Mathematicians of Gaussian Elimination Joseph F
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Arxiv:2003.06292V1 [Math.GR] 12 Mar 2020 Eggnrtr N Ignlmti.Tedaoa Arxi Matrix Diagonal the Matrix
ALGORITHMS IN LINEAR ALGEBRAIC GROUPS SUSHIL BHUNIA, AYAN MAHALANOBIS, PRALHAD SHINDE, AND ANUPAM SINGH ABSTRACT. This paper presents some algorithms in linear algebraic groups. These algorithms solve the word problem and compute the spinor norm for orthogonal groups. This gives us an algorithmic definition of the spinor norm. We compute the double coset decompositionwith respect to a Siegel maximal parabolic subgroup, which is important in computing infinite-dimensional representations for some algebraic groups. 1. INTRODUCTION Spinor norm was first defined by Dieudonné and Kneser using Clifford algebras. Wall [21] defined the spinor norm using bilinear forms. These days, to compute the spinor norm, one uses the definition of Wall. In this paper, we develop a new definition of the spinor norm for split and twisted orthogonal groups. Our definition of the spinornorm is rich in the sense, that itis algorithmic in nature. Now one can compute spinor norm using a Gaussian elimination algorithm that we develop in this paper. This paper can be seen as an extension of our earlier work in the book chapter [3], where we described Gaussian elimination algorithms for orthogonal and symplectic groups in the context of public key cryptography. In computational group theory, one always looks for algorithms to solve the word problem. For a group G defined by a set of generators hXi = G, the problem is to write g ∈ G as a word in X: we say that this is the word problem for G (for details, see [18, Section 1.4]). Brooksbank [4] and Costi [10] developed algorithms similar to ours for classical groups over finite fields. -
Orthogonal Reduction 1 the Row Echelon Form -.: Mathematical
MATH 5330: Computational Methods of Linear Algebra Lecture 9: Orthogonal Reduction Xianyi Zeng Department of Mathematical Sciences, UTEP 1 The Row Echelon Form Our target is to solve the normal equation: AtAx = Atb ; (1.1) m×n where A 2 R is arbitrary; we have shown previously that this is equivalent to the least squares problem: min jjAx−bjj : (1.2) x2Rn t n×n As A A2R is symmetric positive semi-definite, we can try to compute the Cholesky decom- t t n×n position such that A A = L L for some lower-triangular matrix L 2 R . One problem with this approach is that we're not fully exploring our information, particularly in Cholesky decomposition we treat AtA as a single entity in ignorance of the information about A itself. t m×m In particular, the structure A A motivates us to study a factorization A=QE, where Q2R m×n is orthogonal and E 2 R is to be determined. Then we may transform the normal equation to: EtEx = EtQtb ; (1.3) t m×m where the identity Q Q = Im (the identity matrix in R ) is used. This normal equation is equivalent to the least squares problem with E: t min Ex−Q b : (1.4) x2Rn Because orthogonal transformation preserves the L2-norm, (1.2) and (1.4) are equivalent to each n other. Indeed, for any x 2 R : jjAx−bjj2 = (b−Ax)t(b−Ax) = (b−QEx)t(b−QEx) = [Q(Qtb−Ex)]t[Q(Qtb−Ex)] t t t t t t t t 2 = (Q b−Ex) Q Q(Q b−Ex) = (Q b−Ex) (Q b−Ex) = Ex−Q b : Hence the target is to find an E such that (1.3) is easier to solve. -
Spinoza and the Sciences Boston Studies in the Philosophy of Science
SPINOZA AND THE SCIENCES BOSTON STUDIES IN THE PHILOSOPHY OF SCIENCE EDITED BY ROBERT S. COHEN AND MARX W. WARTOFSKY VOLUME 91 SPINOZA AND THE SCIENCES Edited by MARJORIE GRENE University of California at Davis and DEBRA NAILS University of the Witwatersrand D. REIDEL PUBLISHING COMPANY A MEMBER OF THE KLUWER ~~~.'~*"~ ACADEMIC PUBLISHERS GROUP i\"lI'4 DORDRECHT/BOSTON/LANCASTER/TOKYO Library of Congress Cataloging-in-Publication Data Main entry under title: Spinoza and the sciences. (Boston studies in the philosophy of science; v. 91) Bibliography: p. Includes index. 1. Spinoza, Benedictus de, 1632-1677. 2. Science- Philosophy-History. 