Physical Mathematics

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(1)more information – www.cambridge.org/9781107005211.

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(3) Physical Mathematics Unique in its clarity, examples, and range, Physical Mathematics explains as simply as possible the mathematics that graduate students and professional physicists need in their courses and research. The author illustrates the mathematics with numerous physical examples drawn from contemporary research. In addition to basic subjects such as linear algebra, Fourier analysis, complex variables, differential equations, and Bessel functions, this textbook covers topics such as the singular-value decomposition, Lie algebras, the tensors and forms of general relativity, the central limit theorem and Kolmogorov test of statistics, the Monte Carlo methods of experimental and theoretical physics, the renormalization group of condensed-matter physics, and the functional derivatives and Feynman path integrals of quantum field theory. Solutions to exercises are available for instructors at www.cambridge.org/cahill K E V I N C A H I L L is Professor of Physics and Astronomy at the University of New Mexico. He has done research at NIST, Saclay, Ecole Polytechnique, Orsay, Harvard, NIH, LBL, and SLAC, and has worked in quantum optics, quantum field theory, lattice gauge theory, and biophysics. Physical Mathematics is based on courses taught by the author at the University of New Mexico and at Fudan University in Shanghai..

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(5) Physical Mathematics KEVIN CAHILL University of New Mexico.

(6) CAMBRIDGE UNIVERSITY PRESS. Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Mexico City Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9781107005211 c K. Cahill 2013 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2013 Printed and bound in the United Kingdom by the MPG Books Group A catalog record for this publication is available from the British Library Library of Congress Cataloging in Publication data Cahill, Kevin, 1941–, author. Physical mathematics / Kevin Cahill, University of New Mexico. pages cm ISBN 978-1-107-00521-1 (hardback) 1. Mathematical physics. I. Title. QC20.C24 2012 530.15–dc23 2012036027 ISBN 978-1-107-00521-1 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate..

(7) For Ginette, Mike, Sean, Peter, Mia, and James, and in honor of Muntadhar al-Zaidi..

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(9) Contents. Preface 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24. page xvii. Linear algebra Numbers Arrays Matrices Vectors Linear operators Inner products The Cauchy–Schwarz inequality Linear independence and completeness Dimension of a vector space Orthonormal vectors Outer products Dirac notation The adjoint of an operator Self-adjoint or hermitian linear operators Real, symmetric linear operators Unitary operators Hilbert space Antiunitary, antilinear operators Symmetry in quantum mechanics Determinants Systems of linear equations Linear least squares Lagrange multipliers Eigenvectors vii. 1 1 2 4 7 9 11 14 15 16 16 18 19 22 23 23 24 25 26 26 27 34 34 35 37.

(10) CONTENTS. 1.25 1.26 1.27 1.28 1.29 1.30 1.31 1.32 1.33 1.34 1.35 1.36 1.37. Eigenvectors of a square matrix A matrix obeys its characteristic equation Functions of matrices Hermitian matrices Normal matrices Compatible normal matrices The singular-value decomposition The Moore–Penrose pseudoinverse The rank of a matrix Software The tensor/direct product Density operators Correlation functions Exercises. 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13. Fourier series Complex Fourier series The interval Where to put the 2πs Real Fourier series for real functions Stretched intervals Fourier series in several variables How Fourier series converge Quantum-mechanical examples Dirac notation Dirac’s delta function The harmonic oscillator Nonrelativistic strings Periodic boundary conditions Exercises. 75 75 77 77 79 83 84 84 89 96 97 101 103 103 105. 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10. Fourier and Laplace transforms The Fourier transform The Fourier transform of a real function Dirac, Parseval, and Poisson Fourier derivatives and integrals Fourier transforms in several dimensions Convolutions The Fourier transform of a convolution Fourier transforms and Green’s functions Laplace transforms Derivatives and integrals of Laplace transforms. 108 108 111 112 115 119 121 123 124 125 127. viii. 38 41 43 45 50 52 55 63 65 66 66 69 69 71.

(11) CONTENTS. 3.11 3.12 3.13. Laplace transforms and differential equations Inversion of Laplace transforms Application to differential equations Exercises. 128 129 129 134. 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14. Infinite series Convergence Tests of convergence Convergent series of functions Power series Factorials and the gamma function Taylor series Fourier series as power series The binomial series and theorem Logarithmic series Dirichlet series and the zeta function Bernoulli numbers and polynomials Asymptotic series Some electrostatic problems Infinite products Exercises. 136 136 137 138 139 141 145 146 147 148 149 151 152 154 157 158. 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19. Complex-variable theory Analytic functions Cauchy’s integral theorem Cauchy’s integral formula The Cauchy–Riemann conditions Harmonic functions Taylor series for analytic functions Cauchy’s inequality Liouville’s theorem The fundamental theorem of algebra Laurent series Singularities Analytic continuation The calculus of residues Ghost contours Logarithms and cuts Powers and roots Conformal mapping Cauchy’s principal value Dispersion relations. 160 160 161 165 169 170 171 173 173 174 174 177 179 180 182 193 194 197 198 205. ix.

(12) CONTENTS. 5.20 5.21 5.22 5.23 5.24. Kramers–Kronig relations Phase and group velocities The method of steepest descent The Abel–Plana formula and the Casimir effect Applications to string theory Exercises. 207 208 210 212 217 219. 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15. Differential equations Ordinary linear differential equations Linear partial differential equations Notation for derivatives Gradient, divergence, and curl Separable partial differential equations Wave equations First-order differential equations Separable first-order differential equations Hidden separability Exact first-order differential equations The meaning of exactness Integrating factors Homogeneous functions The virial theorem Homogeneous first-order ordinary differential equations Linear first-order ordinary differential equations Systems of differential equations Singular points of second-order ordinary differential equations Frobenius’s series solutions Fuch’s theorem Even and odd differential operators Wronski’s determinant A second solution Why not three solutions? Boundary conditions A variational problem Self-adjoint differential operators Self-adjoint differential systems Making operators formally self adjoint Wronskians of self-adjoint operators First-order self-adjoint differential operators A constrained variational problem. 223 223 225 226 228 230 233 235 235 238 238 240 242 243 243. 6.16 6.17 6.18 6.19 6.20 6.21 6.22 6.23 6.24 6.25 6.26 6.27 6.28 6.29 6.30 6.31 6.32. x. 245 246 248 250 251 253 254 255 255 257 258 259 260 262 264 265 266 267.

(13) CONTENTS. 6.33 6.34 6.35 6.36 6.37 6.38 6.39 6.40. Eigenfunctions and eigenvalues of self-adjoint systems Unboundedness of eigenvalues Completeness of eigenfunctions The inequalities of Bessel and Schwarz Green’s functions Eigenfunctions and Green’s functions Green’s functions in one dimension Nonlinear differential equations Exercises. 273 275 277 284 284 287 288 289 293. 7 7.1 7.2 7.3 7.4 7.5. Integral equations Fredholm integral equations Volterra integral equations Implications of linearity Numerical solutions Integral transformations Exercises. 296 297 297 298 299 301 304. 8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11 8.12 8.13. Legendre functions The Legendre polynomials The Rodrigues formula The generating function Legendre’s differential equation Recurrence relations Special values of Legendre’s polynomials Schlaefli’s integral Orthogonal polynomials The azimuthally symmetric Laplacian Laplacian in two dimensions The Laplacian in spherical coordinates The associated Legendre functions/polynomials Spherical harmonics Exercises. 305 305 306 308 309 311 312 313 313 315 316 317 317 319 323. 9 9.1 9.2 9.3 9.4. Bessel functions Bessel functions of the first kind Spherical Bessel functions of the first kind Bessel functions of the second kind Spherical Bessel functions of the second kind Further reading Exercises. 325 325 335 341 343 345 345. xi.

(14) CONTENTS. 10 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 10.11 10.12 10.13 10.14 10.15 10.16 10.17 10.18 10.19 10.20 10.21 10.22 10.23 10.24 10.25 10.26 10.27 10.28 10.29 10.30 10.31 10.32 10.33 10.34. Group theory What is a group? Representations of groups Representations acting in Hilbert space Subgroups Cosets Morphisms Schur’s lemma Characters Tensor products Finite groups The regular representation Properties of finite groups Permutations Compact and noncompact Lie groups Lie algebra The rotation group The Lie algebra and representations of SU(2) The defining representation of SU(2) The Jacobi identity The adjoint representation Casimir operators Tensor operators for the rotation group Simple and semisimple Lie algebras SU(3) SU(3) and quarks Cartan subalgebra Quaternions The symplectic group Sp (2n) Compact simple Lie groups Group integration The Lorentz group Two-dimensional representations of the Lorentz group The Dirac representation of the Lorentz group The Poincaré group Further reading Exercises. 348 348 350 351 353 354 354 355 356 357 358 359 360 360 361 361 366 368 371 374 374 375 376 376 377 378 379 379 381 383 384 386 389 393 395 396 397. 11 11.1 11.2 11.3. Tensors and local symmetries Points and coordinates Scalars Contravariant vectors. 400 400 401 401 xii.

