Auswahl der wissenschaftlichen Literatur zum Thema „Hilbert spaces“
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Zeitschriftenartikel zum Thema "Hilbert spaces":
Sharma, Sumit Kumar, und Shashank Goel. „Frames in Quaternionic Hilbert Spaces“. Zurnal matematiceskoj fiziki, analiza, geometrii 15, Nr. 3 (25.06.2019): 395–411. http://dx.doi.org/10.15407/mag15.03.395.
Bellomonte, Giorgia, und Camillo Trapani. „Rigged Hilbert spaces and contractive families of Hilbert spaces“. Monatshefte für Mathematik 164, Nr. 3 (08.10.2010): 271–85. http://dx.doi.org/10.1007/s00605-010-0249-1.
Sánchez, Félix Cabello. „Twisted Hilbert spaces“. Bulletin of the Australian Mathematical Society 59, Nr. 2 (April 1999): 177–80. http://dx.doi.org/10.1017/s0004972700032792.
CHITESCU, ION, RAZVAN-CORNEL SFETCU und OANA COJOCARU. „Kothe-Bochner spaces that are Hilbert spaces“. Carpathian Journal of Mathematics 33, Nr. 2 (2017): 161–68. http://dx.doi.org/10.37193/cjm.2017.02.03.
Pisier, Gilles. „Weak Hilbert Spaces“. Proceedings of the London Mathematical Society s3-56, Nr. 3 (Mai 1988): 547–79. http://dx.doi.org/10.1112/plms/s3-56.3.547.
Fabian, M., G. Godefroy, P. Hájek und V. Zizler. „Hilbert-generated spaces“. Journal of Functional Analysis 200, Nr. 2 (Juni 2003): 301–23. http://dx.doi.org/10.1016/s0022-1236(03)00044-2.
Rudolph, Oliver. „Super Hilbert Spaces“. Communications in Mathematical Physics 214, Nr. 2 (November 2000): 449–67. http://dx.doi.org/10.1007/s002200000281.
Ng, Chi-Keung. „Topologized Hilbert spaces“. Journal of Mathematical Analysis and Applications 418, Nr. 1 (Oktober 2014): 108–20. http://dx.doi.org/10.1016/j.jmaa.2014.03.073.
van den Boogaart, Karl Gerald, Juan José Egozcue und Vera Pawlowsky-Glahn. „Bayes Hilbert Spaces“. Australian & New Zealand Journal of Statistics 56, Nr. 2 (Juni 2014): 171–94. http://dx.doi.org/10.1111/anzs.12074.
Schmitt, L. M. „Semidiscrete Hilbert spaces“. Acta Mathematica Hungarica 53, Nr. 1-2 (März 1989): 103–7. http://dx.doi.org/10.1007/bf02170059.
Dissertationen zum Thema "Hilbert spaces":
Wigestrand, Jan. „Inequalities in Hilbert Spaces“. Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9673.
The main result in this thesis is a new generalization of Selberg's inequality in Hilbert spaces with a proof. In Chapter 1 we define Hilbert spaces and give a proof of the Cauchy-Schwarz inequality and the Bessel inequality. As an example of application of the Cauchy-Schwarz inequality and the Bessel inequality, we give an estimate for the dimension of an eigenspace of an integral operator. Next we give a proof of Selberg's inequality including the equality conditions following [Furuta]. In Chapter 2 we give selected facts on positive semidefinite matrices with proofs or references. Then we use this theory for positive semidefinite matrices to study inequalities. First we give a proof of a generalized Bessel inequality following [Akhiezer,Glazman], then we use the same technique to give a new proof of Selberg's inequality. We conclude with a new generalization of Selberg's inequality with a proof. In the last section of Chapter 2 we show how the matrix approach developed in Chapter 2.1 and Chapter 2.2 can be used to obtain optimal frame bounds. We introduce a new notation for frame bounds.
Ameur, Yacin. „Interpolation of Hilbert spaces“. Doctoral thesis, Uppsala universitet, Matematiska institutionen, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-1753.
Ameur, Yacin. „Interpolation of Hilbert spaces /“. Uppsala : Matematiska institutionen, Univ. [distributör], 2001. http://publications.uu.se/theses/91-506-1531-9/.
Panayotov, Ivo. „Conjugate gradient in Hilbert spaces“. Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82402.
Bahmani, Fatemeh. „Ternary structures in Hilbert spaces“. Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/697.
Das, Tushar. „Kleinian Groups in Hilbert Spaces“. Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149579/.
Harris, Terri Joan Mrs. „HILBERT SPACES AND FOURIER SERIES“. CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/244.
Dieuleveut, Aymeric. „Stochastic approximation in Hilbert spaces“. Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE059/document.