3. Scientists-Netherlands- Biography. I. Grene, Marjorie Glicksman, 1910- II. Nails, Debra, 1950- Ill. Series. Q174.B67 vol. 91 OOI'.Ols 85-28183 101 43.S725J 100 I J ISBN-13: 978-94-010-8511-3 e-ISBN-13: 978-94-009-4514-2 DOl: 10.1007/978-94-009-4514-2 Published by D. Reidel Publishing Company, P.O. Box 17, 3300 AA Dordrecht, Holland. Sold and distributed in the U.S.A. and Canada by Kluwer Academic Publishers, 101 Philip Drive, Assinippi Park, Norwell, MA 02061, U.S.A. In all other countries, sold and distributed by Kluwer Academic Publishers Group, P.O. Box 322, 3300 AH Dordrecht, Holland. 2-0490-150 ts All Rights Reserved © 1986 by D. Reidel Publishing Company Softcover reprint of the hardcover 1st edition 1986 and copyright holders as specified on appropriate pages within No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without written permission from the copyright owner FROM SPINOZA'S LETTER TO OLDENBURG, RIJNSBURG, APRIL, 1662 (Photo by permission of Berend Kolk) TABLE OF CONTENTS ACKNOWLEDGEMENTS ix MARJORIE GRENE I Introduction xi 1. -
Newton.Indd | Sander Pinkse Boekproductie | 16-11-12 / 14:45 | Pag
omslag Newton.indd | Sander Pinkse Boekproductie | 16-11-12 / 14:45 | Pag. 1 e Dutch Republic proved ‘A new light on several to be extremely receptive to major gures involved in the groundbreaking ideas of Newton Isaac Newton (–). the reception of Newton’s Dutch scholars such as Willem work.’ and the Netherlands Jacob ’s Gravesande and Petrus Prof. Bert Theunissen, Newton the Netherlands and van Musschenbroek played a Utrecht University crucial role in the adaption and How Isaac Newton was Fashioned dissemination of Newton’s work, ‘is book provides an in the Dutch Republic not only in the Netherlands important contribution to but also in the rest of Europe. EDITED BY ERIC JORINK In the course of the eighteenth the study of the European AND AD MAAS century, Newton’s ideas (in Enlightenment with new dierent guises and interpre- insights in the circulation tations) became a veritable hype in Dutch society. In Newton of knowledge.’ and the Netherlands Newton’s Prof. Frans van Lunteren, sudden success is analyzed in Leiden University great depth and put into a new perspective. Ad Maas is curator at the Museum Boerhaave, Leiden, the Netherlands. Eric Jorink is researcher at the Huygens Institute for Netherlands History (Royal Dutch Academy of Arts and Sciences). / www.lup.nl LUP Newton and the Netherlands.indd | Sander Pinkse Boekproductie | 16-11-12 / 16:47 | Pag. 1 Newton and the Netherlands Newton and the Netherlands.indd | Sander Pinkse Boekproductie | 16-11-12 / 16:47 | Pag. 2 Newton and the Netherlands.indd | Sander Pinkse Boekproductie | 16-11-12 / 16:47 | Pag. -
Implementation of Gaussian- Elimination
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-5 Issue-11, April 2016 Implementation of Gaussian- Elimination Awatif M.A. Elsiddieg Abstract: Gaussian elimination is an algorithm for solving systems of linear equations, can also use to find the rank of any II. PIVOTING matrix ,we use Gaussian Jordan elimination to find the inverse of a non singular square matrix. This work gives basic concepts The objective of pivoting is to make an element above or in section (1) , show what is pivoting , and implementation of below a leading one into a zero. Gaussian elimination to solve a system of linear equations. The "pivot" or "pivot element" is an element on the left Section (2) we find the rank of any matrix. Section (3) we use hand side of a matrix that you want the elements above and Gaussian elimination to find the inverse of a non singular square matrix. We compare the method by Gauss Jordan method. In below to be zero. Normally, this element is a one. If you can section (4) practical implementation of the method we inherit the find a book that mentions pivoting, they will usually tell you computation features of Gaussian elimination we use programs that you must pivot on a one. If you restrict yourself to the in Matlab software. three elementary row operations, then this is a true Keywords: Gaussian elimination, algorithm Gauss, statement. However, if you are willing to combine the Jordan, method, computation, features, programs in Matlab, second and third elementary row operations, you come up software. -