(15) CONTENTS. 11.4 11.5 11.6 11.7 11.8 11.9 11.10 11.11 11.12 11.13 11.14 11.15 11.16 11.17 11.18 11.19 11.20 11.21 11.22 11.23 11.24 11.25 11.26 11.27 11.28 11.29 11.30 11.31 11.32 11.33 11.34 11.35 11.36 11.37 11.38 11.39 11.40 11.41 11.42 11.43 11.44 11.45. Covariant vectors Euclidean space in euclidean coordinates Summation conventions Minkowski space Lorentz transformations Special relativity Kinematics Electrodynamics Tensors Differential forms Tensor equations The quotient theorem The metric tensor A basic axiom The contravariant metric tensor Raising and lowering indices Orthogonal coordinates in euclidean n-space Polar coordinates Cylindrical coordinates Spherical coordinates The gradient of a scalar field Levi-Civita’s tensor The Hodge star Derivatives and affine connections Parallel transport Notations for derivatives Covariant derivatives The covariant curl Covariant derivatives and antisymmetry Affine connection and metric tensor Covariant derivative of the metric tensor Divergence of a contravariant vector The covariant Laplacian The principle of stationary action A particle in a gravitational field The principle of equivalence Weak, static gravitational fields Gravitational time dilation Curvature Einstein’s equations The action of general relativity Standard form xiii. 402 402 404 405 407 408 410 411 414 416 419 420 421 422 422 423 423 424 425 425 426 427 428 431 433 433 434 435 436 436 437 438 441 443 446 447 449 449 451 453 455 455.

(16) CONTENTS. 11.46 11.47 11.48 11.49 11.50 11.51 11.52. Schwarzschild’s solution Black holes Cosmology Model cosmologies Yang–Mills theory Gauge theory and vectors Geometry Further reading Exercises. 456 456 457 463 469 471 474 475 475. 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7. Forms Exterior forms Differential forms Exterior differentiation Integration of forms Are closed forms exact? Complex differential forms Frobenius’s theorem Further reading Exercises. 479 479 481 486 491 496 498 499 500 500. 13 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 13.10 13.11 13.12 13.13 13.14 13.15 13.16 13.17 13.18 13.19 13.20. Probability and statistics Probability and Thomas Bayes Mean and variance The binomial distribution The Poisson distribution The Gaussian distribution The error function erf The Maxwell–Boltzmann distribution Diffusion Langevin’s theory of brownian motion The Einstein–Nernst relation Fluctuation and dissipation Characteristic and moment-generating functions Fat tails The central limit theorem and Jarl Lindeberg Random-number generators Illustration of the central limit theorem Measurements, estimators, and Friedrich Bessel Information and Ronald Fisher Maximum likelihood Karl Pearson’s chi-squared statistic. 502 502 505 508 511 512 515 518 519 520 523 524 528 530 532 537 538 543 546 550 551. xiv.

(17) CONTENTS. 13.21 Kolmogorov’s test Further reading Exercises. 554 560 560. 14 14.1 14.2 14.3 14.4 14.5 14.6. Monte Carlo methods The Monte Carlo method Numerical integration Applications to experiments Statistical mechanics Solving arbitrary problems Evolution Further reading Exercises. 563 563 563 566 572 575 576 577 577. 15 15.1 15.2 15.3 15.4 15.5. Functional derivatives Functionals Functional derivatives Higher-order functional derivatives Functional Taylor series Functional differential equations Exercises. 578 578 578 581 582 583 585. 16 16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8 16.9 16.10 16.11 16.12 16.13 16.14 16.15 16.16 16.17. Path integrals Path integrals and classical physics Gaussian integrals Path integrals in imaginary time Path integrals in real time Path integral for a free particle Free particle in imaginary time Harmonic oscillator in real time Harmonic oscillator in imaginary time Euclidean correlation functions Finite-temperature field theory Real-time field theory Perturbation theory Application to quantum electrodynamics Fermionic path integrals Application to nonabelian gauge theories The Faddeev–Popov trick Ghosts Further reading Exercises. 586 586 586 588 590 593 595 595 597 599 600 603 605 609 613 619 620 622 624 624. xv.

(18) CONTENTS. 17 17.1 17.2 17.3. The renormalization group The renormalization group in quantum field theory The renormalization group in lattice field theory The renormalization group in condensed-matter physics Exercises. 626 626 630 632 634. 18 18.1 18.2 18.3. Chaos and fractals Chaos Attractors Fractals Further reading Exercises. 635 635 639 639 642 642. 19 19.1 19.2 19.3 19.4 19.5 19.6 19.7. Strings The infinities of quantum field theory The Nambu–Goto string action Regge trajectories Quantized strings D-branes String–string scattering Riemann surfaces and moduli Further reading Exercises. 643 643 643 646 647 647 648 649 650 650. References Index. 651 656. xvi.

(19) Preface. To the students: you will find some physics crammed in amongst the mathematics. Don’t let the physics bother you. As you study the math, you’ll learn some physics without extra effort. The physics is a freebie. I have tried to explain the math you need for physics and have left out the rest. To the professors: the book is for students who also are taking mechanics, electrodynamics, quantum mechanics, and statistical mechanics nearly simultaneously and who soon may use probability or path integrals in their research. Linear algebra and Fourier analysis are the keys to physics, so the book starts with them, but you may prefer to skip the algebra or postpone the Fourier analysis. The book is intended to support a one- or two-semester course for graduate students or advanced undergraduates. The first seven, eight, or nine chapters fit in one semester, the others in a second. A list of errata is maintained at panda.unm.edu/cahill, and solutions to all the exercises are available for instructors at www.cambridge.org/cahill. Several friends – Susan Atlas, Bernard Becker, Steven Boyd, Robert Burckel, Sean Cahill, Colston Chandler, Vageli Coutsias, David Dunlap, Daniel Finley, Franco Giuliani, Roy Glauber, Pablo Gondolo, Igor Gorelov, Jiaxing Hong, Fang Huang, Dinesh Loomba, Yin Luo, Lei Ma, Michael Malik, Kent Morrison, Sudhakar Prasad, Randy Reeder, Dmitri Sergatskov, and David Waxman – have given me valuable advice. Students have helped with questions, ideas, and corrections, especially Thomas Beechem, Marie Cahill, Chris Cesare, Yihong Cheng, Charles Cherqui, Robert Cordwell, Amo-Kwao Godwin, Aram Gragossian, Aaron Hankin, Kangbo Hao, Tiffany Hayes, Yiran Hu, Shanshan Huang, Tyler Keating, Joshua Koch, Zilong Li, Miao Lin, ZuMou Lin, Sheng Liu, Yue Liu, Ben Oliker, Boleszek Osinski, Ravi Raghunathan, Akash Rakholia, Xingyue Tian, Toby Tolley, Jiqun Tu, Christopher Vergien, Weizhen Wang, George Wendelberger, Xukun Xu, Huimin Yang, Zhou Yang, Daniel Young, Mengzhen Zhang, Lu Zheng, Lingjun Zhou, and Daniel Zirzow. xvii.

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(21) 1. Linear algebra. 1.1 Numbers The natural numbers are the positive integers and zero. Rational numbers are ratios of integers. Irrational numbers have decimal digits dn x=. ∞ dn 10n n=m. (1.1). x. that do not repeat. Thus the repeating decimals 1/2 = 0.50000 . . . and 1/3 = 0.3¯ ≡ 0.33333 . . . are rational, while π = 3.141592654 . . . is irrational. Decimal arithmetic was invented in India over 1500 years ago but was not widely adopted in the Europe until the seventeenth century. The real numbers R include the rational numbers and the irrational numbers; they correspond to all the points on an infinite line called the real line. The complex numbers C are the real numbers with one new number i whose square is −1. A complex number z is a linear combination of a real number x and a real multiple i y of i z = x + iy.. (1.2). Here x = Rez is the real part of z, and y = Imz is its imaginary part. One adds complex numbers by adding their real and imaginary parts z1 + z2 = x1 + iy1 + x2 + iy2 = x1 + x2 + i(y1 + y2 ).. (1.3). Since i2 = −1, the product of two complex numbers is z1 z2 = (x1 + iy1 )(x2 + iy2 ) = x1 x2 − y1 y2 + i(x1 y2 + y1 x2 ).. (1.4). The polar representation z = r exp(iθ) of z = x + iy is z = x + iy = reiθ = r(cos θ + i sin θ) 1. (1.5).

