Hermann Graßmann and the Foundations of Linear Algebra
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Things You Need to Know About Linear Algebra Math 131 Multivariate
the standard (x; y; z) coordinate three-space. Nota- tions for vectors. Geometric interpretation as vec- tors as displacements as well as vectors as points. Vector addition, the zero vector 0, vector subtrac- Things you need to know about tion, scalar multiplication, their properties, and linear algebra their geometric interpretation. Math 131 Multivariate Calculus We start using these concepts right away. In D Joyce, Spring 2014 chapter 2, we'll begin our study of vector-valued functions, and we'll use the coordinates, vector no- The relation between linear algebra and mul- tation, and the rest of the topics reviewed in this tivariate calculus. We'll spend the first few section. meetings reviewing linear algebra. We'll look at just about everything in chapter 1. Section 1.2. Vectors and equations in R3. Stan- Linear algebra is the study of linear trans- dard basis vectors i; j; k in R3. Parametric equa- formations (also called linear functions) from n- tions for lines. Symmetric form equations for a line dimensional space to m-dimensional space, where in R3. Parametric equations for curves x : R ! R2 m is usually equal to n, and that's often 2 or 3. in the plane, where x(t) = (x(t); y(t)). A linear transformation f : Rn ! Rm is one that We'll use standard basis vectors throughout the preserves addition and scalar multiplication, that course, beginning with chapter 2. We'll study tan- is, f(a + b) = f(a) + f(b), and f(ca) = cf(a). We'll gent lines of curves and tangent planes of surfaces generally use bold face for vectors and for vector- in that chapter and throughout the course. -
Schaum's Outline of Linear Algebra (4Th Edition)
SCHAUM’S SCHAUM’S outlines outlines Linear Algebra Fourth Edition Seymour Lipschutz, Ph.D. Temple University Marc Lars Lipson, Ph.D. University of Virginia Schaum’s Outline Series New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2009, 2001, 1991, 1968 by The McGraw-Hill Companies, Inc. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior writ- ten permission of the publisher. ISBN: 978-0-07-154353-8 MHID: 0-07-154353-8 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-154352-1, MHID: 0-07-154352-X. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. To contact a representative please e-mail us at [email protected]. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licensors reserve all rights in and to the work. -
Determinants Math 122 Calculus III D Joyce, Fall 2012
Determinants Math 122 Calculus III D Joyce, Fall 2012 What they are. A determinant is a value associated to a square array of numbers, that square array being called a square matrix. For example, here are determinants of a general 2 × 2 matrix and a general 3 × 3 matrix. a b = ad − bc: c d a b c d e f = aei + bfg + cdh − ceg − afh − bdi: g h i The determinant of a matrix A is usually denoted jAj or det (A). You can think of the rows of the determinant as being vectors. For the 3×3 matrix above, the vectors are u = (a; b; c), v = (d; e; f), and w = (g; h; i). Then the determinant is a value associated to n vectors in Rn. There's a general definition for n×n determinants. It's a particular signed sum of products of n entries in the matrix where each product is of one entry in each row and column. The two ways you can choose one entry in each row and column of the 2 × 2 matrix give you the two products ad and bc. There are six ways of chosing one entry in each row and column in a 3 × 3 matrix, and generally, there are n! ways in an n × n matrix. Thus, the determinant of a 4 × 4 matrix is the signed sum of 24, which is 4!, terms. In this general definition, half the terms are taken positively and half negatively. In class, we briefly saw how the signs are determined by permutations. -
Span, Linear Independence and Basis Rank and Nullity
Remarks for Exam 2 in Linear Algebra Span, linear independence and basis The span of a set of vectors is the set of all linear combinations of the vectors. A set of vectors is linearly independent if the only solution to c1v1 + ::: + ckvk = 0 is ci = 0 for all i. Given a set of vectors, you can determine if they are linearly independent by writing the vectors as the columns of the matrix A, and solving Ax = 0. If there are any non-zero solutions, then the vectors are linearly dependent. If the only solution is x = 0, then they are linearly independent. A basis for a subspace S of Rn is a set of vectors that spans S and is linearly independent. There are many bases, but every basis must have exactly k = dim(S) vectors. A spanning set in S must contain at least k vectors, and a linearly independent set in S can contain at most k vectors. A spanning set in S with exactly k vectors is a basis. A linearly independent set in S with exactly k vectors is a basis. Rank and nullity The span of the rows of matrix A is the row space of A. The span of the columns of A is the column space C(A). The row and column spaces always have the same dimension, called the rank of A. Let r = rank(A). Then r is the maximal number of linearly independent row vectors, and the maximal number of linearly independent column vectors. So if r < n then the columns are linearly dependent; if r < m then the rows are linearly dependent. -