Naïve Gaussian Elimination Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America [email protected]
nbm_sle_sim_naivegauss.nb 1 Naïve Gaussian Elimination Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America [email protected] Introduction One of the most popular numerical techniques for solving simultaneous linear equations is Naïve Gaussian Elimination method. The approach is designed to solve a set of n equations with n unknowns, [A][X]=[C], where A nxn is a square coefficient matrix, X nx1 is the solution vector, and C nx1 is the right hand side array. Naïve Gauss consists of two steps: 1) Forward Elimination: In this step, the unknown is eliminated in each equation starting with the first@ D equation. This way, the equations @areD "reduced" to one equation and@ oneD unknown in each equation. 2) Back Substitution: In this step, starting from the last equation, each of the unknowns is found. To learn more about Naïve Gauss Elimination as well as the pitfall's of the method, click here. A simulation of Naive Gauss Method follows. Section 1: Input Data Below are the input parameters to begin the simulation. This is the only section that requires user input. Once the values are entered, Mathematica will calculate the solution vector [X]. èNumber of equations, n: n = 4 4 è nxn coefficient matrix, [A]: nbm_sle_sim_naivegauss.nb 2 A = Table 1, 10, 100, 1000 , 1, 15, 225, 3375 , 1, 20, 400, 8000 , 1, 22.5, 506.25, 11391 ;A MatrixForm 1 10@88 100 1000 < 8 < 18 15 225 3375< 8 <<D êê 1 20 400 8000 ji 1 22.5 506.25 11391 zy j z j z j z j z è nx1 rightj hand side array, [RHS]: z k { RHS = Table 227.04, 362.78, 517.35, 602.97 ; RHS MatrixForm 227.04 362.78 @8 <D êê 517.35 ji 602.97 zy j z j z j z j z j z k { Section 2: Naïve Gaussian Elimination Method The following sections divide Naïve Gauss elimination into two steps: 1) Forward Elimination 2) Back Substitution To conduct Naïve Gauss Elimination, Mathematica will join the [A] and [RHS] matrices into one augmented matrix, [C], that will facilitate the process of forward elimination. -
The Newton-Leibniz Controversy Over the Invention of the Calculus
The Newton-Leibniz controversy over the invention of the calculus S.Subramanya Sastry 1 Introduction Perhaps one the most infamous controversies in the history of science is the one between Newton and Leibniz over the invention of the infinitesimal calculus. During the 17th century, debates between philosophers over priority issues were dime-a-dozen. Inspite of the fact that priority disputes between scientists were ¡ common, many contemporaries of Newton and Leibniz found the quarrel between these two shocking. Probably, what set this particular case apart from the rest was the stature of the men involved, the significance of the work that was in contention, the length of time through which the controversy extended, and the sheer intensity of the dispute. Newton and Leibniz were at war in the later parts of their lives over a number of issues. Though the dispute was sparked off by the issue of priority over the invention of the calculus, the matter was made worse by the fact that they did not see eye to eye on the matter of the natural philosophy of the world. Newton’s action-at-a-distance theory of gravitation was viewed as a reversion to the times of occultism by Leibniz and many other mechanical philosophers of this era. This intermingling of philosophical issues with the priority issues over the invention of the calculus worsened the nature of the dispute. One of the reasons why the dispute assumed such alarming proportions and why both Newton and Leibniz were anxious to be considered the inventors of the calculus was because of the prevailing 17th century conventions about priority and attitude towards plagiarism. -
Berkeley's Case Against Realism About Dynamics
