcartier, Canada Defence Research Establishment. Acceleration-Invariant Approximation Method For Recursive Digital Filters. s.n, 1985.

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3

Ibragimov, Nail H. Selected works: Equivalence groups and invariants of differential equations. Extension of Euler's method to parabolic equations. Invariant and formal Lagrangians. Conservation law. ALGA Publications, 2009.

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4

Heitmueller, Axel. A note on decompositions in fixed effects models in the presence of time-invariant characteristics. IZA, 2005.

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5

Geerts, A. H. W. A new method to determine the infinite zero structure of a linear time-invariant system. Eindhoven Univ. of Technology, 1988.

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6

Hermans, F. J. J. Invariant methods for image recognition. UMIST, 1993.

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7

White, Neil L., ed. Invariant Methods in Discrete and Computational Geometry. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8402-9.

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8

Ann, McCabe, and Smith-Resnick Catherine, eds. Milan family therapy: Variant and invariant methods. J. Aronson, 1990.

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9

Demmel, James Weldon. Three methods for refining estimates of invariant subspaces. Courant Institute of Mathematical Sciences, New York University, 1985.

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10

1944-, Morozov Albert D., ed. Invariant sets for Windows. World Scientific, 1999.

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11

Drašković, Zoran. On the invariance in mechanics. Matematički Institut Sanu, 2005.

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12

Signert, Kerstin. Variation och invarians i Maria Montessoris sinnestrände materiel. Göteborgs universitet, Acta Universitatis Gothoburgensis, 2012.

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13

Millsap, Roger Ellis. Statistical approaches to measurement invariance. Psychology Press, 2011.

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14

Gardner, Robert B. The method of equivalence and its applications. Society for Industrial and Applied Mathematics, 1989.

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15

Elkin, V. I. Redukt︠s︡ii︠a︡ nelineĭnykh upravli︠a︡emykh sistem: Dekompozit︠s︡ii︠a︡ i invariantnostʹ po vozmushchenii︠a︡m. Izd-vo FAZIS, 2003.

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16

1939-, Sposito Garrison, ed. Scale dependence and scale invariance in hydrology. Cambridge University Press, 1998.

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17

V, Karlin Ilya, ed. Invariant manifolds for physical and chemical kinetics. Springer, 2005.

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18

Dale, Husemöller, Echterhoff Siegfried 1960-, Fredenhagen Stefan, and Krötz Bernhard, eds. Basic bundle theory and K-cohomology invariants. Springer, 2008.

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19

Deuflhard, P. Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms. Springer-Verlag Berlin Heidelberg, 2011.

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20

Lallemand, Pierre. Theory of the lattice Boltzmann method: Dispersion, dissipation, isotropy, Galilean invariance, and stability. National Aeronautics and Space Administration, Langley Research Center, 2000.

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21

White, Neil L. Invariant Methods in Discrete and Computational Geometry: Proceedings of the Curaçao Conference, 13-17 June, 1994. Springer Netherlands, 1995.

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22

1945-, White Neil, ed. Invariant methods in discrete and computational geometry: Proceedings of the Curaçao conference, 13-17 June, 1994. Kluwer Academic Publishers, 1995.

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23

Lochak, P. Multiphase averaging for classical systems: With applications to adiabatic theorems. Springer Verlag, 1988.

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24

International Conference on Differential Geometric Methods in Theoretical Physics: Physics and Geometry (18th 1988 University of California, Davis). Differential geometric methods in theoretical physics: Physics and geometry. Plenum Press, 1990.

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25

G, Domokos, Horváth Z, and Kövesi-Domokos S, eds. Nonperturbative methods in low dimensional quantum field theories: Proceedings of the Johns Hopkins Workshop on Current Problems in Particle Theory 14, Debrecen, 1990 (August 27-30). World Scientific, 1991.

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26

Pavlović, Miroslav. Introduction to function spaces on the disk. Matematicki Institut SANU, 2004.

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27

Grebneva, Valentina. "A self-organizing person." Psychology of interaction in higher education. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1865830.

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The monograph examines the features of psychological interaction in higher education, based on the systemic principles of the functioning of social phenomena. From the point of view of a person-centered approach, concepts, principles and methods are revealed, defined: the structure, patterns, conditions and mechanisms of interaction in the "man — university" system. The idea of a new image of a "self-organizing person" is put forward. Theoretically, two self-organizing models, invariant to each other, are substantiated: a person and an educational environment. The results of an empirical study

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28

Gómez, César. Quantum groups in two-dimensional physics. Cambridge University Press, 2005.

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29

Factorization Method for Boundary Value Problems by Invariant Embedding. Elsevier, 2016. http://dx.doi.org/10.1016/c2015-0-06336-5.

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30

Henry, Jacques, and Angel M. Ramos. Factorization Method for Boundary Value Problems by Invariant Embedding. Elsevier, 2016.

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31

Henry, Jacques, and A. M. Ramos. Factorization of Boundary Value Problems Using the Invariant Embedding Method. Elsevier, 2016.

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32

Haro, Àlex, Marta Canadell, Jordi-Lluis Figueras, Alejandro Luque, and Josep Maria Mondelo. Parameterization Method for Invariant Manifolds: From Rigorous Results to Effective Computations. Springer London, Limited, 2016.