(22) LINEAR ALGEBRA. in which r is the modulus or absolute value of z r = |z| = x2 + y2. (1.6). and θ is its phase or argument θ = arctan (y/x).. (1.7). Since exp(2πi) = 1, there is an inevitable ambiguity in the definition of the phase of any complex number: for any integer n, the phase θ + 2πn gives the same z as θ. In various computer languages, the function atan2(y, x) returns the angle θ in the interval −π < θ ≤ π for which (x, y) = r(cos θ, sin θ). There are two common notations z∗ and z¯ for the complex conjugate of a complex number z = x + iy z∗ = z¯ = x − iy.. (1.8). The square of the modulus of a complex number z = x + iy is |z|2 = x2 + y2 = (x + iy)(x − iy) = z¯ z = z∗ z.. (1.9). The inverse of a complex number z = x + iy is z−1 = (x + iy)−1 =. x − iy z∗ x − iy z∗ = 2 = = . (x − iy)(x + iy) z∗ z x + y2 |z|2. (1.10). Grassmann numbers θi are anticommuting numbers, that is, the anticommutator of any two Grassmann numbers vanishes {θi , θj } ≡ [θi , θj ]+ ≡ θi θj + θj θi = 0.. (1.11). So the square of any Grassmann number is zero, θi2 = 0. We won’t use these numbers until chapter 16, but they do have amusing properties. The highest monomial in N Grassmann numbers θi is the product θ1 θ2 . . . θN . So the most complicated power series in two Grassmann numbers is just f (θ1 , θ2 ) = f0 + f1 θ1 + f2 θ2 + f12 θ1 θ2. (1.12). (Hermann Grassmann, 1809–1877).. 1.2 Arrays An array is an ordered set of numbers. Arrays play big roles in computer science, physics, and mathematics. They can be of any (integral) dimension. A one-dimensional array (a1 , a2 , . . . , an ) is variously called an n-tuple, a row vector when written horizontally, a column vector when written vertically, or an n-vector. The numbers ak are its entries or components. A two-dimensional array aik with i running from 1 to n and k from 1 to m is an n × m matrix. The numbers aik are its entries, elements, or matrix elements. 2.

(23) 1.2 ARRAYS. One can think of a matrix as a stack of row vectors or as a queue of column vectors. The entry aik is in the ith row and the kth column. One can add together arrays of the same dimension and shape by adding their entries. Two n-tuples add as (a1 , . . . , an ) + (b1 , . . . , bn ) = (a1 + b1 , . . . , an + bn ). (1.13). and two n × m matrices a and b add as (a + b)ik = aik + bik .. (1.14). One can multiply arrays by numbers. Thus z times the three-dimensional array aijk is the array with entries z aijk . One can multiply two arrays together no matter what their shapes and dimensions. The outer product of an n-tuple a and an m-tuple b is an n × m matrix with elements (a b)ik = ai bk. (1.15). or an m × n matrix with entries (ba)ki = bk ai . If a and b are complex, then one also can form the outer products (a b)ik = ai bk , (b a)ki = bk ai , and (b a)ki = bk ai . The outer product of a matrix aik and a three-dimensional array bjm is a five-dimensional array (a b)ikjm = aik bjm .. (1.16). An inner product is possible when two arrays are of the same size in one of their dimensions. Thus the inner product (a, b) ≡ a|b or dot-product a · b of two real n-tuples a and b is (a, b) = a|b = a · b = (a1 , . . . , an ) · (b1 , . . . , bn ) = a1 b1 + · · · + an bn . (1.17) The inner product of two complex n-tuples often is defined as (a, b) = a|b = a · b = (a1 , . . . , an ) · (b1 , . . . , bn ) = a1 b1 + · · · + an bn (1.18) or as its complex conjugate (a, b)∗ = a|b∗ = (a · b)∗ = (b, a) = b|a = b · a. (1.19). so that the inner product of a vector with itself is nonnegative (a, a) ≥ 0. The product of an m×n matrix aik times an n-tuple bk is the m-tuple b whose ith component is n aik bk . (1.20) b i = ai1 b1 + ai2 b2 + · · · + ain bn = k=1. b. This product is = a b in matrix notation. If the size n of the second dimension of a matrix a matches that of the first dimension of a matrix b, then their product a b is a matrix with entries (a b)i = ai1 b1 + · · · + ain bn . 3. (1.21).

(24) LINEAR ALGEBRA. 1.3 Matrices Apart from n-tuples, the most important arrays in linear algebra are the twodimensional arrays called matrices. The trace of an n × n matrix a is the sum of its diagonal elements Tr a = tr a = a11 + a22 + · · · + ann =. n . aii .. (1.22). i=1. The trace of two matrices is independent of their order Tr (a b) =. n n . aik bki =. i=1 k=1. n n . bki aik = Tr (ba). (1.23). k=1 i=1. as long as the matrix elements are numbers that commute with each other. It follows that the trace is cyclic Tr (a b . . . z) = Tr (b . . . z a) . The transpose of an n × matrix a is an × n matrix aT with entries T a ij = aji .. (1.24). (1.25). Some mathematicians use a prime to mean transpose, as in a = aT , but physicists tend to use primes to label different objects or to indicate differentiation. One may show that (a b) T = bT aT .. (1.26). A matrix that is equal to its transpose a = aT. (1.27). is symmetric. The (hermitian) adjoint of a matrix is the complex conjugate of its transpose (Charles Hermite, 1822–1901). That is, the (hermitian) adjoint a† of an N × L complex matrix a is the L × N matrix with entries (a† )ij = (aji )∗ = a∗ji .. (1.28). (a b)† = b† a† .. (1.29). One may show that. A matrix that is equal to its adjoint (a† )ij = (aji )∗ = a∗ji = aij 4. (1.30).

(25) 1.3 MATRICES. (and which must be a square matrix) is hermitian or self adjoint a = a† . Example 1.1 (The Pauli matrices) 0 1 0 , σ2 = σ1 = 1 0 i. −i , 0. (1.31). . and. 1 σ3 = 0. 0 −1. (1.32). are all hermitian (Wolfgang Pauli, 1900–1958).. A real hermitian matrix is symmetric. If a matrix a is hermitian, then the quadratic form v|a|v =. N N . vi∗ aij vj ∈ R. (1.33). i=1 j=1. is real for all complex n-tuples v. The Kronecker delta δik is defined to be unity if i = k and zero if i

(26) = k (Leopold Kronecker, 1823–1891). The identity matrix I has entries Iik = δik . The inverse a−1 of an n × n matrix a is a square matrix that satisfies a−1 a = a a−1 = I. (1.34). in which I is the n × n identity matrix. So far we have been writing n-tuples and matrices and their elements with lower-case letters. It is equally common to use capital letters, and we will do so for the rest of this section. A matrix U whose adjoint U † is its inverse U † U = UU † = I. (1.35). is unitary. Unitary matrices are square. A real unitary matrix O is orthogonal and obeys the rule OT O = OOT = I.. (1.36). Orthogonal matrices are square. An N × N hermitian matrix A is nonnegative A≥0. (1.37). if for all complex vectors V the quadratic form V |A|V =. N N i=1 j=1. 5. Vi∗ Aij Vj ≥ 0. (1.38).

(27) LINEAR ALGEBRA. is nonnegative. It is positive or positive definite if V |A|V > 0. (1.39). for all nonzero vectors |V . Example 1.2 (Kinds of positivity) The nonsymmetric, nonhermitian 2 × 2 matrix 1 1 (1.40) −1 1 is positive on the space of all real 2-vectors but not on the space of all complex 2-vectors. Example 1.3 (Representations of imaginary and Grassmann numbers) The 2 × 2 matrix 0 −1 (1.41) 1 0 can represent the number i since 0 −1 0 1 0 1 The 2 × 2 matrix. −1 −1 = 0 0 0 1. 0 = −I. −1. 0 0. can represent a Grassmann number since 0 0 0 0 0 = 1 0 1 0 0. (1.42). (1.43) 0 = 0. 0. (1.44). To represent two Grassmann numbers, one needs 4 × 4 matrices, such as ⎛ ⎛ ⎞ ⎞ 0 0 1 0 0 1 0 0 ⎜0 0 0 −1⎟ ⎜ ⎟ ⎟ and θ2 = ⎜0 0 0 0⎟ . θ1 = ⎜ (1.45) ⎝0 0 0 0 ⎠ ⎝0 0 0 1⎠ 0 0 0 0 0 0 0 0 The matrices that represent n Grassmann numbers are 2n × 2n . Example 1.4 (Fermions) The matrices annihilation operators for a system of † and a2 = θ2 and their adjoints a1 and anticommutation relations †. {ai , ak } = δik. and. (1.45) also can represent lowering or two fermionic states. For a1 = θ1 † a2 , the creation operators satisfy the †. †. {ai , ak } = {ai , ak } = 0. 6. (1.46).

(28) 1.4 VECTORS †. where i and k take the values 1 or 2. In particular, the relation (ai )2 = 0 implements Pauli’s exclusion principle, the rule that no state of a fermion can be doubly occupied.. 1.4 Vectors Vectors are things that can be multiplied by numbers and added together to form other vectors in the same vector space. So if U and V are vectors in a vector space S over a set F of numbers x and y and so forth, then W = xU + yV. (1.47). also is a vector in the vector space S. A basis for a vector space S is a set of vectors Bk for k = 1, . . . , N in terms of which every vector U in S can be expressed as a linear combination U = u1 B1 + u2 B2 + · · · + uN BN. (1.48). with numbers uk in F. The numbers uk are the components of the vector U in the basis Bk . Example 1.5 (Hardware store) Suppose the vector W represents a certain kind of washer and the vector N represents a certain kind of nail. Then if n and m are natural numbers, the vector H = nW + mN. (1.49). would represent a possible inventory of a very simple hardware store. The vector space of all such vectors H would include all possible inventories of the store. That space is a two-dimensional vector space over the natural numbers, and the two vectors W and N form a basis for it. Example 1.6 (Complex numbers) The complex numbers are a vector space. Two of its vectors are the number 1 and the number i; the vector space of complex numbers is then the set of all linear combinations z = x1 + yi = x + iy.. (1.50). So the complex numbers are a two-dimensional vector space over the real numbers, and the vectors 1 and i are a basis for it. The complex numbers also form a one-dimensional vector space over the complex numbers. Here any nonzero real or complex number, for instance the number 1, can be a basis consisting of the single vector 1. This one-dimensional vector space is the set of all z = z1 for arbitrary complex z.. 7.

