On the Rank of a Tropical Matrix
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Introduction to Linear Bialgebra
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of New Mexico University of New Mexico UNM Digital Repository Mathematics and Statistics Faculty and Staff Publications Academic Department Resources 2005 INTRODUCTION TO LINEAR BIALGEBRA Florentin Smarandache University of New Mexico, [email protected] W.B. Vasantha Kandasamy K. Ilanthenral Follow this and additional works at: https://digitalrepository.unm.edu/math_fsp Part of the Algebra Commons, Analysis Commons, Discrete Mathematics and Combinatorics Commons, and the Other Mathematics Commons Recommended Citation Smarandache, Florentin; W.B. Vasantha Kandasamy; and K. Ilanthenral. "INTRODUCTION TO LINEAR BIALGEBRA." (2005). https://digitalrepository.unm.edu/math_fsp/232 This Book is brought to you for free and open access by the Academic Department Resources at UNM Digital Repository. It has been accepted for inclusion in Mathematics and Statistics Faculty and Staff Publications by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected], [email protected], [email protected]. INTRODUCTION TO LINEAR BIALGEBRA W. B. Vasantha Kandasamy Department of Mathematics Indian Institute of Technology, Madras Chennai – 600036, India e-mail: [email protected] web: http://mat.iitm.ac.in/~wbv Florentin Smarandache Department of Mathematics University of New Mexico Gallup, NM 87301, USA e-mail: [email protected] K. Ilanthenral Editor, Maths Tiger, Quarterly Journal Flat No.11, Mayura Park, 16, Kazhikundram Main Road, Tharamani, Chennai – 600 113, India e-mail: [email protected] HEXIS Phoenix, Arizona 2005 1 This book can be ordered in a paper bound reprint from: Books on Demand ProQuest Information & Learning (University of Microfilm International) 300 N. -
Determinants Linear Algebra MATH 2010
Determinants Linear Algebra MATH 2010 • Determinants can be used for a lot of different applications. We will look at a few of these later. First of all, however, let's talk about how to compute a determinant. • Notation: The determinant of a square matrix A is denoted by jAj or det(A). • Determinant of a 2x2 matrix: Let A be a 2x2 matrix a b A = c d Then the determinant of A is given by jAj = ad − bc Recall, that this is the same formula used in calculating the inverse of A: 1 d −b 1 d −b A−1 = = ad − bc −c a jAj −c a if and only if ad − bc = jAj 6= 0. We will later see that if the determinant of any square matrix A 6= 0, then A is invertible or nonsingular. • Terminology: For larger matrices, we need to use cofactor expansion to find the determinant of A. First of all, let's define a few terms: { Minor: A minor, Mij, of the element aij is the determinant of the matrix obtained by deleting the ith row and jth column. ∗ Example: Let 2 −3 4 2 3 A = 4 6 3 1 5 4 −7 −8 Then to find M11, look at element a11 = −3. Delete the entire column and row that corre- sponds to a11 = −3, see the image below. Then M11 is the determinant of the remaining matrix, i.e., 3 1 M11 = = −8(3) − (−7)(1) = −17: −7 −8 ∗ Example: Similarly, to find M22 can be found looking at the element a22 and deleting the same row and column where this element is found, i.e., deleting the second row, second column: Then −3 2 M22 = = −8(−3) − 4(−2) = 16: 4 −8 { Cofactor: The cofactor, Cij is given by i+j Cij = (−1) Mij: Basically, the cofactor is either Mij or −Mij where the sign depends on the location of the element in the matrix. -
8 Rank of a Matrix
8 Rank of a matrix We already know how to figure out that the collection (v1;:::; vk) is linearly dependent or not if each n vj 2 R . Recall that we need to form the matrix [v1 j ::: j vk] with the given vectors as columns and see whether the row echelon form of this matrix has any free variables. If these vectors linearly independent (this is an abuse of language, the correct phrase would be \if the collection composed of these vectors is linearly independent"), then, due to the theorems we proved, their span is a subspace of Rn of dimension k, and this collection is a basis of this subspace. Now, what if they are linearly dependent? Still, their span will be a subspace of Rn, but what is the dimension and what is a basis? Naively, I can answer this question by looking at these vectors one by one. In particular, if v1 =6 0 then I form B = (v1) (I know that one nonzero vector is linearly independent). Next, I add v2 to this collection. If the collection (v1; v2) is linearly dependent (which I know how to check), I drop v2 and take v3. If independent then I form B = (v1; v2) and add now third vector v3. This procedure will lead to the vectors that form a basis of the span of my original collection, and their number will be the dimension. Can I do it in a different, more efficient way? The answer if \yes." As a side result we'll get one of the most important facts of the basic linear algebra. -
Computations in Algebraic Geometry with Macaulay 2
