Algebraic Study of Systems of Partial Differential Equations Mémoires De La S
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8.5 Least Squares Solutions to Inconsistent Systems
8.5 Least Squares Solutions to Inconsistent Systems Performance Criterion: 8. (f) Find the least-squares approximation to the solution of an inconsistent system of equations. Solve a problem using least-squares approximation. (g) Give the least squares error and least squares error vector for a least squares approximation to a solution to a system of equations. Recall that an inconsistent system is one for which there is no solution. Often we wish to solve inconsistent systems and it is just not acceptable to have no solution. In those cases we can find some vector (whose components are the values we are trying to find when attempting to solve the system) that is “closer to being a solution” than all other vectors. The theory behind this process is part of the second term of this course, but we now have enough knowledge to find such a vector in a “cookbook” manner. Suppose that we have a system of equations Ax = b. Pause for a moment to reflect on what we know and what we are trying to find when solving such a system: We have a system of linear equations, and the entries of A are the coefficients of all the equations. The vector b is the vector whose components are the right sides of all the equations, and the vector x is the vector whose components are the unknown values of the variables we are trying to find. So we know A and b and we are trying to find x. If A is invertible, the solution vector x is given by x = A−1 b. -
Fifty Years of Mathematics with Masaki Kashiwara
Introduction D-modules SKK R-H correspondence Microlocal sheaf theory Others Fifty years of Mathematics with Masaki Kashiwara Pierre Schapira Sorbonne Universit´e,Paris, France ICM 2018 Rio de Janeiro, August 1st 1 / 26 Introduction D-modules SKK R-H correspondence Microlocal sheaf theory Others Before Kashiwara Masaki Kashiwara was a student of Mikio Sato and the story begins long ago, in the late fifties, when Sato created a new branch of mathematics, now called \Algebraic Analysis" by publishing his papers on hyperfunction theory and developed his vision of analysis and linear partial differential equations (LPDE) in a series of lectures at Tokyo University. 2 / 26 Introduction D-modules SKK R-H correspondence Microlocal sheaf theory Others Hyperfunctions on a real analytic manifold M are cohomology classes supported by M of the sheaf OX of holomorphic functions on a complexification X of M. In these old times, trying to understand real phenomena by complexifying a real manifold and looking at what happens in the complex domain was a totally new idea. The use of cohomology of sheaves in analysis was definitely revolutionary. 3 / 26 Introduction D-modules SKK R-H correspondence Microlocal sheaf theory Others Figure : ¿藤幹d and Á原正樹 4 / 26 Introduction D-modules SKK R-H correspondence Microlocal sheaf theory Others Kashiwara's thesis and D-module theory Then came Masaki Kashiwara. In his master's thesis, Tokyo University, 1970, Kashiwara establishes the foundations of analytic D-module theory, a theory which is now a fundamental tool in many branches of mathematics, from number theory to mathematical physics. (Soon after and independently, Joseph Bernstein developed a similar theory in the algebraic setting.) 5 / 26 Introduction D-modules SKK R-H correspondence Microlocal sheaf theory Others On a complex manifold X , one has the non commutative sheaf of rings DX of holomorphic partial differential operators. -
Algebraic Analysis of Rotation Data
11 : 2 2020 Algebraic Statistics ALGEBRAIC ANALYSIS OF ROTATION DATA MICHAEL F. ADAMER, ANDRÁS C. LORINCZ˝ , ANNA-LAURA SATTELBERGER AND BERND STURMFELS msp Algebraic Statistics Vol. 11, No. 2, 2020 https://doi.org/10.2140/astat.2020.11.189 msp ALGEBRAIC ANALYSIS OF ROTATION DATA MICHAEL F. ADAMER, ANDRÁS C. LORINCZ˝ , ANNA-LAURA SATTELBERGER AND BERND STURMFELS We develop algebraic tools for statistical inference from samples of rotation matrices. This rests on the theory of D-modules in algebraic analysis. Noncommutative Gröbner bases are used to design numerical algorithms for maximum likelihood estimation, building on the holonomic gradient method of Sei, Shibata, Takemura, Ohara, and Takayama. We study the Fisher model for sampling from rotation matrices, and we apply our algorithms to data from the applied sciences. On the theoretical side, we generalize the underlying equivariant D-modules from SO.3/ to arbitrary Lie groups. For compact groups, our D-ideals encode the normalizing constant of the Fisher model. 1. Introduction Many of the multivariate functions that arise in statistical inference are holonomic. Being holonomic roughly means that the function is annihilated by a system of linear partial differential operators with polynomial coefficients whose solution space is finite-dimensional. Such a system of PDEs can be written as a left ideal in the Weyl algebra, or D-ideal, for short. This representation allows for the application of algebraic geometry and algebraic analysis, including the use of computational tools, such as Gröbner bases in the Weyl algebra[28; 30]. This important connection between statistics and algebraic analysis was first observed by a group of scholars in Japan, and it led to their development of the holonomic gradient method (HGM) and the holonomic gradient descent (HGD). -