(29) LINEAR ALGEBRA. Example 1.7 (2-space) linear combinations. Ordinary flat two-dimensional space is the set of all r = xxˆ + yˆy. (1.51). in which x and y are real numbers and xˆ and yˆ are perpendicular vectors of unit length (unit vectors). This vector space, called R2 , is a 2-d space over the reals. Note that the same vector r can be described either by the basis vectors xˆ and yˆ or by any other set of basis vectors, such as −ˆy and xˆ ˆ r = xxˆ + yˆy = −y(−ˆy) + xx. (1.52) ˆ yˆ basis and (−y, x) in the So the components of the vector r are (x, y) in the x, −ˆy, xˆ basis. Each vector is unique, but its components depend upon the basis. Example 1.8 (3-space) linear combinations. Ordinary flat three-dimensional space is the set of all r = xxˆ + yˆy + zˆz. (1.53). in which x, y, and z are real numbers. It is a 3-d space over the reals. Example 1.9 (Matrices) Arrays of a given dimension and size can be added and multiplied by numbers, and so they form a vector space. For instance, all complex three-dimensional arrays aijk in which 1 ≤ i ≤ 3, 1 ≤ j ≤ 4, and 1 ≤ k ≤ 5 form a vector space over the complex numbers. Example 1.10 (Partial derivatives) Derivatives are vectors, so are partial derivatives. For instance, the linear combinations of x and y partial derivatives taken at x = y = 0 ∂ ∂ +b (1.54) a ∂x ∂y form a vector space. Example 1.11 (Functions) The space of all linear combinations of a set of functions fi (x) defined on an interval [a, b] f (x) = zi fi (x) (1.55) i. is a vector space over the natural, real, or complex numbers {zi }. Example 1.12 (States) In quantum mechanics, a state is represented by a vector, often written as ψ or in Dirac’s notation as |ψ. If c1 and c2 are complex numbers, and |ψ1 and |ψ2 are any two states, then the linear combination |ψ = c1 |ψ1 + c2 |ψ2 also is a possible state of the system.. 8. (1.56).

(30) 1.5 LINEAR OPERATORS. 1.5 Linear operators A linear operator A maps each vector U in its domain into a vector U = A(U) ≡ AU in its range in a way that is linear. So if U and V are two vectors in its domain and b and c are numbers, then A(bU + cV ) = bA(U) + cA(V ) = bAU + cAV .. (1.57). If the domain and the range are the same vector space S, then A maps each basis vector Bi of S into a linear combination of the basis vectors Bk ABi = a1i B1 + a2i B2 + · · · + aNi BN =. N . aki Bk .. (1.58). k=1. The square matrix aki represents the linear operator A in the Bk basis. The effect of A on any vector U = u1 B1 + u2 B2 + · · · + uN BN in S then is N N N N ui Bi = ui ABi = ui aki Bk AU = A i=1. =. N . N . k=1. i=1. So the kth component. u k. . i=1. i=1. k=1. aki ui Bk .. (1.59). of the vector U = AU is. u k = ak1 u1 + ak2 u2 + · · · + akN uN =. N . aki ui .. (1.60). i=1. Thus the column vector u of the components u k of the vector U = AU is the product u = a u of the matrix with elements aki that represents the linear operator A in the Bk basis and the column vector with components ui that represents the vector U in that basis. So in each basis, vectors and linear operators are represented by column vectors and matrices. Each linear operator is unique, but its matrix depends upon the basis. If we change from the Bk basis to another basis Bk. Bk =. N =1. uk B. (1.61). in which the N × N matrix uk has an inverse matrix u−1 ki so that N N N N N N −1 −1 −1. uki Bk = uki uk B = uk uki B = δi B = Bi , k=1. k=1. =1. =1. 9. k=1. =1. (1.62).

(31) LINEAR ALGEBRA. then the new basis vectors Bi are given by Bi. =. N . u−1 ki Bk .. (1.63). k=1. Thus (exercise 1.9) the linear operator A maps the basis vector Bi to ABi =. N . N . u−1 ki ABk =. k=1. u−1 ki ajk Bj =. j,k=1. N . uj ajk u−1 ki B .. (1.64). j,k,=1. a. B. So the matrix that represents A in the basis is related to the matrix a that represents it in the B basis by a similarity transformation a i. =. N . uj ajk u−1 ki. or. a = u a u−1. (1.65). jk=1. in matrix notation. Example 1.13 (Change of basis) Let the action of the linear operator A on the basis vectors {B1 , B2 } be AB1 = B2 and AB2 = 0. If the column vectors 1 0 b1 = and b2 = (1.66) 0 1 represent the basis vectors B1 and B2 , then the matrix 0 0 a= 1 0. (1.67). represents the linear operator A. But if we use the basis vectors 1 B1 = √ (B1 + B2 ) 2 then the vectors. 1 1 b 1 = √ 2 1. 1 and B2 = √ (B1 − B2 ) 2 1 1 and b 2 = √ −1 2. would represent B1 and B2 , and the matrix 1 1 1 a = 2 −1 −1. (1.68). (1.69). (1.70). would represent the linear operator A (exercise 1.10).. A linear operator A also may map a vector space S with basis Bk into a different vector space T with its own basis Ck . In this case, A maps the basis vector Bi into a linear combination of the basis vectors Ck 10.

(32) 1.6 INNER PRODUCTS. ABi =. M . aki Ck. (1.71). k=1. and an arbitrary vector U = u1 B1 + · · · + uN BN in S into the vector N M AU = aki ui Ck k=1. (1.72). i=1. in T.. 1.6 Inner products Most of the vector spaces used by physicists have an inner product. A positivedefinite inner product associates a number ( f , g) with every ordered pair of vectors f and g in the vector space V and satisfies the rules ( f , g) = (g, f )∗ ( f , z g + w h) = z (f , g) + w (f , h) (f , f ) ≥ 0 and (f , f ) = 0 ⇐⇒ f = 0. (1.73) (1.74) (1.75). in which f , g, and h are vectors, and z and w are numbers. The first rule says that the inner product is hermitian; the second rule says that it is linear in the second vector z g + w h of the pair; and the third rule says that it is positive definite. The first two rules imply that (exercise 1.11) the inner product is antilinear in the first vector of the pair (z g + w h, f ) = z∗ (g, f ) + w∗ (h, f ).. (1.76). A Schwarz inner product satisfies the first two rules (1.73, 1.74) for an inner product and the fourth (1.76) but only the first part of the third (1.75) ( f , f ) ≥ 0.. (1.77). This condition of nonnegativity implies (exercise 1.15) that a vector f of zero length must be orthogonal to all vectors g in the vector space V ( f , f ) = 0 ⇒ (g, f ) = 0 for all g ∈ V .. (1.78). So a Schwarz inner product is almost positive definite. Inner products of 4-vectors can be negative. To accommodate them we define an indefinite inner product without regard to positivity as one that satisfies the first two rules (1.73 & 1.74) and therefore also the fourth rule (1.76) and that instead of being positive definite is nondegenerate ( f , g) = 0 for all f ∈ V ⇒ g = 0. 11. (1.79).

(33) LINEAR ALGEBRA. This rule says that only the zero vector is orthogonal to all the vectors of the space. The positive-definite condition (1.75) is stronger than and implies nondegeneracy (1.79) (exercise 1.14). Apart from the indefinite inner products of 4-vectors in special and general relativity, most of the inner products physicists use are Schwarz inner products or positive-definite inner products. For such inner products, we can define the norm | f | = f of a vector f as the square-root of the nonnegative inner product ( f , f ) (1.80) f = (f , f ). The distance between two vectors f and g is the norm of their difference f −g.. (1.81). Example 1.14 (Euclidean space) The space of real vectors U, V with N components Ui , Vi forms an N-dimensional vector space over the real numbers with an inner product (U, V ) =. N . Ui Vi. (1.82). i=1. that is nonnegative when the two vectors are the same (U, U) =. N . Ui Ui =. i=1. N . Ui2 ≥ 0. (1.83). i=1. and vanishes only if all the components Ui are zero, that is, if the vector U = 0. Thus the inner product (1.82) is positive definite. When (U, V ) is zero, the vectors U and V are orthogonal. Example 1.15 (Complex euclidean space) The space of complex vectors with N components Ui , Vi forms an N-dimensional vector space over the complex numbers with inner product (U, V ) =. N . Ui∗ Vi = (V , U)∗ .. (1.84). i=1. The inner product (U, U) is nonnegative and vanishes (U, U) =. N . Ui∗ Ui =. i=1. N . |Ui |2 ≥ 0. (1.85). i=1. only if U = 0. So the inner product (1.84) is positive definite. If (U, V ) is zero, then U and V are orthogonal. 12.