The goal of supervised machine learning is to infer relationships between a phenomenon one seeks to predict and “explanatory” variables. To that end, multiple occurrences of the phenomenon are observed, from which a prediction rule is constructed. The last two decades have witnessed the apparition of very large data-sets, both in terms of the number of observations (e.g., in image analysis) and in terms of the number of explanatory variables (e.g., in genetics). This has raised two challenges: first, avoiding the pitfall of over-fitting, especially when the number of explanatory variables is much higher than the number of observations; and second, dealing with the computational constraints, such as when the mere resolution of a linear system becomes a difficulty of its own. Algorithms that take their roots in stochastic approximation methods tackle both of these difficulties simultaneously: these stochastic methods dramatically reduce the computational cost, without degrading the quality of the proposed prediction rule, and they can naturally avoid over-fitting. As a consequence, the core of this thesis will be the study of stochastic gradient methods. The popular parametric methods give predictors which are linear functions of a set ofexplanatory variables. However, they often result in an imprecise approximation of the underlying statistical structure. In the non-parametric setting, which is paramount in this thesis, this restriction is lifted. The class of functions from which the predictor is proposed depends on the observations. In practice, these methods have multiple purposes, and are essential for learning with non-vectorial data, which can be mapped onto a vector in a functional space using a positive definite kernel. This allows to use algorithms designed for vectorial data, but requires the analysis to be made in the non-parametric associated space: the reproducing kernel Hilbert space. Moreover, the analysis of non-parametric regression also sheds some light on the parametric setting when the number of predictors is much larger than the number of observations. The first contribution of this thesis is to provide a detailed analysis of stochastic approximation in the non-parametric setting, precisely in reproducing kernel Hilbert spaces. This analysis proves optimal convergence rates for the averaged stochastic gradient descent algorithm. As we take special care in using minimal assumptions, it applies to numerous situations, and covers both the settings in which the number of observations is known a priori, and situations in which the learning algorithm works in an on-line fashion. The second contribution is an algorithm based on acceleration, which converges at optimal speed, both from the optimization point of view and from the statistical one. In the non-parametric setting, this can improve the convergence rate up to optimality, even inparticular regimes for which the first algorithm remains sub-optimal. Finally, the third contribution of the thesis consists in an extension of the framework beyond the least-square loss. The stochastic gradient descent algorithm is analyzed as a Markov chain. This point of view leads to an intuitive and insightful interpretation, that outlines the differences between the quadratic setting and the more general setting. A simple method resulting in provable improvements in the convergence is then proposed
Boralugoda, Sanath Kumara. „Prox-regular functions in Hilbert spaces“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0006/NQ34740.pdf.
Lapinski, Felicia. „Hilbert spaces and the Spectral theorem“. Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454412.
Bücher zum Thema "Hilbert spaces":
Janson, Svante. Gaussian Hilbert spaces. Cambridge, U.K: Cambridge University Press, 1997.
Debnath, Lokenath. Hilbert spaces with applications. 3. Aufl. Oxford: Academic, 2005.
Mlak, W. Hilbert spaces and operator theory. Dordrecht: Boston, 1991.
Mashreghi, Javad. Hilbert spaces of analytic functions. Providence, R.I: American Mathematical Society, 2010.
Mashreghi, Javad. Hilbert spaces of analytic functions. Providence, R.I: American Mathematical Society, 2010.
Javad, Mashreghi, Ransford Thomas und Seip Kristian 1962-, Hrsg. Hilbert spaces of analytic functions. Providence, R.I: American Mathematical Society, 2010.
Ma, Tsoy-Wo. Banach-Hilbert spaces, vector measures, and group representations. River Edge, NJ: World Scientific, 2002.
Sarason, Donald. Sub-Hardy Hilbert spaces in the unit disk. New York: Wiley, 1994.
Simon, Jacques. Banach, Fréchet, Hilbert and Neumann Spaces. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119426516.
Agler, Jim. Pick interpolation and Hilbert function spaces. Providence, R.I: American Mathematical Society, 2002.
Buchteile zum Thema "Hilbert spaces":
D’Angelo, John P. „Hilbert Spaces“. In Hermitian Analysis, 45–94. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8526-1_2.
Roman, Steven. „Hilbert Spaces“. In Advanced Linear Algebra, 263–90. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4757-2178-2_14.
Ovchinnikov, Sergei. „Hilbert Spaces“. In Universitext, 149–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91512-8_7.
Cicogna, Giampaolo. „Hilbert Spaces“. In Undergraduate Lecture Notes in Physics, 1–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76165-7_1.
Gasquet, Claude, und Patrick Witomski. „Hilbert Spaces“. In Texts in Applied Mathematics, 141–52. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1598-1_16.
Komornik, Vilmos. „Hilbert Spaces“. In Lectures on Functional Analysis and the Lebesgue Integral, 3–54. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-6811-9_1.
Shima, Hiroyuki, und Tsuneyoshi Nakayama. „Hilbert Spaces“. In Higher Mathematics for Physics and Engineering, 73–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/b138494_4.
van der Vaart, Aad W., und Jon A. Wellner. „Hilbert Spaces“. In Weak Convergence and Empirical Processes, 49–51. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2545-2_8.