Lecture Notes in Mathematics a Collection of Informal Reports and Seminars Edited by A
Lecture Notes in Mathematics A collection of informal reports and seminars Edited by A. Dold, Heidelberg and B. Eckmann, Z(Jrich 287 Hyperfunctions and Pseudo-Differential Equations Proceedings of a Conference at Katata, 1971 Edited by Hikosaburo Komatsu, University of Tokyo, Tokyo/Japan Springer-Verlag Berlin. Heidelberg New York 1973 AMS Subject Classifications 1970): 35 A 05, 35 A 20, 35 D 05, 35 D 10, 35 G 05, 35 N t0, 35 S 05, 46F 15 ISBN 3-540-06218-1 Springer-Verlag Berlin- Heidelberg" New York ISBN 0-387-06218-1 Springer-Verlag New York • Heidelberg • Berlin This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically those of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machine or similar means, and storage in data banks. Under § 54 of the German Copyright Law where copies are made for other than private use, a fee is payable to the publisher, the amount of the fee to be determined by agreement with the publisher. © by Springer Verlag Berlin . Heidelberg 1973. Library of Congress Catalog Card Number 72-88782. Printed in Germany. Offsetdruck: Jutius Beltz, Hemsbach/Bergstr. Dedicated to the memory of the late professor Andre MARTINEAU, who had originally planned to attend this conference. He appreciated the importance of hyperfunctions for the first time and has made the most profound contributions to the theory of hyperfunctions. LIST OF PARTICIPANTS Y. AKIZUKI (Gunma University) H. HIRONAKA (Harvard University) A. KANEKO (University of Tokyo) M. KASHIWARA (RIMS, Kyoto University) T. -
Linear Algebra I
Linear Algebra I Gregg Waterman Oregon Institute of Technology c 2016 Gregg Waterman This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. The essence of the license is that You are free: to Share to copy, distribute and transmit the work • to Remix to adapt the work • Under the following conditions: Attribution You must attribute the work in the manner specified by the author (but not in • any way that suggests that they endorse you or your use of the work). Please contact the author at [email protected] to determine how best to make any attribution. Noncommercial You may not use this work for commercial purposes. • Share Alike If you alter, transform, or build upon this work, you may distribute the resulting • work only under the same or similar license to this one. With the understanding that: Waiver Any of the above conditions can be waived if you get permission from the copyright • holder. Public Domain Where the work or any of its elements is in the public domain under applicable • law, that status is in no way affected by the license. Other Rights In no way are any of the following rights affected by the license: • Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; ⋄ The author’s moral rights; ⋄ Rights other persons may have either in the work itself or in how the work is used, such as ⋄ publicity or privacy rights. Notice For any reuse or distribution, you must make clear to others the license terms of this • work. -
MASAKI KASHIWARA PIERRE SCHAPIRA Ind-Sheaves
Astérisque MASAKI KASHIWARA PIERRE SCHAPIRA Ind-sheaves Astérisque, tome 271 (2001) <http://www.numdam.org/item?id=AST_2001__271__R1_0> © Société mathématique de France, 2001, tous droits réservés. L’accès aux archives de la collection « Astérisque » (http://smf4.emath.fr/ Publications/Asterisque/) implique l’accord avec les conditions générales d’uti- lisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ ASTÉRISQUE 271 IND-SHEAVES Masaki Kashiwara Pierre Schapira Société Mathématique de France 2001 M. Kashiwara Research Institute for Mathematical Sciences, Kyoto University, Kyoto 606-8502, Japan. E-mail : [email protected] P. Schapira Institut de Mathématiques, Analyse Algébrique, Université P & M Curie, Case 82, 4, place Jussieu F-75252, Paris Cedex 05, France. E-mail : [email protected] Url : http://www.math.jussieu.fr/"schapira 2000 Mathematics Subject Classification. — 18F20, 32C38, 32S60. Key words and phrases. — Sheaves, Grothendieck topologies, ind-objects, D-modules, moderate cohomology, integral transforms. The first named author benefits by a "Chaire Internationale de Recherche Blaise Pascal de l'Etat et de la Région d'Ile-de-France, gérée par la Fondation de l'Ecole Normale Supérieure". IND-SHEAVES Masaki Kashiwara, Pierre Schapira Abstract. — Sheaf theory is not well suited to the study of various objects in Analysis which are not defined by local properties. The aim of this paper is to show that it is possible to overcome this difficulty by enlarging the category of sheaves to that of ind-sheaves, and by extending to ind-sheaves the machinery of sheaves. -