(34) 1.6 INNER PRODUCTS. Example 1.16 (Complex matrices) For the vector space of N×L complex matrices A, B, . . ., the trace of the adjoint (1.28) of A multiplied by B is an inner product (A, B) = TrA† B =. L N . (A† )ji Bij =. i=1 j=1. L N . A∗ij Bij. (1.86). i=1 j=1. that is nonnegative when the matrices are the same (A, A) = TrA† A =. L N . A∗ij Aij =. i=1 j=1. L N . |Aij |2 ≥ 0. (1.87). i=1 j=1. and zero only when A = 0. So this inner product is positive definite.. A vector space with a positive-definite inner product (1.73–1.77) is called an inner-product space, a metric space, or a pre-Hilbert space. A sequence of vectors fn is a Cauchy sequence if for every > 0 there is an integer N( ) such that fn − fm < whenever both n and m exceed N( ). A sequence of vectors fn converges to a vector f if for every > 0 there is an integer N( ) such that f − fn < whenever n exceeds N( ). An innerproduct space with a norm defined as in (1.80) is complete if each of its Cauchy sequences converges to a vector in that space. A Hilbert space is a complete inner-product space. Every finite-dimensional inner-product space is complete and so is a Hilbert space. But the term Hilbert space more often is used to describe infinite-dimensional complete inner-product spaces, such as the space of all square-integrable functions (David Hilbert, 1862–1943).. Example 1.17 (The Hilbert space of square-integrable functions) For the vector space of functions (1.55), a natural inner product is b ( f , g) = dx f ∗ (x)g(x). (1.88) a. The squared norm f of a function f (x) is b 2 dx | f (x)|2 . f =. (1.89). a. A function is square integrable if its norm is finite. The space of all squareintegrable functions is an inner-product space; it also is complete and so is a Hilbert space. Example 1.18 (Minkowski inner product) The Minkowski or Lorentz inner product (p, x) of two 4-vectors p = (E/c, p1 , p2 , p3 ) and x = (ct, x1 , x2 , x3 ) is 13.

(35) LINEAR ALGEBRA. p · x − Et . It is indefinite, nondegenerate, and invariant under Lorentz transformations, and often is written as p · x or as px. If p is the 4-momentum of a freely moving physical particle of mass m, then p · p = p · p − E 2 /c2 = − c2 m2 ≤ 0.. (1.90). The Minkowski inner product satisfies the rules (1.73, 1.75, and 1.79), but it is not positive definite, and it does not satisfy the Schwarz inequality (Hermann Minkowski, 1864–1909; Hendrik Lorentz, 1853–1928).. 1.7 The Cauchy–Schwarz inequality For any two vectors f and g, the Schwarz inequality ( f , f ) (g, g) ≥ |( f , g)|2. (1.91). holds for any Schwarz inner product (and so for any positive-definite inner product). The condition (1.77) of nonnegativity ensures that for any complex number λ the inner product of the vector f − λg with itself is nonnegative (f − λg, f − λg) = ( f , f ) − λ∗ (g, f ) − λ( f , g) + |λ|2 (g, g) ≥ 0.. (1.92). Now if (g, g) = 0, then for (f − λg, f − λg) to remain nonnegative for all complex values of λ it is necessary that ( f , g) = 0 also vanish (exercise 1.15). Thus if (g, g) = 0, then the Schwarz inequality (1.91) is trivially true because both sides of it vanish. So we assume that (g, g) > 0 and set λ = (g, f )/(g, g). The inequality (1.92) then gives us (g, f ) ( f , g)(g, f ) (g, f ) g, f − g = (f,f) − ≥0 ( f − λg, f − λg) = f − (g, g) (g, g) (g, g) which is the Schwarz inequality (1.91) (Hermann Schwarz, 1843–1921) ( f , f )(g, g) ≥ |( f , g)|2 .. (1.93). Taking the square-root of each side, we get f g ≥ |( f , g)|.. (1.94). Example 1.19 (Some Schwarz inequalities) For the dot-product of two real 3-vectors r and R, the Cauchy–Schwarz inequality is (r · r) (R · R) ≥ (r · R)2 = (r · r) (R · R) cos2 θ. (1.95). where θ is the angle between r and R. The Schwarz inequality for two real n-vectors x is (x · x) (y · y) ≥ (x · y)2 = (x · x) (y · y) cos2 θ 14. (1.96).

(36) 1.8 LINEAR INDEPENDENCE AND COMPLETENESS. and it implies (Exercise 1.16) that x + y ≥ x + y. For two complex n-vectors u and v, the Schwarz inequality is ∗ ∗ ∗ 2 ∗ ∗ u · u v · v ≥ u · v = u · u v · v cos2 θ. (1.97). (1.98). and it implies (exercise 1.17) that u + v ≥ u + v.. (1.99). The inner product (1.88) of two complex functions f and g provides a somewhat different instance 2 b b b 2 2 ∗ dx |f (x)| dx |g(x)| ≥ dx f (x)g(x) (1.100) a a a of the Schwarz inequality.. 1.8 Linear independence and completeness A set of N vectors V1 , V2 , . . . , VN is linearly dependent if there exist numbers ci , not all zero, such that the linear combination c1 V1 + · · · + cN VN = 0. (1.101). vanishes. A set of vectors is linearly independent if it is not linearly dependent. A set {Vi } of linearly independent vectors is maximal in a vector space S if the addition of any other vector U in S to the set {Vi } makes the enlarged set {U, Vi } linearly dependent. A set of N linearly independent vectors V1 , V2 , . . . , VN that is maximal in a vector space S can represent any vector U in the space S as a linear combination of its vectors, U = u1 V1 + · · · + uN VN . For if we enlarge the maximal set {Vi } by including in it any vector U not already in it, then the bigger set {U, Vi } will be linearly dependent. Thus there will be numbers c, c1 , . . . , cN , not all zero, that make the sum c U + c1 V1 + · · · + cN VN = 0. (1.102). vanish. Now if c were 0, then the set {Vi } would be linearly dependent. Thus c

(37) = 0, and so we may divide by c and express the arbitrary vector U as a linear combination of the vectors Vi 1 U = − (c1 V1 + · · · + cN VN ) = u1 V1 + · · · + uN VN c. (1.103). with uk = −ck /c. So a set of linearly independent vectors {Vi } that is maximal in a space S can represent every vector U in S as a linear combination 15.

(38) LINEAR ALGEBRA. U = u1 V1 + . . . + uN VN of its vectors. The set {Vi } spans the space S; it is a complete set of vectors in the space S. A set of vectors {Vi } that spans a vector space S provides a basis for that space because the set lets us represent an arbitrary vector U in S as a linear combination of the basis vectors {Vi }. If the vectors of a basis are linearly dependent, then at least one of them is superfluous, and so it is convenient to have the vectors of a basis be linearly independent.. 1.9 Dimension of a vector space If V1 , . . . , VN and W1 , . . . , WM are two maximal sets of N and M linearly independent vectors in a vector space S, then N = M. Suppose M < N. Since the Us are complete, they span S, and so we may express each of the N vectors Vi in terms of the M vectors Wj Vi =. M . Aij Wj .. (1.104). j=1. Let Aj be the vector with components Aij . There are M < N such vectors, and each has N > M components. So it is always possible to find a nonzero N-dimensional vector C with components ci that is orthogonal to all M vectors Aj N . ci Aij = 0.. (1.105). i=1. Thus the linear combination N i=1. ci Vi =. M N . ci Aij Wj = 0. (1.106). i=1 j=1. vanishes, which implies that the N vectors Vi are linearly dependent. Since these vectors are by assumption linearly independent, it follows that N ≤ M. Similarly, one may show that M ≤ N. Thus M = N. The number of vectors in a maximal set of linearly independent vectors in a vector space S is the dimension of the vector space. Any N linearly independent vectors in an N-dimensional space form a basis for it.. 1.10 Orthonormal vectors Suppose the vectors V1 , V2 , . . . , VN are linearly independent. Then we can make out of them a set of N vectors Ui that are orthonormal (Ui , Uj ) = δij . 16. (1.107).

(39) 1.10 ORTHONORMAL VECTORS. There are many ways to do this, because there are many such sets of orthonormal vectors. We will use the Gram–Schmidt method. We set V1 , (1.108) U1 = (V1 , V1 ) so the first vector U1 is normalized. Next we set u2 = V2 + c12 U1 and require that u2 be orthogonal to U1 0 = (U1 , u2 ) = (U1 , c12 U1 + V2 ) = c12 + (U1 , V2 ).. (1.109). Thus c12 = −(U1 , V2 ), and so u2 = V2 − (U1 , V2 ) U1 .. (1.110). The normalized vector U2 then is U2 = . u2 (u2 , u2 ). .. (1.111). We next set u3 = V3 + c13 U1 + c23 U2 and ask that u3 be orthogonal to U1 0 = (U1 , u3 ) = (U1 , c13 U1 + c23 U2 + V3 ) = c13 + (U1 , V3 ). (1.112). and also to U2 0 = (U2 , u3 ) = (U2 , c13 U1 + c23 U2 + V3 ) = c23 + (U2 , V3 ).. (1.113). So c13 = −(U1 , V3 ) and c23 = −(U2 , V3 ), and we have u3 = V3 − (U1 , V3 ) U1 − (U2 , V3 ) U2 .. (1.114). The normalized vector U3 then is U3 = . u3 (u3 , u3 ). .. (1.115). We may continue in this way until we reach the last of the N linearly independent vectors. We require the kth unnormalized vector uk uk = Vk +. k−1 . cik Ui. (1.116). i=1. to be orthogonal to the k − 1 vectors Ui and find that cik = −(Ui , Vk ) so that uk = Vk −. k−1 . (Ui , Vk ) Ui .. (1.117). i=1. The normalized vector then is Uk = . uk (uk , uk ). .. A basis is more convenient if its vectors are orthonormal. 17. (1.118).