Lisa Downing [Published in Berkeley’s Metaphysics, ed. Muehlmann, Penn State Press 1995, 197-214. Turbayne Essay Prize winner, 1992.] Berkeley's case against realism about dynamics While De Motu, Berkeley's treatise on the philosophical foundations of mechanics, has frequently been cited for the surprisingly modern ring of certain of its passages, it has not often been taken as seriously as Berkeley hoped it would be. Even A.A. Luce, in his editor's introduction to De Motu, describes it as a modest work, of limited scope. Luce writes: The De Motu is written in good, correct Latin, but in construction and balance the workmanship falls below Berkeley's usual standards. The title is ambitious for so brief a tract, and may lead the reader to expect a more sustained argument than he will find. A more modest title, say Motion without Matter, would fitly describe its scope and content. Regarded as a treatise on motion in general, it is a slight and disappointing work; but viewed from a narrower angle, it is of absorbing interest and high importance. It is the application of immaterialism to contemporary problems of motion, and should be read as such. ...apart from the Principles the De Motu would be nonsense.1 1The Works of George Berkeley, Bishop of Cloyne, ed. A.A. Luce and T.E. Jessop (London: Thomas Nelson and Sons, 1948-57), 4: 3-4. In this paper, all references to Berkeley are to the Luce-Jessop edition. Quotations from De Motu are taken from Luce's translation. I use the following abbreviations for Berkeley’s works: PC Philosophical Commentaries PHK-I Introduction to The Principles of Human Knowledge PHK The Principles of Human Knowledge DM De Motu A Alciphron TVV The Theory of Vision Vindicated and Explained S Siris 1 There are good general reasons to think, however, that Berkeley's aims in writing the book were as ambitious as the title he chose. -
Linear Programming
Linear Programming Lecture 1: Linear Algebra Review Lecture 1: Linear Algebra Review Linear Programming 1 / 24 1 Linear Algebra Review 2 Linear Algebra Review 3 Block Structured Matrices 4 Gaussian Elimination Matrices 5 Gauss-Jordan Elimination (Pivoting) Lecture 1: Linear Algebra Review Linear Programming 2 / 24 columns rows 2 3 a1• 6 a2• 7 = a a ::: a = 6 7 •1 •2 •n 6 . 7 4 . 5 am• 2 3 2 T 3 a11 a21 ::: am1 a•1 T 6 a12 a22 ::: am2 7 6 a•2 7 AT = 6 7 = 6 7 = aT aT ::: aT 6 . .. 7 6 . 7 1• 2• m• 4 . 5 4 . 5 T a1n a2n ::: amn a•n Matrices in Rm×n A 2 Rm×n 2 3 a11 a12 ::: a1n 6 a21 a22 ::: a2n 7 A = 6 7 6 . .. 7 4 . 5 am1 am2 ::: amn Lecture 1: Linear Algebra Review Linear Programming 3 / 24 rows 2 3 a1• 6 a2• 7 = 6 7 6 . 7 4 . 5 am• 2 3 2 T 3 a11 a21 ::: am1 a•1 T 6 a12 a22 ::: am2 7 6 a•2 7 AT = 6 7 = 6 7 = aT aT ::: aT 6 . .. 7 6 . 7 1• 2• m• 4 . 5 4 . 5 T a1n a2n ::: amn a•n Matrices in Rm×n A 2 Rm×n columns 2 3 a11 a12 ::: a1n 6 a21 a22 ::: a2n 7 A = 6 7 = a a ::: a 6 . .. 7 •1 •2 •n 4 . 5 am1 am2 ::: amn Lecture 1: Linear Algebra Review Linear Programming 3 / 24 2 3 2 T 3 a11 a21 ::: am1 a•1 T 6 a12 a22 ::: am2 7 6 a•2 7 AT = 6 7 = 6 7 = aT aT ::: aT 6 . -
Newton and Kant on Absolute Space: from Theology to Transcendental Philosophy
Newton and Kant on Absolute Space: From Theology to Transcendental Philosophy Michael Friedman Abstract I argue that Einstein’s creation of both special and general relativity instantiates Reichenbach’s conception of the relativized a priori. I do this by show- ing how the original Kantian conception actually contributes to the development of Einstein’s theories through the intervening philosophical and scientific work of Helmholtz, Mach, and Poincaré. In my previous work on Newton and Kant I have primarily emphasized methodo- logical issues: why Kant takes the Newtonian Laws of Motion (as well as certain related propositions of what he calls “pure natural science”) as synthetic a priori constitutive principles rather than mere empirical laws, and how this point is inti- mately connected, in turn, with Kant’s conception of absolute space as a regulative idea of reason – as the limit point of an empirical constructive procedure rather than a self-subsistent “container” existing prior to and independently of all perceptible matter. I have also argued that these methodological differences explain the circum- stance that Kant, unlike Newton, asserts that gravitational attraction must be con- ceived as an “action at a distance through empty space,” and even formulates a (rare) criticism of Newton for attempting to leave the question of the “true cause” of gravitational attraction entirely open. In this paper I emphasize the importance of metaphysical and theological issues – about God, his creation of the material world in space, and the consequences different views of such creation have for the metaphysical foundations of physics. I argue, in particular, that Kant’s differences with Newton over these issues constitute an essential part of his radical transforma- tion of the very meaning of metaphysics as practiced by his predecessors. -