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33

Haro, Àlex, Marta Canadell, Jordi-Lluis Figueras, Alejandro Luque, and Josep Maria Mondelo. The Parameterization Method for Invariant Manifolds: From Rigorous Results to Effective Computations. Springer, 2018.

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34

Haro, Àlex, Marta Canadell, Jordi-Lluis Figueras, Alejandro Luque, and Josep Maria Mondelo. The Parameterization Method for Invariant Manifolds: From Rigorous Results to Effective Computations. Springer, 2016.

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35

Invariant Imbedding T-Matrix Method for Light Scattering by Nonspherical and Inhomogeneous Particles. Elsevier, 2020. http://dx.doi.org/10.1016/c2018-0-02999-0.

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36

Yang, Ping, Michael Kahnert, Bingqiang Sun, Lei Bi, and George Kattawar. Invariant Imbedding T-Matrix Method for Light Scattering by Nonspherical and Inhomogeneous Particles. Elsevier, 2019.

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37

Yang, Ping, Michael Kahnert, Bingqiang Sun, Lei Bi, and George Kattawar. Invariant Imbedding T-Matrix Method for Light Scattering by Nonspherical and Inhomogeneous Particles. Elsevier, 2019.

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38

Engelhard, George Jr. Invariant Measurement. Taylor & Francis Group, 2012.

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39

Bouchaud, Jean-Philippe. Random matrix theory and (big) data analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0006.

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This chapter reviews methods from random matrix theory to extract information about a large signal matrix C (for example, a correlation matrix arising in big data problems), from its noisy observation matrix M. The chapter shows that the replica method can be used to obtain both the spectral density and the overlaps between noise-corrupted eigenvectors and the true ones, for both additive and multiplicative noise. This allows one to construct optimal rotationally invariant estimators of C based on the observation of M alone. This chapter also discusses the case of rectangular correlation matri

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40

Deng, Youjin. Conformal Symmetries & Constrained Critical Phenomena. Delft Univ Pr, 2004.

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41

Liu, Chaohong. Invariant Natural Killer T-Cells: Methods and Protocols. Springer, 2022.

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42

Liu, Chaohong. Invariant Natural Killer T-Cells: Methods and Protocols. Springer, 2021.

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43

Dyson, Freeman. Spectral statistics of unitary ensembles. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.4.

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This article focuses on the use of the orthogonal polynomial method for computing correlation functions, cluster functions, gap probability, Janossy density, and spacing distributions for the eigenvalues of matrix ensembles with unitary-invariant probability law. It first considers the classical families of orthogonal polynomials (Hermite, Laguerre, and Jacobi) and some corresponding unitary ensembles before discussing the statistical properties of N-tuples of real numbers. It then reviews the definitions of basic statistical quantities and demonstrates how their distributions can be made expl

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44

Invariant Sets for Windows. World Scientific Publishing Co Pte Ltd, 1999.

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45

Eilenberger, G. Solitons: Mathematical Methods for Physicists. Springer London, Limited, 2012.

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46

Sorrentino, Alfonso. Action-minimizing Methods in Hamiltonian Dynamics (MN-50). Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691164502.001.0001.

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John Mather's seminal works in Hamiltonian dynamics represent some of the most important contributions to our understanding of the complex balance between stable and unstable motions in classical mechanics. His novel approach—known as Aubry–Mather theory—singles out the existence of special orbits and invariant measures of the system, which possess a very rich dynamical and geometric structure. In particular, the associated invariant sets play a leading role in determining the global dynamics of the system. This book provides a comprehensive introduction to Mather's theory, and can serve as an

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47

Farb, Benson, and Dan Margalit. Presentations and Low-dimensional Homology. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691147949.003.0006.

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This chapter presents explicit computations of the first and second homology groups of the mapping class group. It begins with a simple proof, due to Harer, of the theorem of Mumford, Birman, and Powell; the proof includes the lantern relation, a relation in Mod(S) between seven Dehn twists. It then applies a method from geometric group theory to prove the theorem that Mod(Sɡ) is finitely presentable. It also provides explicit presentations of Mod(Sɡ), including the Wajnryb presentation and the Gervais presentation, and gives a detailed construction of the Euler class, the most basic invariant

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48

An Invariant Approach to Statistical Analysis of Shapes. Chapman & Hall/CRC, 2001.

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49

Rajeev, S. G. Hamiltonian Systems Based on a Lie Algebra. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805021.003.0010.

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There is a remarkable analogy between Euler’s equations for a rigid body and his equations for an ideal fluid. The unifying idea is that of a Lie algebra with an inner product, which is not invariant, on it. The concepts of a vector space, Lie algebra, and inner product are reviewed. A hamiltonian dynamical system is derived from each metric Lie algebra. The Virasoro algebra (famous in string theory) is shown to lead to the KdV equation; and in a limiting case, to the Burgers equation for shocks. A hamiltonian formalism for two-dimensional Euler equations is then developed in detail. A discret

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50

Statistical approaches to measurement invariance. Psychology Press, 2011.

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