(40) LINEAR ALGEBRA. 1.11 Outer products From any two vectors f and g, we may make an operator A that takes any vector h into the vector f with coefficient (g, h) A h = f (g, h).. (1.119). Since for any vectors e, h and numbers z, w A (z h + w e) = f (g, z h + w e) = zf (g, h) + wf (g, e) = z A h + w A e (1.120) it follows that A is linear. If in some basis f , g, and h are vectors with components fi , gi , and hi , then the linear transformation is (Ah)i =. N . Aij hj = fi. j=1. N . gj∗ hj. (1.121). j=1. and in that basis A is the matrix with entries Aij = fi gj∗ .. (1.122). It is the outer product of the vectors f and g.. Example 1.20 (Outer product) If in some basis the vectors f and g are ⎛ ⎞ i 2 f = and g = ⎝ 1 ⎠ (1.123) 3 3i then their outer product is the matrix 2 −2i −i 1 −3i = A= 3 −3i. 2 3. −6i . −9i. (1.124). Dirac developed a notation that handles outer products very easily. Example 1.21 (Outer products) If the vectors f = |f and g = |g are ⎛ ⎞ a z |f = ⎝ b ⎠ and |g = w c then their outer products are ⎛ ∗ ⎞ az aw∗ |f g| = ⎝bz∗ bw∗ ⎠ cz∗ cw∗. and. |gf | =. 18. za∗ wa∗. zb∗ wb∗. zc∗ wc∗. (1.125). (1.126).

(41) 1.12 DIRAC NOTATION. as well as. ⎛ ∗ aa |f f | = ⎝ba∗ ca∗. ab∗ bb∗ cb∗. ⎞ ac∗ bc∗ ⎠ cc∗. and |gg| =. zz∗ wz∗. zw∗ . ww∗. (1.127). Students should feel free to write down their own examples.. 1.12 Dirac notation Outer products are important in quantum mechanics, and so Dirac invented a notation for linear algebra that makes them easy to write. In his notation, a vector f is a ket f = |f . The new thing in his notation is the bra g|. The inner product of two vectors (g, f ) is the bracket (g, f ) = g|f . A matrix element (g, Af ) is then (g, Af ) = g|A|f in which the bra and ket bracket the operator. In Dirac notation, the outer product A h = f (g, h) reads A |h = |f g|h, so that the outer product A itself is A = |f g|. Before Dirac, bras were implicit in the definition of the inner product, but they did not appear explicitly; there was no way to write the bra g| or the operator |f g|. If the kets |n form an orthonormal basis in an N-dimensional vector space, then we can expand an arbitrary ket in the space as |f =. N . cn |n.. (1.128). n=1. Since the basis vectors are orthonormal |n = δn , we can identify the coefficients cn by forming the inner product |f =. N . cn |n =. n=1. N . cn δ,n = c .. (1.129). n=1. The original expansion (1.128) then must be |f =. N n=1. N N N cn |n = n|f |n = |n n|f = |n n| |f . n=1. n=1. (1.130). n=1. Since this equation must hold for every vector |f in the space, it follows that the sum of outer products within the parentheses is the identity operator for the space I=. N . |n n|.. n=1. 19. (1.131).

(42) LINEAR ALGEBRA. Every set of kets |αn that forms an orthonormal basis αn |α = δn for the space gives us an equivalent representation of the identity operator I=. N . |αn αn | =. n=1. N . |n n|.. (1.132). n=1. Before Dirac, one could not write such equations. They provide for every vector |f in the space the expansions |f =. N . |αn αn |f =. n=1. N . |n n|f .. (1.133). n=1. Example 1.22 (Inner-product rules) In Dirac’s notation, the rules (1.73–1.76) of a positive-definite inner product are f |g = g|f ∗. (1.134). f |z1 g1 + z2 g2 = z1 f |g1 + z2 f |g2 z1 f1 + z2 f2 |g = z∗1 f1 |g + z∗2 f2 |g f |f ≥ 0 and f |f = 0. ⇐⇒ f = 0.. (1.135) (1.136) (1.137). Usually states in Dirac notation are labeled |ψ or by their quantum numbers |n, l, m, and one rarely sees plus signs or complex numbers or operators inside bras or kets. But one should. Example 1.23 (Gram–Schmidt) In Dirac notation, the formula (1.117) for the kth orthogonal linear combination of the vectors |V is k−1 k−1 |Ui Ui |Vk = I − |Ui Ui | |Vk (1.138) |uk = |Vk − i=1. i=1. and the formula (1.118) for the kth orthonormal linear combination of the vectors |V is |uk |Uk = √ . (1.139) uk |uk The vectors |Uk are not unique; they vary with the order of the |Vk .. Vectors and linear operators are abstract. The numbers we compute with are inner products like g|f and g|A|f . In terms of N orthonormal basis vectors |n with fn = n|f and gn∗ = g|n, we can use the expansion (1.131) to write these inner products as 20.

(43) 1.12 DIRAC NOTATION. g|f = g|I|f =. N . g|nn|f =. n=1. g|A|f = g|IAI|f =. N . gn∗ fn ,. n=1. N . g|nn|A||f =. n,=1. N . gn∗ An f (1.140). n,=1. in which An = n|A|. We often gather the inner products f = |f into a column vector f with components f = |f ⎛ ⎞ ⎛ ⎞ 1|f f1 ⎜ 2|f ⎟ ⎜ f2 ⎟ ⎜ ⎟ ⎜ ⎟ f =⎜ (1.141) ⎟ = ⎜ .. ⎟ .. ⎝ ⎠ ⎝ . . ⎠ N|f . f3. and the n|A| into a matrix A with matrix elements An = n|A|. If we also line up the inner products g|n = g|n∗ in a row vector that is the transpose of the complex conjugate of the column vector g ∗ (1.142) g† = 1|g∗ , 2|g∗ , . . . , N|g∗ = g1∗ , g2∗ , . . . , gN then we can write inner products in matrix notation as g|f = g† f and as g|A|f = g† Af . If we switch to a different basis, say from |ns to |αn s, then the components of the column vectors change from fn = n|f to fn = αn |f , and similarly those of the row vectors g† and of the matrix A change, but the bras, the kets, the linear operators, and the inner products g|f and g|A|f do not change because the identity operator is basis independent (1.132) g|f = g|A|f =. N N g|nn|f = g|αn αn |f , n=1 N . n=1. g|nn|A||f =. n,=1. N . g|αn αn |A|α α |f . (1.143). n,=1. Dirac’s outer products show how to change from one basis to another. The sum of outer products U=. N . |αn n|. (1.144). n=1. maps the ket | of the orthonormal basis we started with into |α U| =. N . |αn n| =. n=1. N n=1. 21. |αn δn = |α .. (1.145).

(44) LINEAR ALGEBRA. Example 1.24 (A simple change of basis) If the ket |αn of the new basis is simply |αn = |n + 1 with |αN = |N + 1 ≡ |1 then the operator that maps the N kets |n into the kets |αn is U=. N . |αn n| =. n=1. N . |n + 1n|.. (1.146). n=1. The square U 2 of U also changes the basis; it sends |n to |n + 2. The set of operators U k for k = 1, 2, . . . , N forms a group known as ZN .. 1.13 The adjoint of an operator In Dirac’s notation, the most general linear operator on an N-dimensional vector space is a sum of dyadics like z |n| in which z is a complex number and the kets |n and | are two of the N orthonormal kets that make up a basis for the space. The adjoint of this basic linear operator is (z |n|)† = z∗ |n|.. (1.147). Thus with z = n|A|, the most general linear operator on the space is N . A = IAI =. |nn|A||. (1.148). n,=1. and its adjoint A† is the operator IA† I †. A =. N n,=1. †. |nn|A || =. N . ∗. |n|A| n| =. n,=1. N . |n|A|n∗ |.. n,=1. †. It follows that n|A† | = |A|n∗ so that the matrix An that represents A† in this basis is † (1.149) An = n|A† | = |A|n∗ = A∗n = A∗nT in agreement with our definition (1.28) of the adjoint of a matrix as the transpose of its complex conjugate, A† = A∗ T . We also have g|A† f = g|A† |f = f |A|g∗ = f |Ag∗ = Ag|f . Taking the adjoint of the adjoint is by (1.147) † † (z |n|)† = z∗ |n| = z |n|. (1.150). (1.151). the same as doing nothing at all. This also follows from the matrix formula (1.149) because both (A∗ )∗ = A and (AT )T = A, and so † ∗ A† = A∗ T T = A, (1.152) the adjoint of the adjoint of a matrix is the original matrix. 22.

