A MATLAB Tutorial
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Package 'Popbio'
Package ‘popbio’ February 1, 2020 Version 2.7 Author Chris Stubben, Brook Milligan, Patrick Nantel Maintainer Chris Stubben <[email protected]> Title Construction and Analysis of Matrix Population Models License GPL-3 Encoding UTF-8 LazyData true Suggests quadprog Description Construct and analyze projection matrix models from a demography study of marked individu- als classified by age or stage. The package covers methods described in Matrix Population Mod- els by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002). RoxygenNote 6.1.1 NeedsCompilation no Repository CRAN Date/Publication 2020-02-01 20:10:02 UTC R topics documented: 01.Introduction . .3 02.Caswell . .3 03.Morris . .5 aq.census . .6 aq.matrix . .7 aq.trans . .8 betaval............................................9 boot.transitions . 11 calathea . 13 countCDFxt . 14 damping.ratio . 15 eigen.analysis . 16 elasticity . 18 1 2 R topics documented: extCDF . 19 fundamental.matrix . 20 generation.time . 21 grizzly . 22 head2 . 24 hudcorrs . 25 hudmxdef . 25 hudsonia . 26 hudvrs . 27 image2 . 28 Kendall . 29 lambda . 31 lnorms . 32 logi.hist.plot . 33 LTRE ............................................ 34 matplot2 . 37 matrix2 . 38 mean.list . 39 monkeyflower . 40 multiresultm . 42 nematode . 43 net.reproductive.rate . 44 pfister.plot . 45 pop.projection . 46 projection.matrix . 48 QPmat . 50 reproductive.value . 51 resample . 52 secder . 53 sensitivity . 55 splitA . 56 stable.stage . 57 stage.vector.plot . 58 stoch.growth.rate . 59 stoch.projection . 60 stoch.quasi.ext . 62 stoch.sens . 63 stretchbetaval . 64 teasel . 65 test.census . 66 tortoise . 67 var2 ............................................. 68 varEst . 69 vitalsens . 70 vitalsim . 71 whale . 74 woodpecker . 74 Index 76 01.Introduction 3 01.Introduction Introduction to the popbio Package Description Popbio is a package for the construction and analysis of matrix population models. -
Determinant Notes
68 UNIT FIVE DETERMINANTS 5.1 INTRODUCTION In unit one the determinant of a 2× 2 matrix was introduced and used in the evaluation of a cross product. In this chapter we extend the definition of a determinant to any size square matrix. The determinant has a variety of applications. The value of the determinant of a square matrix A can be used to determine whether A is invertible or noninvertible. An explicit formula for A–1 exists that involves the determinant of A. Some systems of linear equations have solutions that can be expressed in terms of determinants. 5.2 DEFINITION OF THE DETERMINANT a11 a12 Recall that in chapter one the determinant of the 2× 2 matrix A = was a21 a22 defined to be the number a11a22 − a12 a21 and that the notation det (A) or A was used to represent the determinant of A. For any given n × n matrix A = a , the notation A [ ij ]n×n ij will be used to denote the (n −1)× (n −1) submatrix obtained from A by deleting the ith row and the jth column of A. The determinant of any size square matrix A = a is [ ij ]n×n defined recursively as follows. Definition of the Determinant Let A = a be an n × n matrix. [ ij ]n×n (1) If n = 1, that is A = [a11], then we define det (A) = a11 . n 1k+ (2) If na>=1, we define det(A) ∑(-1)11kk det(A ) k=1 Example If A = []5 , then by part (1) of the definition of the determinant, det (A) = 5. -
Workshop on Modern Applied Mathematics PK 2012 Kraków 23 — 24 November 2012