Josiah Willard Gibbs
GENERAL ARTICLE Josiah Willard Gibbs V Kumaran The foundations of classical thermodynamics, as taught in V Kumaran is a professor textbooks today, were laid down in nearly complete form by of chemical engineering at the Indian Institute of Josiah Willard Gibbs more than a century ago. This article Science, Bangalore. His presentsaportraitofGibbs,aquietandmodestmanwhowas research interests include responsible for some of the most important advances in the statistical mechanics and history of science. fluid mechanics. Thermodynamics, the science of the interconversion of heat and work, originated from the necessity of designing efficient engines in the late 18th and early 19th centuries. Engines are machines that convert heat energy obtained by combustion of coal, wood or other types of fuel into useful work for running trains, ships, etc. The efficiency of an engine is determined by the amount of useful work obtained for a given amount of heat input. There are two laws related to the efficiency of an engine. The first law of thermodynamics states that heat and work are inter-convertible, and it is not possible to obtain more work than the amount of heat input into the machine. The formulation of this law can be traced back to the work of Leibniz, Dalton, Joule, Clausius, and a host of other scientists in the late 17th and early 18th century. The more subtle second law of thermodynamics states that it is not possible to convert all heat into work; all engines have to ‘waste’ some of the heat input by transferring it to a heat sink. The second law also established the minimum amount of heat that has to be wasted based on the absolute temperatures of the heat source and the heat sink. -
MATH 532: Linear Algebra Chapter 4: Vector Spaces
MATH 532: Linear Algebra Chapter 4: Vector Spaces Greg Fasshauer Department of Applied Mathematics Illinois Institute of Technology Spring 2015 [email protected] MATH 532 1 Outline 1 Spaces and Subspaces 2 Four Fundamental Subspaces 3 Linear Independence 4 Bases and Dimension 5 More About Rank 6 Classical Least Squares 7 Kriging as best linear unbiased predictor [email protected] MATH 532 2 Spaces and Subspaces Outline 1 Spaces and Subspaces 2 Four Fundamental Subspaces 3 Linear Independence 4 Bases and Dimension 5 More About Rank 6 Classical Least Squares 7 Kriging as best linear unbiased predictor [email protected] MATH 532 3 Spaces and Subspaces Spaces and Subspaces While the discussion of vector spaces can be rather dry and abstract, they are an essential tool for describing the world we work in, and to understand many practically relevant consequences. After all, linear algebra is pretty much the workhorse of modern applied mathematics. Moreover, many concepts we discuss now for traditional “vectors” apply also to vector spaces of functions, which form the foundation of functional analysis. [email protected] MATH 532 4 Spaces and Subspaces Vector Space Definition A set V of elements (vectors) is called a vector space (or linear space) over the scalar field F if (A1) x + y 2 V for any x; y 2 V (M1) αx 2 V for every α 2 F and (closed under addition), x 2 V (closed under scalar (A2) (x + y) + z = x + (y + z) for all multiplication), x; y; z 2 V, (M2) (αβ)x = α(βx) for all αβ 2 F, (A3) x + y = y + x for all x; y 2 V, x 2 V, (A4) There exists a zero vector 0 2 V (M3) α(x + y) = αx + αy for all α 2 F, such that x + 0 = x for every x; y 2 V, x 2 V, (M4) (α + β)x = αx + βx for all (A5) For every x 2 V there is a α; β 2 F, x 2 V, negative (−x) 2 V such that (M5)1 x = x for all x 2 V. -
On the History of Some Linear Algebra Concepts:From Babylon to Pre
The Online Journal of Science and Technology - January 2017 Volume 7, Issue 1 ON THE HISTORY OF SOME LINEAR ALGEBRA CONCEPTS: FROM BABYLON TO PRE-TECHNOLOGY Sinan AYDIN Kocaeli University, Kocaeli Vocational High school, Kocaeli – Turkey [email protected] Abstract: Linear algebra is a basic abstract mathematical course taught at universities with calculus. It first emerged from the study of determinants in 1693. As a textbook, linear algebra was first used in graduate level curricula in 1940’s at American universities. In the 2000s, science departments of universities all over the world give this lecture in their undergraduate programs. The study of systems of linear equations first introduced by the Babylonians around at 1800 