Determinism Is False
%&5&3.*/*4. Barry Loewer %FUFSNJOJTNJTBDPOUJOHFOUNFUBQIZTJDBMDMBJNBCPVUUIFGVOEBNFOUBMOBUVSBMMBXT UIBUIPMEJOUIFVOJWFSTF*UTBZT The natural laws and the way things are at time t determine the way things will be at later times. 5IF NBUIFNBUJDJBO 1JFSSF4JNPO -BQMBDF FYQSFTTFE IJT CFMJFG UIBU EFUFS- minism is true this way: 8F PVHIU UP SFHBSE UIF QSFTFOU TUBUF PG UIF VOJWFSTF BT UIF FGGFDU PG JUT antecedent state and as the cause of the state that is to follow. An intel- MJHFODF LOPXJOH BMM UIF GPSDFT BDUJOH JO OBUVSF BU B HJWFO JOTUBOU BT XFMM as the momentary positions of all things in the universe, would be able to comprehend in one single formula the motions of the largest bodies as well as the lightest atoms in the world, provided that its intellect were suf!ciently QPXFSGVMUPTVCKFDUBMMEBUBUPBOBMZTJTUPJUOPUIJOHXPVMECFVODFSUBJO UIF future as well as the past would be present to its eyes. The perfection that the human mind has been able to give to astronomy affords but a feeble outline of such intelligence. 5IF QIZTJDT PG -BQMBDFT EBZ UIF àSTU EFDBEFT PG UIF OJOFUFFOUI DFOUVSZ XBT /FXUPOJBO DMBTTJDBM NFDIBOJDT*TBBD/FXUPOGPSNVMBUFEQSJODJQMFTUIBUIFUIPVHIU FYQSFTTUIFMBXTEFTDSJCJOHIPXGPSDFTEFUFSNJOFUIFNPUJPOTPGCPEJFT F ma) and IPXUIFQPTJUJPOTPGCPEJFTBOEPUIFSGBDUPSTEFUFSNJOFHSBWJUBUJPOBMBOEPUIFSLJOET PGGPSDFT6TJOHUIFTFQSJODJQMFT /FXUPOBOEQIZTJDJTUTGPMMPXJOHIJNXFSFBCMFUP QSFEJDUBOEFYQMBJOUIFNPUJPOTPGDFMFTUJBMBOEUFSSFTUSJBMCPEJFT'PSFYBNQMF UIFTF laws account for the orbits of the planets, the trajectories of cannon balls, and the QFSJPET PG QFOEVMVNT-JLF/FXUPO -BQMBDFEJE OPULOPX BMM UIFGPSDFTUIFSF BSF but he envisioned that, once those forces (and the corresponding force laws) were LOPXO /FXUPOJBO QIZTJDT XPVME CF B complete physical theory. That is, its laws would account for the motions of all material particles. And since he thought that FWFSZUIJOH UIBU FYJTUT JO TQBDF JT DPNQPTFE PG WBSJPVT LJOET PG WFSZ TNBMM NBUFSJBM #"33:-0&8&3 QBSUJDMFT PSBUPNT IFUIPVHIUUIBU/FXUPOJBONFDIBOJDT PODFBMMUIFGPSDFTXFSF LOPXO XPVMECFXIBUUPEBZXFXPVMEDBMMthe theory of everything. -
Gaussian Elimination and Lu Decomposition (Supplement for Ma511)
GAUSSIAN ELIMINATION AND LU DECOMPOSITION (SUPPLEMENT FOR MA511) D. ARAPURA Gaussian elimination is the go to method for all basic linear classes including this one. We go summarize the main ideas. 1. Matrix multiplication The rule for multiplying matrices is, at first glance, a little complicated. If A is m × n and B is n × p then C = AB is defined and of size m × p with entries n X cij = aikbkj k=1 The main reason for defining it this way will be explained when we talk about linear transformations later on. THEOREM 1.1. (1) Matrix multiplication is associative, i.e. A(BC) = (AB)C. (Henceforth, we will drop parentheses.) (2) If A is m × n and Im×m and In×n denote the identity matrices of the indicated sizes, AIn×n = Im×mA = A. (We usually just write I and the size is understood from context.) On the other hand, matrix multiplication is almost never commutative; in other words generally AB 6= BA when both are defined. So we have to be careful not to inadvertently use it in a calculation. Given an n × n matrix A, the inverse, if it exists, is another n × n matrix A−1 such that AA−I = I;A−1A = I A matrix is called invertible or nonsingular if A−1 exists. In practice, it is only necessary to check one of these. THEOREM 1.2. If B is n × n and AB = I or BA = I, then A invertible and B = A−1. Proof. The proof of invertibility of A will need to wait until we talk about deter- minants.