Computations in algebraic geometry with Macaulay 2 Editors: D. Eisenbud, D. Grayson, M. Stillman, and B. Sturmfels Preface Systems of polynomial equations arise throughout mathematics, science, and engineering. Algebraic geometry provides powerful theoretical techniques for studying the qualitative and quantitative features of their solution sets. Re- cently developed algorithms have made theoretical aspects of the subject accessible to a broad range of mathematicians and scientists. The algorith- mic approach to the subject has two principal aims: developing new tools for research within mathematics, and providing new tools for modeling and solv- ing problems that arise in the sciences and engineering. A healthy synergy emerges, as new theorems yield new algorithms and emerging applications lead to new theoretical questions. This book presents algorithmic tools for algebraic geometry and experi- mental applications of them. It also introduces a software system in which the tools have been implemented and with which the experiments can be carried out. Macaulay 2 is a computer algebra system devoted to supporting research in algebraic geometry, commutative algebra, and their applications. The reader of this book will encounter Macaulay 2 in the context of concrete applications and practical computations in algebraic geometry. The expositions of the algorithmic tools presented here are designed to serve as a useful guide for those wishing to bring such tools to bear on their own problems. A wide range of mathematical scientists should find these expositions valuable. This includes both the users of other programs similar to Macaulay 2 (for example, Singular and CoCoA) and those who are not interested in explicit machine computations at all. -
Prizes and Awards Session
PRIZES AND AWARDS SESSION Wednesday, July 12, 2021 9:00 AM EDT 2021 SIAM Annual Meeting July 19 – 23, 2021 Held in Virtual Format 1 Table of Contents AWM-SIAM Sonia Kovalevsky Lecture ................................................................................................... 3 George B. Dantzig Prize ............................................................................................................................. 5 George Pólya Prize for Mathematical Exposition .................................................................................... 7 George Pólya Prize in Applied Combinatorics ......................................................................................... 8 I.E. Block Community Lecture .................................................................................................................. 9 John von Neumann Prize ......................................................................................................................... 11 Lagrange Prize in Continuous Optimization .......................................................................................... 13 Ralph E. Kleinman Prize .......................................................................................................................... 15 SIAM Prize for Distinguished Service to the Profession ....................................................................... 17 SIAM Student Paper Prizes .................................................................................................................... -
Arxiv:2010.06953V2 [Math.RA] 23 Apr 2021
IDENTITIES AND BASES IN THE HYPOPLACTIC MONOID ALAN J. CAIN, ANTÓNIO MALHEIRO, AND DUARTE RIBEIRO Abstract. This paper presents new results on the identities satisfied by the hypoplactic monoid. We show how to embed the hypoplactic monoid of any rank strictly greater than 2 (including infinite rank) into a direct product of copies of the hypoplactic monoid of rank 2. This confirms that all hypoplactic monoids of rank greater than or equal to 2 satisfy exactly the same identities. We then give a complete characterization of those identities, and prove that the variety generated by the hypoplactic monoid has finite axiomatic rank, by giving a finite basis for it. 1. Introduction A (non-trivial) identity is a formal equality u ≈ v, where u and v are words over some alphabet of variables, which is not of the form u ≈ u. If a monoid is known to satisfy an identity, an important question is whether the set of identities it satisfies is finitely based, that is, if all these identities are consequences of those in some finite subset (see [37, 42]). The plactic monoid plac, also known as the monoid of Young tableaux, is an important algebraic structure, first studied by Schensted [38] and Knuth [23], and later studied in depth by Lascoux and Schützenberger [27]. It is connected to many different areas of Mathematics, such as algebraic combinatorics, symmetric functions [29], crystal bases [4] and representation theory [14]. In particular, the question of identities in the plactic monoid has received a lot of attention recently [26, 20]. Finitely-generated polynomial-growth groups are virtually nilpotent and so sat- isfy identities [16]. -
Vita BERND STURMFELS Department of Mathematics, University of California, Berkeley, CA 94720 Phone