BC. For solving of linear equations systems, Cardan constructed a simple rule for two linear equations with two unknowns around at 1550 AD. Lagrange used matrices in his work on the optimization problems of real functions around at 1750 AD. Also, determinant concept was used by Gauss at that times. Between 1800 and 1900, there was a rapid and important developments in the context of linear algebra. Some theorems for determinant, the concept of eigenvalues, diagonalisation of a matrix and similar matrix concept were added in linear algebra by Couchy. Vector concept, one of the main part of liner algebra, first used by Grassmann as vector product and vector operations. The term ‘matrix’ as a rectangular forms of scalars was first introduced by J. Sylvester. The configuration of linear transformations and its connection with the matrix addition, multiplication and scalar multiplication were studied first by A. -
The Tragedy of Grassmann Séminaire De Philosophie Et Mathématiques, 1979, Fascicule 2 « the Tragedy of Grassmann », , P
Séminaire de philosophie et mathématiques J. DIEUDONNÉ The Tragedy of Grassmann Séminaire de Philosophie et Mathématiques, 1979, fascicule 2 « The Tragedy of Grassmann », , p. 1-14 <http://www.numdam.org/item?id=SPHM_1979___2_A1_0> © École normale supérieure – IREM Paris Nord – École centrale des arts et manufactures, 1979, tous droits réservés. L’accès aux archives de la série « Séminaire de philosophie et mathématiques » implique l’accord avec les conditions générales d’utilisation (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/ - 1 - The Tragedy of Grassmann I. GRASSMANN'S LIFE In Ihc whole gallery of prominent mathematicians who, since the time of the Greeks, have left their mark on science, Hermann Grassmann certainly stands out as the most exceptional in many respects. When compared with other mathematicians, his career is an uninterrupted succession of oddities: unusual were his studies; unusual his mathematical style; highly unusual his own belated realization of his powers as a mathematician; unusual and unfortunate the total lack of understanding of his ideas, not only during his lifetime but long after his death; deplorable the neglect which compelled him to remain all his life professor in a high-school ("Gymnasiallehrer") when far lesser men occupied University positions; and the shroud of ignorance and uncertainty still surrounds his life and works in the minds of most mathematicians of our time, even when they put his original ideas to daily use. -
Multi-Variable Calculus/Linear Algebra Scope & Sequence
Multi-Variable Calculus/Linear Algebra Scope & Sequence Grading Period Unit Title Learning Targets Throughout the *Apply mathematics to problems in everyday life School Year *Use a problem-solving model that incorporates analyzing information, formulating a plan, determining a solution, justifying the solution and evaluating the reasonableness of the solution *Select tools to solve problems *Communicate mathematical ideas, reasoning and their implications using multiple representations *Create and use representations to organize, record and communicate mathematical ideas *Analyze mathematical relationships to connect and communicate mathematical ideas *Display, explain and justify mathematical ideas and arguments First Grading Vectors in a plane and Unit vectors, graphing and basic operations Period in space MULTIVARIABLE Dot products and cross Angles between vectors, orthogonality, projections, work CALCULUS products Lines and Planes in Equations, intersections, distance space Surfaces in Space Cylindrical surfaces, quadric surfaces, surfaces of revolution, cylindrical and spherical coordinate Vector Valued Parameterization, domain, range, limits, graphical representation, continuity, differentiation, Functions integration, smoothness, velocity, acceleration, speed, position for a projectile, tangent and normal vectors, principal unit normal, arc length and curvature. Functions of several Graphically, level curves, delta neighborhoods, interior point, boundary point, closed regions, variables limits by definition, limits from various -
Grassmann's Vision