Brokate, Martin, und Götz Kersting. „Hilbert Spaces“. In Compact Textbooks in Mathematics, 137–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15365-0_12.
Kubrusly, Carlos S. „Hilbert Spaces“. In Elements of Operator Theory, 311–440. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4757-3328-0_5.
Konferenzberichte zum Thema "Hilbert spaces":
RANDRIANANTOANINA, BEATA. „A CHARACTERIZATION OF HILBERT SPACES“. In Proceedings of the Sixth Conference. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704450_0021.
Taddei, Valentina, Luisa Malaguti und Irene Benedetti. „Nonlocal problems in Hilbert spaces“. In The 10th AIMS Conference on Dynamical Systems, Differential Equations and Applications (Madrid, Spain). American Institute of Mathematical Sciences, 2015. http://dx.doi.org/10.3934/proc.2015.0103.
Tang, Wai-Shing. „Biorthogonality and multiwavelets in Hilbert spaces“. In International Symposium on Optical Science and Technology, herausgegeben von Akram Aldroubi, Andrew F. Laine und Michael A. Unser. SPIE, 2000. http://dx.doi.org/10.1117/12.408620.
Pope, Graeme, und Helmut Bolcskei. „Sparse signal recovery in Hilbert spaces“. In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283506.
Małkiewicz, Przemysław. „Physical Hilbert spaces in quantum gravity“. In Proceedings of the MG14 Meeting on General Relativity. WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813226609_0514.
Khimshiashvili, G. „Loop spaces and Riemann-Hilbert problems“. In Geometry and Topology of Manifolds. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2007. http://dx.doi.org/10.4064/bc76-0-19.
Deepshikha, Saakshi Garg, Lalit K. Vashisht und Geetika Verma. „On weaving fusion frames for Hilbert spaces“. In 2017 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2017. http://dx.doi.org/10.1109/sampta.2017.8024363.
Gritsutenko, Stanislav, Elina Biberdorf und Rui Dinis. „On the Sampling Theorem in Hilbert Spaces“. In Computer Graphics and Imaging. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.798-012.
Tuia, Devis, Gustavo Camps-Valls und Manel Martinez-Ramon. „Explicit recursivity into reproducing kernel Hilbert spaces“. In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947266.
SUQUET, CHARLES. „REPRODUCING KERNEL HILBERT SPACES AND RANDOM MEASURES“. In Proceedings of the 5th International ISAAC Congress. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789812835635_0013.
Berichte der Organisationen zum Thema "Hilbert spaces":
Saraivanov, Michael. Quantum Circuit Synthesis using Group Decomposition and Hilbert Spaces. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.1108.
Korezlioglu, H., und C. Martias. Stochastic Integration for Operator Valued Processes on Hilbert Spaces and on Nuclear Spaces. Revision. Fort Belvoir, VA: Defense Technical Information Center, März 1986. http://dx.doi.org/10.21236/ada168501.
Fukumizu, Kenji, Francis R. Bach und Michael I. Jordan. Dimensionality Reduction for Supervised Learning With Reproducing Kernel Hilbert Spaces. Fort Belvoir, VA: Defense Technical Information Center, Mai 2003. http://dx.doi.org/10.21236/ada446572.
Teolis, Anthony. Discrete Representation of Signals from Infinite Dimensional Hilbert Spaces with Application to Noise Suppression and Compression. Fort Belvoir, VA: Defense Technical Information Center, Januar 1993. http://dx.doi.org/10.21236/ada453215.
Salamon, Dietmar. Realization Theory in Hilbert Space. Fort Belvoir, VA: Defense Technical Information Center, Juli 1985. http://dx.doi.org/10.21236/ada158172.
Yao, Jen-Chih. A monotone complementarity problem in Hilbert space. Office of Scientific and Technical Information (OSTI), April 1990. http://dx.doi.org/10.2172/7043013.
Yao, Jen-Chih. A generalized complementarity problem in Hilbert space. Office of Scientific and Technical Information (OSTI), März 1990. http://dx.doi.org/10.2172/6930669.
Cottle, Richard W., und Jen-Chih Yao. Pseudo-Monotone Complementarity Problems in Hilbert Space. Fort Belvoir, VA: Defense Technical Information Center, Juli 1990. http://dx.doi.org/10.21236/ada226477.
Kallianpur, G., und V. Perez-Abreu. Stochastic Evolution Equations with Values on the Dual of a Countably Hilbert Nuclear Space. Fort Belvoir, VA: Defense Technical Information Center, Juli 1986. http://dx.doi.org/10.21236/ada174876.
Monrad, D., und W. Philipp. Nearby Variables with Nearby Conditional Laws and a Strong Approximation Theorem for Hilbert Space Valued Martingales. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada225992.