Mikio Sato, a Visionary of Mathematics, Volume 54, Number 2
Mikio Sato, a Visionary of Mathematics Pierre Schapira Like singularities in mathematics and physics, ideas People were essentially looking for existence propagate, and the speed of propagation depends theorems for linear partial differential equations highly on the energy put into promoting them. (PDE), and most of the proofs were reduced to Mikio Sato did not spend a great deal of time or finding “the right functional space”, to prove some energy popularizing his ideas. We can hope that a priori estimate and apply the Hahn-Banach his receipt of the 2002/2003 Wolf Prize1 will help theorem. make better known his deep work, which is perhaps It was in this environment that Mikio Sato too original to be immediately accepted. Sato does defined hyperfunctions in 1959–1960 as boundary not write a lot, does not communicate easily, and values of holomorphic functions, a discovery that attends very few meetings. But he invented a new allowed him to obtain a position at Tokyo Univer- way of doing analysis, “Algebraic Analysis”, and sity thanks to the clever patronage of Shokichi created a school, “the Kyoto school”. Iyanaga, an exceptionally open-minded person 2 Born in 1928 , Sato became known in mathe- and a great friend of French culture. Next, Sato matics only in 1959–1960 with his theory of spent two years in the USA, in Princeton, where he hyperfunctions. Indeed, his studies had been unsuccessfully tried to convince André Weil of the seriously disrupted by the war, particularly by relevance of his cohomological approach to the bombing of Tokyo. After his family home analysis. -
Underdetermined and Overdetermined Linear Algebraic Systems
Underdetermined and Overdetermined Linear Algebraic Systems ES100 March 1, 1999 T.S. Whitten Objectives ● Define underdetermined systems ● Define overdetermined systems ● Least Squares Examples Review B 500 N = − ° = ∑ Fx 0; 500 FBC sin 45 0 = ° − = ∑ Fy 0; FBC cos 45 FBA 0 FBC FBA − sin 45° 0 F − 500 ⋅ BC = ° 1cos42445 4341 1F2BA3 0 coefficients variables Review cont. − sin 45° 0 F − 500 ⋅ BC = ° 1cos42445 4341 1F2BA3 0 coefficients variables The system of matrices above is of the form: Ax = b and can be solved using MATLAB left division thus, x = A\b × results in a 1 2 matrix of values for FBC and FBA Review Summary ● A system of two Equations and two unknowns may yield a unique solution. ● The exception is when the determinant of A is equal to zero. Then the system is said to be singular. ● The left division operator will solve the linear system in one step by combining two matrix operations ●A\B is equivalent to A-1*B Graphical Representation of Unique vs. Singular Systems unique solution y singular system x Underdetermined Systems ● A system of linear equations is may be undetermined if; 1 The determinant of A is equal to zero A = 0 2 The matrix A is not square, i.e. the are more unknowns than there are equations + + = x x 3y 2z 2 1 3 2 2 ⋅ y = x + y + z = 4 1 1 1 4 z Overdetermined Systems ● The converse of an underdetermined system is an overdetermined system where there are more equations than there are variables ● This situation arises frequently in engineering. -
Solutions of Overdetermined Linear Problems (Least Square Problems)
Chapter 3 Solutions of overdetermined linear problems (Least Square problems) 1. Over-constrained problems See Chapter 11 from the textbook 1.1. Definition In the previous chapter, we focused on solving well-defined linear problems de- fined by m linear equations for m unknowns, put into a compact matrix-vector form Ax = b with A an m m square matrix, and b and x m long column vectors. We focussed on using⇥ direct methods to seek exact solutions− to such well-defined linear systems, which exist whenever A is nonsingular. We will re- visit these problems later this quarter when we learn about iterative methods. In this chapter, we look at a more general class of problems defined by so- called overdetermined systems – systems with a larger numbers of equations (m) than unknowns (n): this time, we have Ax = b with A an m n matrix with m>n, x an n-long column vector and b an m long column vector.⇥ This time there generally are no exact solutions to the problem.− Rather we now want to find approximate solutions to overdetermined linear systems which minimize the residual error E = r = b Ax (3.1) || || || − || using some norm. The vector r = b Ax is called the residual vector. − Any choice of norm would generally work, although, in practice, we prefer to use the Euclidean norm (i.e., the 2-norm) which is more convenient for nu- merical purposes, as they provide well-established relationships with the inner product and orthogonality, as well as its smoothness and convexity (we shall see later). -