In: Hermann Gunther Grasmann (1809-1877): Visionary Mathematician, Scientist and Neohumanist Scholar, 1996 (Gert Schubring, Ed.), Kluwer Academic Publishers, Dordrecht/Boston 191–201. GRASSMANN’S VISION David Hestenes Abstract. Hermann Grassmann is cast as a pivotal gure in the historical development of a universal geometric calculus for mathematics and physics which continues to this day. He formulated most of the basic ideas and, to a remarkable extent, anticipates later developments. His inuence is far more potent and pervasive than generally recognized. After nearly a century on the brink of obscurity, Hermann Grassmann is widely recognized as the originator of Grassmann algebra, an indispensable tool in modern mathematics. Still, in conception and applications, conventional renditions of his exterior algebra fall far short of Grassmann’s original vision. A fuller realization of his vision is found in other mathematical developments to which his name is not ordinarily attached. This Sesquicen- tennial Celebration of Grassmann’s great book, Die Lineale Ausdehnungslehre [1], provides the opportunity for a renewed articulation and assessment of Grassmann’s vision. Grassmann is a pivotal gure in the historical evolution of a mathematical language to characterize human understanding of the physical world. Since quantitative concepts of space and time are fundamental to that understanding, the language is fundamentally geometrical and can best be described as a geometric calculus. For the most part the evo- lution of geometric calculus has been tacit and piecemeal, with many individuals achieving isolated results in response to isolated problems. Grassmann is pivotal in this historical process because he made it explicit and programmatic. In no uncertain terms, he declared the goal of his research on extension theory [1] as no less than the creation of a universal instrument for geometric research. -
The Exterior Algebra and Central Notions in Mathematics
The Exterior Algebra and Central Notions in Mathematics Gunnar Fløystad Dedicated to Stein Arild Strømme (1951–2014) The neglect of the exterior algebra is the mathematical tragedy of our century. —Gian-Carlo Rota, Indiscrete Thoughts (1997) his note surveys how the exterior algebra the somewhat lesser-known regressive product on and deformations or quotients of it the exterior algebra, which intuitively corresponds capture essences of five domains in to intersection of linear spaces. It relates this to mathematics: geometry and it also shows how analysis may T be extended to functions of extensive quantities. • Combinatorics •Mathematical physics Only in the last two decades of the 1800s did • Topology •Algebraic geometry publications inspired by Grassmann’s work achieve • Lie theory a certain mass. It may have been with some regret The exterior algebra originated in the work that Grassmann in his second version had an of Hermann Grassmann (1809–1877) in his book exclusively mathematical form, since he in the Ausdehnungslehre from 1844, and the thoroughly foreword says “[extension theory] is not simply one revised 1862 version, which now exists in an English among the other branches of mathematics, such translation [20] from 2000. Grassmann worked as algebra, combination theory or function theory, as a professor at the gymnasium in Stettin, then bur rather surpasses them, in that all fundamental Germany. Partly because Grassmann was an original elements are unified under this branch, which thinker and maybe partly because his education thus as it were forms the keystone of the entire had not focused much on mathematics, the first structure of mathematics.” edition of his book had a more philosophical The present note indicates that he was not quite than mathematical form and therefore gained little off the mark here. -
Whitney Algebras and Grassmann's Regressive Products
Whitney algebras and Grassmann’s regressive products Andrea Brini and Francesco Regonati Dipartimento di Matematica “Alma Mater Studiorum” Universit`adegli Studi di Bologna October 18, 2018 Abstract Geometric products on tensor powers Λ(V )⊗m of an exterior algebra and on Whitney algebras [13] provide a rigorous version of Grassmann’s regressive products of 1844 [17]. We study geometric products and their relations with other classical operators on exterior algebras, such as the Hodge ∗−operators and the join and meet products in Cayley-Grassmann algebras [2, 30]. We establish encodings of tensor powers Λ(V )⊗m and of Whitney algebras W m(M) in terms of letterplace algebras and of their geometric products in terms of divided powers of polarization operators. We use these encodings to provide simple proofs of the Crapo and Schmitt exchange relations in Whitney algebras and of two typical classes of identities in Cayley-Grassmann algebras . arXiv:1010.2964v1 [math.CO] 14 Oct 2010 We thank Henry Crapo and William Schmitt for their advice, encouragement, and invaluable suggestions Contents 1 Introduction 2 2 The algebras: generalities 5 3 The algebras: basic constructions 6 3.1 Cayley-Grassmannalgebras . .. .. 6 3.2 Tensorpowersofexterioralgebras. 7 3.3 Whitneyalgebras .............................. 11 1 4 Relations among the algebras 12 4.1 From tensor powers of exterior algebras to CG-Algebras . .... 12 4.2 From Whitney algebras to tensor powers of exterior algebras. .... 12 5 The Hodge operators 13 5.1 The Hodge ∗−operators .......................... 13 5.2 ThegeneralizedHodgeoperators . 15 6 Letterplace algebras and polarization operators 17 6.1 Skew-symmetric Letterplace algebras and place polarization operators.