Workshop on Modern Applied Mathematics PK 2012 Kraków 23 — 24 November 2012 Warsztaty z Nowoczesnej Matematyki i jej Zastosowań Kraków 23 — 24 listopada 2012 Kraków 23 — 24 November 2012 3 Contents 1 Introduction 5 1.1 Organizing Committee . 6 1.2 Scientific Committee . 6 2 List of Participants 7 3 Abstracts 11 3.1 On residually finite groups Orest D. Artemovych . 11 3.2 Some properties of Bernstein-Durrmeyer operators Magdalena Baczyńska . 12 3.3 Some Applications of Gröbner Bases Katarzyna Bolek . 14 3.4 Criteria for normality via C0-semigroups and moment sequences Dariusz Cichoń . 15 3.5 Picard–Fuchs operator for certain one parameter families of Calabi–Yau threefolds Sławomir Cynk . 16 3.6 Inference with Missing data Christiana Drake . 17 3.7 An outer measure on a commutative ring Dariusz Dudzik . 18 3.8 Resampling methods for weakly dependent sequences Elżbieta Gajecka-Mirek . 18 3.9 Graphs with path systems of length k in a hamiltonian cycle Grzegorz Gancarzewicz . 19 3.10 Hausdorff gaps and automorphisms of P(N)=fin Magdalena Grzech . 20 3.11 Functional Regression Models with Structured Penalties Jarosław Harezlak, Madan Kundu . 21 3.12 Approximation of function of two variables from exponential weight spaces Monika Herzog . 22 3.13 The existence of a weak solution of the semilinear first-order differential equation in a Banach space Mariusz Jużyniec . 25 3.14 Bayesian inference for cyclostationary time series Oskar Knapik . 26 4 Workshop on Modern Applied Mathematics PK 2012 3.15 Hausdorff limit in o-minimal structures Beata Kocel–Cynk . 27 3.16 Resampling methods for nonstationary time series Jacek Leśkow . -
Projects and Publications of the Applied Mathematics Division
NATIONAL BUREAU OF STANDARDS REPORT 4546 PROJECTS and PUBLICATIONS of the APPLIED MATHEMATICS DIVISION A Quarterly Report October through December 1 955 U. S. DEPARTMENT OF COMMERCE NATIONAL BUREAU OF STANDARDS FOR OFFICIAL DISTRIBUTION U. S. DEPARTMENT OF COMMERCE Sinclair Weeks, Secretary NATIONAL BUREAU OF STANDARDS A. V. Astin, Director THE NATIONAL BUREAU OF STANDARDS The 6cope of activities of the National Bureau of Standards is suggested in the following listing of the divisions and sections engaged in technical work. In general, each section is engaged in specialized research, development, and engineering in the field indicated by its title. A brief description of the activities, and of the resultant reports and publications, appears on the inside of the back cover of this report. Electricity and Electronics. Resistance and Reactance. Electron Tubes. Electrical Instru- ments. Magnetic Measurements. Process Technology. Engineering Electronics. Electronic Instrumentation. Electrochemistry. Optics and Metrology. Photometry and Colorimetry. Optical Instruments. Photographic Technology. Length. Engineering Metrology. Heat and Power. Temperature Measurements. Thermodynamics. Cryogenic Physics. Engines and Lubrication. Engine Fuels. Atomic and Radiation Physics. Spectroscopy. Radiometry. Mass Spectrometry. Solid State Physics. Electron Physics. Atomic Physics. Nuclear Physics. Radioactivity. X-rays. Betatron. Nucleonic Instrumentation. Radiological Equipment. AEC Radiation Instruments. Chemistry. Organic Coatings. Surface Chemistry. Organic -
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. -
Projects and Publications of the Applied Mathematics Division