(45) 1.15 REAL, SYMMETRIC LINEAR OPERATORS. Before Dirac, the adjoint A† of a linear operator A was defined by (g, A† f ) = (A g, f ) = ( f , A g)∗ .. (1.153). This definition also implies that A†† = A since (g, A†† f ) = (A† g, f ) = ( f , A† g)∗ = (Af , g)∗ = (g, Af ).. (1.154). We also have (g, Af ) = (g, A†† f ) = (A† g, f ).. 1.14 Self-adjoint or hermitian linear operators An operator A that is equal to its adjoint, A† = A, is self adjoint or hermitian. In view of (1.149), the matrix elements of a self-adjoint linear operator A satisfy n|A† | = |A|n∗ = n|A| in any orthonormal basis. So a matrix that represents a hermitian operator is equal to the transpose of its complex conjugate †. An = n|A| = n|A† | = |A|n∗ = A∗T n = An .. (1.155). We also have g| A |f = A g|f = f |A g∗ = f | A |g∗. (1.156). and in pre-Dirac notation (g, A f ) = (A g, f ) = ( f , A g)∗ .. (1.157). A matrix Aij that is real and symmetric or imaginary and antisymmetric is hermitian. But a self-adjoint linear operator A that is represented by a matrix Aij that is real and symmetric (or imaginary and antisymmetric) in one orthonormal basis will not in general be represented by a matrix that is real and symmetric (or imaginary and antisymmetric) in a different orthonormal basis, but it will be represented by a hermitian matrix in every orthonormal basis. A ket |a is an eigenvector of a linear operator A with eigenvalue a if A|a = a |a . As we’ll see in section 1.28, hermitian matrices have real eigenvalues and complete sets of orthonormal eigenvectors. Hermitian operators and matrices represent physical variables in quantum mechanics.. 1.15 Real, symmetric linear operators In quantum mechanics, we usually consider complex vector spaces, that is, spaces in which the vectors |f are complex linear combinations |f =. N i=1. of complex orthonormal basis vectors |i. 23. zi |i. (1.158).

(46) LINEAR ALGEBRA. But real vector spaces also are of interest. A real vector space is a vector space in which the vectors |f are real linear combinations |f =. N . xn |n. (1.159). n=1. of real orthonormal basis vectors, x∗n = xn and |n∗ = |n. A real linear operator A on a real vector space N . A=. |nn|A|mm| =. n,m=1. N . |nAnm m|. (1.160). n,m=1. A∗nm. = Anm . A real linear operator A that is self is represented by a real matrix adjoint on a real vector space satisfies the condition (1.157) of hermiticity but with the understanding that complex conjugation has no effect (g, A f ) = (A g, f ) = ( f , A g)∗ = ( f , A g).. (1.161). Thus its matrix elements are symmetric, g|A|f = f |A|g. Since A is hermitian as well as real, the matrix Anm that represents it (in a real basis) is real and hermitian, and so is symmetric Anm = A∗mn = Amn .. 1.16 Unitary operators A unitary operator U is one whose adjoint is its inverse U U † = U † U = I.. (1.162). Any operator that changes from one orthonormal basis |n to another |αn U=. N . |αn n|. (1.163). n=1. is unitary since UU † =. N . |αn n|. n=1. =. N . N . |mαm | =. m=1. |αn n|mαm |. n,m=1. |αn δn,m αm | =. n,m=1. N . N . (1.164). |αn αn | = I. n=1. as well as U †U =. N m=1. |mαm |. N . |αn n| =. n=1. 24. N n=1. |nn| = I.. (1.165).

(47) 1.17 HILBERT SPACE. A unitary operator maps any orthonormal basis |n into another orthonormal basis |αn . For if |αn = U|n, then αn |αm = δn,m (exercise 1.22). If we multiply the relation |αn = U|n by the bra n| and then sum over the index n, we get N n=1. |αn n| =. N . U|nn| = U. n=1. N . |nn| = U.. (1.166). n=1. Every unitary operator is a basis-changing operator, and vice versa. Inner products do not change under unitary transformations because g|f = g|U † U|f = Ug|U|f = Ug|Uf , which in pre-Dirac notation is (g, f ) = (g, U † Uf ) = (Ug, Uf ). Unitary matrices have unimodular determinants because the determinant of the product of two matrices is the product of their determinants (1.204) and because transposition doesn’t change the value of a determinant (1.194) 1 = |I| = |UU † | = |U||U † | = |U||U T |∗ = |U||U|∗ .. (1.167). A unitary matrix that is real is orthogonal and satisfies OOT = OT O = I.. (1.168). 1.17 Hilbert space We have mostly been talking about linear operators that act on finitedimensional vector spaces and that can be represented by matrices. But infinite-dimensional vector spaces and the linear operators that act on them play central roles in electrodynamics and quantum mechanics. For instance, the Hilbert space H of all “wave” functions ψ(x, t) that are square integrable over three-dimensional space at all times t is of infinite dimension. In one space dimension, the state |x represents a particle at position x and is an eigenstate of the hermitian position operator x with eigenvalue x , that is, x|x = x |x . These states form a basis that is orthogonal in the sense that x|x = 0 for x

(48) = x and normalized in the sense that x|x = δ(x − x ) in which δ(x − x ) is Dirac’s delta function. The delta function δ(x − x ) actually is a functional δx that maps any suitably smooth function f into (1.169) δx [f ] = δ(x − x ) f (x) dx = f (x ), its value at x . Another basis for the Hilbert space of one-dimensional quantum mechanics is made of the states |p of well-defined momentum. The state |p represents a particle or system with momentum p . It is an eigenstate of the hermitian 25.

(49) LINEAR ALGEBRA. momentum operator p with eigenvalue p , that is, p|p = p |p . The momentum states also are orthonormal in Dirac’s sense, p|p = δ(p − p ). The operator that translates a system in space by a distance a is U(a) = |x + ax| dx. (1.170) It maps the state |x to the state |x + a and is unitary (exercise 1.23). Remarkably, this translation operator is an exponential of the momentum operator U(a) = exp(−i p a/h¯ ) in which h¯ = h/2π = 1.054×10−34 Js is Planck’s constant divided by 2π. In two dimensions, with basis states |x, y that are orthonormal in Dirac’s sense, x, y|x , y = δ(x − x )δ(y − y ), the unitary operator U(θ) = |x cos θ − y sin θ, x sin θ + y cos θx, y| dxdy (1.171) rotates a system in space by the angle θ. This rotation operator is the exponential U(θ) = exp(−i θ Lz /h¯ ) in which the z component of the angular momentum is Lz = x py − y px . We may carry most of our intuition about matrices over to these unitary transformations that change from one infinite basis to another. But we must use common sense and keep in mind that infinite sums and integrals do not always converge.. 1.18 Antiunitary, antilinear operators Certain maps on states |ψ → |ψ , such as those involving time reversal, are implemented by operators K that are antilinear K (zψ + wφ) = K (z|ψ + w|φ) = z∗ K|ψ+w∗ K|φ = z∗ Kψ +w∗ Kφ (1.172) and antiunitary (Kφ, Kψ) = Kφ|Kψ = (φ, ψ)∗ = φ|ψ∗ = ψ|φ = (ψ, φ) .. (1.173). In Dirac notation, these rules are K(z|ψ) = z∗ ψ| and K(wφ|) = w∗ |φ.. 1.19 Symmetry in quantum mechanics In quantum mechanics, a symmetry is a map of states |ψ → |ψ and |φ → |φ that preserves inner products and probabilities |φ |ψ |2 = |φ|ψ|2 . 26. (1.174).

(50) 1.20 DETERMINANTS. Eugene Wigner (1902–1995) showed that every symmetry in quantum mechanics can be represented either by an operator U that is linear and unitary or by an operator K that is antilinear and antiunitary. The antilinear, antiunitary case seems to occur only when the symmetry involves time reversal. Most symmetries are represented by operators that are linear and unitary. Unitary operators are of great importance in quantum mechanics. We use them to represent rotations, translations, Lorentz transformations, and internal-symmetry transformations.. 1.20 Determinants The determinant of a 2 × 2 matrix A is det A = |A| = A11 A22 − A21 A12 .. (1.175). In terms of the 2 × 2 antisymmetric (eij = −eji ) matrix e12 = 1 = −e21 with e11 = e22 = 0, this determinant is det A =. 2 2 . eij Ai1 Aj2 .. (1.176). i=1 j=1. It’s also true that ek det A =. 2 2 . eij Aik Aj .. (1.177). i=1 j=1. These definitions and results extend to any square matrix. If A is a 3 × 3 matrix, then its determinant is det A =. 3 . eijk Ai1 Aj2 Ak3. (1.178). i,j,k=1. in which eijk is totally antisymmetric with e123 = 1, and the sums over i, j, and k run from 1 to 3. More explicitly, this determinant is det A =. 3 . eijk Ai1 Aj2 Ak3. i,j,k=1. =. 3 i=1. Ai1. 3 . eijk Aj2 Ak3. j,k=1. = A11 (A22 A33 − A32 A23 ) + A21 (A32 A13 − A12 A33 ) + A31 (A12 A23 − A22 A13 ) .. 27. (1.179).