Math 253 - Homework 2 Due in Class on Wednesday, February 19
Math 253 - Homework 2 Due in class on Wednesday, February 19 Write your answers clearly and carefully, being sure to emphasize your answer and the key steps of your work. You may work with others in this class, but the solutions handed in must be your own. If you work with someone or get help from another source, give a brief citation on each problem for which that is the case. Part I While you are expected to complete all of these problems, do not hand in the problems in Part I. You are encouraged to write complete solutions and to discuss them with me or your peers. As extra motivation, some of these problems will appear on the weekly quizzes. 1. Practice Problems: (a) Section 1.3: 1, 2 2. Exercises: (a) Section 1.3: 1-13 odd, 19, 21, 25, 27, 29, 33 Part II Hand in each problem separately, individually stapled if necessary. Please keep all problems together with a paper clip. n 1. The center of mass of v1;:::; vk in R , with a mass of mi at vi, for i = 1; : : : ; k and total mass m = m1 + ··· + mk, is given by m v + ··· + m v m m v¯ = 1 1 k k = 1 v + ··· + k v : m m 1 m k n The centroid, which can be thought of as the geometric center, of v1;:::; vk in R is the center of mass with mi = 1 for each i = 1; : : : ; k, i.e. v + ··· + v 1 1 c¯ = 1 k = v + ··· + v : k k 1 k k 243 2−13 2 1 3 2−53 2 1 3 Pk (a) Let v1 = 415 ; v2 = 4 0 5 ; v3 = 4 3 5 ; v4 = 4 0 5 ; v5 = 4−45 : Let uk = i=1 vi. -
27 Oct 2008 Masaki Kashiwara and Algebraic Analysis
Masaki Kashiwara and Algebraic Analysis Pierre Schapira Abstract This paper is based on a talk given in honor of Masaki Kashiwara’s 60th birthday, Kyoto, June 27, 2007. It is a brief overview of his main contributions in the domain of microlocal and algebraic analysis. It is a great honor to present here some aspects of the work of Masaki Kashiwara. Recall that Masaki’s work covers many fields of mathematics, algebraic and microlocal analysis of course, but also representation theory, Hodge the- ory, integrable systems, quantum groups and so on. Also recall that Masaki had many collaborators, among whom Daniel Barlet, Jean-Luc Brylinski, Et- surio Date, Ryoji Hotta, Michio Jimbo, Seok-Jin Kang, Takahiro Kawai, Tet- suji Miwa, Kiyosato Okamoto, Toshio Oshima, Mikio Sato, myself, Toshiyuki Tanisaki and Mich`ele Vergne. In each of the domain he approached, Masaki has given essential contri- butions and made important discoveries, such as, for example, the existence of crystal bases in quantum groups. But in this talk, I will restrict myself to describe some part of his work related to microlocal and algebraic analysis. The story begins long ago, in the early sixties, when Mikio Sato created a new branch of mathematics now called “Algebraic Analysis”. In 1959/60, arXiv:0810.4875v1 [math.HO] 27 Oct 2008 M. Sato published two papers on hyperfunction theory [24] and then devel- oped his vision of analysis and linear partial differential equations in a series of lectures at Tokyo University (see [1]). If M is a real analytic manifold and X is a complexification of M, hyperfunctions on M are cohomology classes supported by M of the sheaf X of holomorphic functions on X. -
Investigating an Overdetermined System of Linear Equations by Using Convex Functions
Hacettepe Journal of Mathematics and Statistics Volume 46 (5) (2017), 865 874 Investigating an overdetermined system of linear equations by using convex functions Zlatko Pavi¢ ∗ y and Vedran Novoselac z Abstract The paper studies the application of convex functions in order to prove the existence of optimal solutions of an overdetermined system of lin- ear equations. The study approaches the problem by using even convex functions instead of projections. The research also relies on some spe- cial properties of unbounded convex sets, and the lower level sets of continuous functions. Keywords: overdetermined system, convex function, global minimum. 2000 AMS Classication: 15A06, 26B25. Received : 30.08.2016 Accepted : 19.12.2016 Doi : 10.15672/ HJMS.2017.423 1. Introduction We consider a system of m linear equations with n unknowns over the eld of real numbers given by a11x1 + ::: + a1nxn = b1 . (1.1) . .. : am1x1+ ::: + amnxn = bm Including the matrices 2 a11 : : : a1n 3 2 x1 3 2 b1 3 . (1.2) 6 . .. 7 6 . 7 6 . 7 A = 4 . 5 ; x = 4 . 5 ; b = 4 . 5 ; am1 : : : amn xn bm the given system gets the matrix form (1.3) Ax = b: ∗Department of Mathematics, Mechanical Engineering Faculty in Slavonski Brod, University of Osijek, Croatia, Email: [email protected] yCorresponding Author. zDepartment of Mathematics, Mechanical Engineering Faculty in Slavonski Brod, University of Osijek, Croatia, Email: [email protected] 866 n m Identifying the matrix A with a linear operator from R to R , the column matrix x n m with a vector of R , and the column matrices Ax and b with vectors of R , the given system takes the operator form.