NATIONAL BUREAU OF STANDARDS REPORT 4658 PROJECTS and PUBLICATIONS of the APPLIED MATHEMATICS DIVISION A Quarterly Report January through March, 1956 U. S. DEPARTMENT OF COMMERCE NATIONAL BUREAU OF STANDARDS FOR OFFICIAL DISTRIBUTION U. S. DEPARTMENT OF COMMERCE Sinclair Weeks, Secretary NATIONAL BUREAU OF STANDARDS A. V. Astin, Director THE NATIONAL BUREAU OF STANDARDS The scope of activities of the National Bureau of Standards is suggested in the following listing of the divisions and sections engaged in technical work. In general, each section is engaged in specialized research, development, and engineering in the field indicated by its title. A brief description of the activities, and of the resultant reports and publications, appears on the inside of the back cover of this report. Electricity and Electronics. Resistance and Reactance. Electron Tubes. Electrical Instru- ments. Magnetic Measurements. Process Technology. Engineering Electronics. Electronic Instrumentation. Electrochemistry. Optics and Metrology. Photometry and Colorimetry. Optical Instruments. Photographic Technology. Length. Engineering Metrology. Heat and Power. Temperature Measurements. Thermodynamics. Cryogenic Physics. Engines and Lubrication. Engine Fuels. Atomic and Radiation Physics. Spectroscopy. Radiometry. Mass Spectrometry. Solid State Physics. Electron Physics. Atomic Physics. Nuclear Physics. Radioactivity. X-rays. Betatron. Nucleonic Instrumentation. Radiological Equipment. AEC Radiation Instruments. Chemistry. Organic Coatings. Surface Chemistry. Organic -
MATH 304 Linear Algebra Lecture 24: Scalar Product. Vectors: Geometric Approach
MATH 304 Linear Algebra Lecture 24: Scalar product. Vectors: geometric approach B A B′ A′ A vector is represented by a directed segment. • Directed segment is drawn as an arrow. • Different arrows represent the same vector if • they are of the same length and direction. Vectors: geometric approach v B A v B′ A′ −→AB denotes the vector represented by the arrow with tip at B and tail at A. −→AA is called the zero vector and denoted 0. Vectors: geometric approach v B − A v B′ A′ If v = −→AB then −→BA is called the negative vector of v and denoted v. − Vector addition Given vectors a and b, their sum a + b is defined by the rule −→AB + −→BC = −→AC. That is, choose points A, B, C so that −→AB = a and −→BC = b. Then a + b = −→AC. B b a C a b + B′ b A a C ′ a + b A′ The difference of the two vectors is defined as a b = a + ( b). − − b a b − a Properties of vector addition: a + b = b + a (commutative law) (a + b) + c = a + (b + c) (associative law) a + 0 = 0 + a = a a + ( a) = ( a) + a = 0 − − Let −→AB = a. Then a + 0 = −→AB + −→BB = −→AB = a, a + ( a) = −→AB + −→BA = −→AA = 0. − Let −→AB = a, −→BC = b, and −→CD = c. Then (a + b) + c = (−→AB + −→BC) + −→CD = −→AC + −→CD = −→AD, a + (b + c) = −→AB + (−→BC + −→CD) = −→AB + −→BD = −→AD. Parallelogram rule Let −→AB = a, −→BC = b, −−→AB′ = b, and −−→B′C ′ = a. Then a + b = −→AC, b + a = −−→AC ′. -
Package 'Matrix'
Package ‘Matrix’ March 8, 2019 Version 1.2-16 Date 2019-03-04 Priority recommended Title Sparse and Dense Matrix Classes and Methods Contact Doug and Martin <[email protected]> Maintainer Martin Maechler <[email protected]> Description A rich hierarchy of matrix classes, including triangular, symmetric, and diagonal matrices, both dense and sparse and with pattern, logical and numeric entries. Numerous methods for and operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries. Depends R (>= 3.2.0) Imports methods, graphics, grid, stats, utils, lattice Suggests expm, MASS Enhances MatrixModels, graph, SparseM, sfsmisc Encoding UTF-8 LazyData no LazyDataNote not possible, since we use data/*.R *and* our classes BuildResaveData no License GPL (>= 2) | file LICENCE URL