(51) LINEAR ALGEBRA. The terms within parentheses are the 2 × 2 determinants (called minors) of the matrix A without column 1 and row i, multiplied by (−1)1+i : det A = A11 (−1)2 (A22 A33 − A32 A23 ) + A21 (−1)3 (A12 A33 − A32 A13 ) + A31 (−1)4 (A12 A23 − A22 A13 ) = A11 C11 + A21 C21 + A31 C31. (1.180). The minors multiplied by (−1)1+i are called cofactors: C11 = A22 A33 − A23 A32 , C21 = A32 A13 − A12 A33 , C31 = A12 A23 − A22 A13 .. (1.181). Example 1.25 (Determinant of a 3 × 3 matrix) The determinant of a 3 × 3 matrix is the dot-product of the vector of its first row with the cross-product of the vectors of its second and third rows: U1 U2 U3 3 3 V1 V2 V3 = e U V W = Ui (V × W )i = U · (V × W ) ijk i j k W1 W2 W3 i,j,k=1 i=1 which is called the scalar triple product.. Laplace used the totally antisymmetric symbol ei1 i2 ...iN with N indices and with e123...N = 1 to define the determinant of an N × N matrix A as det A =. N . ei1 i2 ...iN Ai1 1 Ai2 2 . . . AiN N. (1.182). i1 ,i2 ,...,iN =1. in which the sums over i1 . . . iN run from 1 to N. In terms of cofactors, two forms of his expansion of this determinant are det A =. N . Aik Cik =. i=1. N . Aik Cik. (1.183). k=1. in which the first sum is over the row index i but not the (arbitrary) column index k, and the second sum is over the column index k but not the (arbitrary) row index i. The cofactor Cik is (−1)i+k Mik in which the minor Mik is the determinant of the (N − 1) × (N − 1) matrix A without its ith row and kth column. It’s also true that ek1 k2 ...kN det A =. N . ei1 i2 ...iN Ai1 k1 Ai2 k2 . . . AiN kN .. i1 ,i2 ,...,iN =1. 28. (1.184).

(52) 1.20 DETERMINANTS. The key feature of a determinant is that it is an antisymmetric combination of products of the elements Aik of a matrix A. One implication of this antisymmetry is that the interchange of any two rows or any two columns changes the sign of the determinant. Another is that if one adds a multiple of one column to another column, for example a multiple xAi2 of column 2 to column 1, then the determinant det A =. N . ei1 i2 ...iN Ai1 1 + xAi1 2 Ai2 2 . . . AiN N. (1.185). i1 ,i2 ,...,in =1. is unchanged. The reason is that the extra term δ det A vanishes δ det A =. N . x ei1 i2 ...iN Ai1 2 Ai2 2 . . . AiN N = 0. (1.186). i1 ,i2 ,...,iN =1. because it is proportional to a sum of products of a factor ei1 i2 ...iN that is antisymmetric in i1 and i2 and a factor Ai1 2 Ai2 2 that is symmetric in these indices. For instance, when i1 and i2 are 5 & 7 and 7 & 5, the two terms cancel e57...iN A52 A72 . . . AiN N + e75...iN A72 A52 . . . AiN N = 0. (1.187). because e57...iN = −e75...iN . By repeated additions of x2 Ai2 , x3 Ai3 , etc. to Ai1 , we can change the first column of the matrix A to a linear combination of all the columns Ai1 −→ Ai1 +. N . xk Aik. (1.188). k=2. without changing det A. In this linear combination, the coefficients xk are arbitrary. The analogous operation with arbitrary yk N . Ai −→ Ai +. yk Aik. (1.189). k=1,k

(53) =. replaces the th column by a linear combination of all the columns without changing det A. Suppose that the columns of an N × N matrix A are linearly dependent (section 1.8), so that the linear combination of columns N . yk Aik = 0. for i = 1, . . . N. (1.190). k=1. vanishes for some coefficients yk not all zero. Suppose y1

(54) = 0. Then by adding suitable linear combinations of columns 2 through N to column 1, we could make all the modified elements A i1 of column 1 vanish without changing det A. 29.

(55) LINEAR ALGEBRA. But then det A as given by (1.182) would vanish. Thus the determinant of any matrix whose columns are linearly dependent must vanish. The converse also is true: if columns of a matrix are linearly independent, then the determinant of that matrix can not vanish. The reason is that any linearly independent set of vectors is complete (section 1.8). Thus if the columns of a matrix A are linearly independent and therefore complete, some linear combination of all columns 2 through N when added to column 1 will convert column 1 into a (nonzero) multiple of the N-dimensional column vector (1, 0, 0, . . . 0), say (c1 , 0, 0, . . . 0). Similar operations will convert column 2 into a (nonzero) multiple of the column vector (0, 1, 0, . . . 0), say (0, c2 , 0, . . . 0). Continuing in this way, we may convert the matrix A to a matrix with nonzero entries along the main diagonal and zeros everywhere else. The determinant det A is then the product of the nonzero diagonal entries c1 c2 . . . cN

(56) = 0, and so det A can not vanish. We may extend these arguments to the rows of a matrix. The addition to row k of a linear combination of the other rows Aki −→ Aki +. N . z Ai. (1.191). =1,

(57) =k. does not change the value of the determinant. In this way, one may show that the determinant of a matrix vanishes if and only if its rows are linearly dependent. The reason why these results apply to the rows as well as to the columns is that the determinant of a matrix A may be defined either in terms of the columns as in definitions (1.182 & 1.184) or in terms of the rows: det A =. N . ei1 i2 ...iN A1i1 A2i2 . . . ANiN ,. (1.192). ei1 i2 ...iN Ak1 i1 Ak2 i2 . . . AkN iN .. (1.193). i1 ,i2 ,...,iN =1. ek1 k2 ...kN det A =. N i1 ,i2 ,...,iN =1. These and other properties of determinants follow from a study of permutations (section 10.13). Detailed proofs are in Aitken (1959). By comparing the row (1.182 & 1.184) and column (1.192 & 1.193) definitions of determinants, we see that the determinant of the transpose of a matrix is the same as the determinant of the matrix itself: (1.194) det AT = det A. Let us return for a moment to Laplace’s expansion (1.183) of the determinant det A of an N × N matrix A as a sum of Aik Cik over the row index i with the column index k held fixed 30.

(58) 1.20 DETERMINANTS. det A =. N . Aik Cik. (1.195). i=1. in order to prove that δk det A =. N . Aik Ci .. (1.196). i=1. For k = , this formula just repeats Laplace’s expansion (1.195). But for k

(59) = , it is Laplace’s expansion for the determinant of a matrix A that is the same as A but with its th column replaced by its kth one. Since the matrix A has two identical columns, its determinant vanishes, which explains (1.196) for k

(60) = . This rule (1.196) provides a formula for the inverse of a matrix A whose determinant does not vanish. Such matrices are said to be nonsingular. The inverse A−1 of an N × N nonsingular matrix A is the transpose of the matrix of cofactors divided by det A Ci CT A−1 = or A−1 = . (1.197) i det A det A To verify this formula, we use it for A−1 in the product A−1 A and note that by (1.196) the kth entry of the product A−1 A is just δk A−1 A. k. =. N . A−1. i=1. i. Aik =. N Ci Aik = δk . det A. (1.198). i=1. Example 1.26 (Inverting a 2 × 2 matrix) Let’s apply our formula (1.197) to find the inverse of the general 2 × 2 matrix a b A= . (1.199) c d We find then A. −1. 1 = ad − bc. . d −c. −b , a. (1.200). which is the correct inverse as long as ad

(61) = bc.. The simple example of matrix multiplication ⎛ ⎞⎛ ⎞ ⎛ a b c 1 x y a xa + b ⎝d e f ⎠ ⎝0 1 z ⎠ = ⎝d xd + e g h i 0 0 1 g xg + h. ⎞ ya + zb + c yd + ze + f ⎠ yg + zh + i. (1.201). shows that the operations (1.189) on columns that don’t change the value of the determinant can be written as matrix multiplication from the right by a matrix 31.

(62) LINEAR ALGEBRA. that has unity on its main diagonal and zeros below. Now consider the matrix product A 0 I B A AB = (1.202) −I B 0 I −I 0 in which A and B are N × N matrices, I is the N × N identity matrix, and 0 is the N × N matrix of all zeros. The second matrix on the left-hand side has unity on its main diagonal and zeros below, and so it does not change the value of the determinant of the matrix to its left, which then must equal that of the matrix on the right-hand side: . A det −I. 0 A = det B −I. AB . 0. (1.203). By using Laplace’s expansion (1.183) along the first column to evaluate the determinant on the left-hand side and his expansion along the last row to compute the determinant on the right-hand side, one finds that the determinant of the product of two matrices is the product of the determinants det A det B = det AB.. (1.204). Example 1.27 (Two 2 × 2 matrices) When the matrices A and B are both 2 × 2, the two sides of (1.203) are ⎞ ⎛ 0 0 a11 a12 ⎜a21 a22 0 0 ⎟ A 0 ⎟ det = det ⎜ ⎝−1 0 b11 b12 ⎠ −I B 0 −1 b21 b22 = a11 a22 det B − a21 a12 det B = det A det B (1.205) and det. A −I. ⎞ ⎛ a11 a12 ab11 ab12 ⎜a21 a22 ab21 ab22 ⎟ AB ⎟ = det ⎜ ⎝−1 0 0 0 ⎠ 0 0 −1 0 0 = (−1)C42 = (−1)(−1) det AB = det AB . (1.206). and so they give the product rule det A det B = det AB.. Often one uses the notation |A| = det A to denote a determinant. In this more compact notation, the obvious generalization of the product rule is. 32.

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