http://Matrix.R-forge.R-project.org/ BugReports https://r-forge.r-project.org/tracker/?group_id=61 Author Douglas Bates [aut], Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Timothy A. Davis [ctb] (SuiteSparse and 'cs' C libraries, notably CHOLMOD, AMD; collaborators listed in dir(pattern = '^[A-Z]+[.]txt$', full.names=TRUE, system.file('doc', 'SuiteSparse', package='Matrix'))), Jens Oehlschlägel [ctb] (initial nearPD()), Jason Riedy [ctb] (condest() and onenormest() for octave, Copyright: Regents of the University of California), R Core Team [ctb] (base R matrix implementation) 1 2 R topics documented: Repository CRAN Repository/R-Forge/Project matrix Repository/R-Forge/Revision 3291 Repository/R-Forge/DateTimeStamp 2019-03-04 11:00:53 Date/Publication 2019-03-08 15:33:45 UTC NeedsCompilation yes R topics documented: abIndex-class . .4 abIseq . .6 all-methods . .7 all.equal-methods . -
Inner Product Spaces
CHAPTER 6 Woman teaching geometry, from a fourteenth-century edition of Euclid’s geometry book. Inner Product Spaces In making the definition of a vector space, we generalized the linear structure (addition and scalar multiplication) of R2 and R3. We ignored other important features, such as the notions of length and angle. These ideas are embedded in the concept we now investigate, inner products. Our standing assumptions are as follows: 6.1 Notation F, V F denotes R or C. V denotes a vector space over F. LEARNING OBJECTIVES FOR THIS CHAPTER Cauchy–Schwarz Inequality Gram–Schmidt Procedure linear functionals on inner product spaces calculating minimum distance to a subspace Linear Algebra Done Right, third edition, by Sheldon Axler 164 CHAPTER 6 Inner Product Spaces 6.A Inner Products and Norms Inner Products To motivate the concept of inner prod- 2 3 x1 , x 2 uct, think of vectors in R and R as x arrows with initial point at the origin. x R2 R3 H L The length of a vector in or is called the norm of x, denoted x . 2 k k Thus for x .x1; x2/ R , we have The length of this vector x is p D2 2 2 x x1 x2 . p 2 2 x1 x2 . k k D C 3 C Similarly, if x .x1; x2; x3/ R , p 2D 2 2 2 then x x1 x2 x3 . k k D C C Even though we cannot draw pictures in higher dimensions, the gener- n n alization to R is obvious: we define the norm of x .x1; : : : ; xn/ R D 2 by p 2 2 x x1 xn : k k D C C The norm is not linear on Rn. -
Properties of Transpose
3.2, 3.3 Inverting Matrices P. Danziger Properties of Transpose Transpose has higher precedence than multiplica- tion and addition, so T T T T AB = A B and A + B = A + B As opposed to the bracketed expressions (AB)T and (A + B)T Example 1 1 2 1 1 0 1 Let A = ! and B = !. 2 5 2 1 1 0 Find ABT , and (AB)T . T 1 1 1 2 1 1 0 1 1 2 1 0 1 ABT = ! ! = ! 0 1 2 5 2 1 1 0 2 5 2 B C @ 1 0 A 2 3 = ! 4 7 Whereas (AB)T is undefined. 1 3.2, 3.3 Inverting Matrices P. Danziger Theorem 2 (Properties of Transpose) Given ma- trices A and B so that the operations can be pre- formed 1. (AT )T = A 2. (A + B)T = AT + BT and (A B)T = AT BT − − 3. (kA)T = kAT 4. (AB)T = BT AT 2 3.2, 3.3 Inverting Matrices P. Danziger Matrix Algebra Theorem 3 (Algebraic Properties of Matrix Multiplication) 1. (k + `)A = kA + `A (Distributivity of scalar multiplication I) 2. k(A + B) = kA + kB (Distributivity of scalar multiplication II) 3. A(B + C) = AB + AC (Distributivity of matrix multiplication) 4. A(BC) = (AB)C (Associativity of matrix mul- tiplication) 5. A + B = B + A (Commutativity of matrix ad- dition) 6. (A + B) + C = A + (B + C) (Associativity of matrix addition) 7. k(AB) = A(kB) (Commutativity of Scalar Mul- tiplication) 3 3.2, 3.3 Inverting Matrices P. Danziger The matrix 0 is the identity of matrix addition. -
Clifford Algebra with Mathematica
Clifford Algebra with Mathematica J.L. ARAGON´ G. ARAGON-CAMARASA Universidad Nacional Aut´onoma de M´exico University of Glasgow Centro de F´ısica Aplicada School of Computing Science y Tecnolog´ıa Avanzada Sir Alwyn William Building, Apartado Postal 1-1010, 76000 Quer´etaro Glasgow, G12 8QQ Scotland MEXICO UNITED KINGDOM [email protected] [email protected] G. ARAGON-GONZ´ ALEZ´ M.A. RODRIGUEZ-ANDRADE´ Universidad Aut´onoma Metropolitana Instituto Polit´ecnico Nacional Unidad Azcapotzalco Departamento de Matem´aticas, ESFM San Pablo 180, Colonia Reynosa-Tamaulipas, UP Adolfo L´opez Mateos, 02200 D.F. M´exico Edificio 9. 07300 D.F. M´exico MEXICO MEXICO [email protected] [email protected] Abstract: The Clifford algebra of a n-dimensional Euclidean vector space provides a general language comprising vectors, complex numbers, quaternions, Grassman algebra, Pauli and Dirac matrices. In this work, we present an introduction to the main ideas of Clifford algebra, with the main goal to develop a package for Clifford algebra calculations for the computer algebra program Mathematica.∗ The Clifford algebra package is thus a powerful tool since it allows the manipulation of all Clifford mathematical objects. The package also provides a visualization tool for elements of Clifford Algebra in the 3-dimensional space. clifford.m is available from https://github.com/jlaragonvera/Geometric-Algebra Key–Words: Clifford Algebras, Geometric Algebra, Mathematica Software. 1 Introduction Mathematica, resulting in a package for doing Clif- ford algebra computations. There exists some other The importance of Clifford algebra was recognized packages and specialized programs for doing Clif- for the first time in quantum field theory. -
Eigenvalues and Eigenvectors
Section 5.1 Eigenvalues and Eigenvectors At this point in the class, we understand that the operation of matrix multiplication is very different from scalar multiplication. Matrix multiplication is an operation that finds the product of a pair of matrices, whereas scalar multiplication finds the product of a scalar with a matrix. More to the point, scalar multiplication of a scalar k with a matrix X is an easy operation to perform; indeed there's a simple algorithm: multiply each entry of X by the scalar k. Matrix multiplication on the other hand cannot be expressed using a simple algorithm. Indeed, the operation is extremely complex: to multiply a pair of n×n matrices, it takes about n2 operations, a hugely inefficient process. Thus it is surprising that, for any square matrix A, certain matrix multiplications \collapse" (in a sense) into scalar multiplication. As an example, consider the matrices ( ) ( ) ( ) 3 2 −4 10 A = ; w = ; x = : 3 4 −1 15 The product Aw is not particularly interesting: ( ) −14 Aw = ; −16 indeed, there appears to be very little connection between A, w, and Aw. On the other hand, an we see an interesting phenomenon when we calculate Ax: ( ) ( ) 60 10 Ax = = 6 = 6x: 90 15 Forgetting the calculation in the middle, we have Ax = 6x; in a sense, the matrix multiplication Ax collapsed into scalar multiplication 6x. We will see below that the phenomenon described here is more common than one might think. Identifying products like Ax = 6x will allow us to extract a great deal of useful information about A; accordingly, the rest of the section will be devoted to this phenomenon, which we give a name in the next definition.