Vita BERND STURMFELS Department of Mathematics, University of California, Berkeley, CA 94720 Phone: (510) 642 4687, Fax: (510) 642 8204, [email protected] M.A. [Diplom] TH Darmstadt, Germany, Mathematics and Computer Science, 1985 Ph.D. [Dr. rer. nat.] TH Darmstadt, Germany, Mathematics, 1987 Ph.D. University of Washington, Seattle, Mathematics, 1987 Professional Experience: 1987–1988 Postdoctoral Fellow, I.M.A., University of Minnesota, Minneapolis 1988–1989 Assistant Professor, Research Institute for Symbolic Computation, (RISC-Linz), Linz, Austria 1989–1991 Assistant Professor, Department of Mathematics, Cornell University 1992–1996 Associate Professor, Department of Mathematics, Cornell University 1994–2001 Professor, Department of Mathematics, University of California, Berkeley 2001– Professor, Department of Mathematics and Computer Science, UC Berkeley Academic Honors: 1986 - 1987 Alfred P. Sloan Doctoral Dissertation Fellowship 1991 - 1993 Alfred P. Sloan Research Fellow 1992 - 1997 National Young Investigator (NSF) 1992 - 1997 David and Lucile Packard Fellowship 1999 Lester R. Ford Prize for Expository Writing (MAA) 2000-2001 Miller Research Professorship, UC Berkeley Spring 2003 John von Neumann Professor, Technical University M¨unchen 2003-2004 Hewlett-Packard Research Professor at MSRI Berkeley July 2004 Clay Mathematics Institute Senior Scholar Research Interests: Computational Algebra, Combinatorics, Algebraic Geometry Selected Professional Activities: Visiting Positions: D´epartment de Math´ematiques, Universit´ede Nice, France, Spring 1989 Mathematical Sciences Research Institute, Berkeley, Fall 1992 Courant Institute, New York University, 1994–95 RIMS, Kyoto University, Japan, 1997–98 Current Editorial Board Membership: Journal of the American Mathematical Society, Duke Mathematical Journal, Collecteana Mathematica, Beitr¨agezur Geometrie und Algebra, Order, Discrete and Computational Geometry, Applicable Algebra (AAECC) Journal of Combinatorial Theory (Ser. -
April 2017 Table of Contents
ISSN 0002-9920 (print) ISSN 1088-9477 (online) of the American Mathematical Society April 2017 Volume 64, Number 4 AMS Prize Announcements page 311 Spring Sectional Sampler page 333 AWM Research Symposium 2017 Lecture Sampler page 341 Mathematics and Statistics Awareness Month page 362 About the Cover: How Minimal Surfaces Converge to a Foliation (see page 307) MATHEMATICAL CONGRESS OF THE AMERICAS MCA 2017 JULY 2428, 2017 | MONTREAL CANADA MCA2017 will take place in the beautiful city of Montreal on July 24–28, 2017. The many exciting activities planned include 25 invited lectures by very distinguished mathematicians from across the Americas, 72 special sessions covering a broad spectrum of mathematics, public lectures by Étienne Ghys and Erik Demaine, a concert by the Cecilia String Quartet, presentation of the MCA Prizes and much more. SPONSORS AND PARTNERS INCLUDE Canadian Mathematical Society American Mathematical Society Pacifi c Institute for the Mathematical Sciences Society for Industrial and Applied Mathematics The Fields Institute for Research in Mathematical Sciences National Science Foundation Centre de Recherches Mathématiques Conacyt, Mexico Atlantic Association for Research in Mathematical Sciences Instituto de Matemática Pura e Aplicada Tourisme Montréal Sociedade Brasileira de Matemática FRQNT Quebec Unión Matemática Argentina Centro de Modelamiento Matemático For detailed information please see the web site at www.mca2017.org. AMERICAN MATHEMATICAL SOCIETY PUSHING LIMITS From West Point to Berkeley & Beyond PUSHING LIMITS FROM WEST POINT TO BERKELEY & BEYOND Ted Hill, Georgia Tech, Atlanta, GA, and Cal Poly, San Luis Obispo, CA Recounting the unique odyssey of a noted mathematician who overcame military hurdles at West Point, Army Ranger School, and the Vietnam War, this is the tale of an academic career as noteworthy for its o beat adventures as for its teaching and research accomplishments. -
Block Matrices in Linear Algebra
PRIMUS Problems, Resources, and Issues in Mathematics Undergraduate Studies ISSN: 1051-1970 (Print) 1935-4053 (Online) Journal homepage: https://www.tandfonline.com/loi/upri20 Block Matrices in Linear Algebra Stephan Ramon Garcia & Roger A. Horn To cite this article: Stephan Ramon Garcia & Roger A. Horn (2020) Block Matrices in Linear Algebra, PRIMUS, 30:3, 285-306, DOI: 10.1080/10511970.2019.1567214 To link to this article: https://doi.org/10.1080/10511970.2019.1567214 Accepted author version posted online: 05 Feb 2019. Published online: 13 May 2019. Submit your article to this journal Article views: 86 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=upri20 PRIMUS, 30(3): 285–306, 2020 Copyright # Taylor & Francis Group, LLC ISSN: 1051-1970 print / 1935-4053 online DOI: 10.1080/10511970.2019.1567214 Block Matrices in Linear Algebra Stephan Ramon Garcia and Roger A. Horn Abstract: Linear algebra is best done with block matrices. As evidence in sup- port of this thesis, we present numerous examples suitable for classroom presentation. Keywords: Matrix, matrix multiplication, block matrix, Kronecker product, rank, eigenvalues 1. INTRODUCTION This paper is addressed to instructors of a first course in linear algebra, who need not be specialists in the field. We aim to convince the reader that linear algebra is best done with block matrices. In particular, flexible thinking about the process of matrix multiplication can reveal concise proofs of important theorems and expose new results. Viewing linear algebra from a block-matrix perspective gives an instructor access to use- ful techniques, exercises, and examples. -
Curriculum Vitae
Paul Breiding | Curriculum Vitae Max-Planck-Institute for Mathematics in the Sciences, Inselstr. 22, 04103 Leipzig, Germany Q [email protected] • paulbreiding.org • PBrdng u Paul Breiding • 7 @_pbrdng • born 12th of May 1988, german citizenship Max-Plack-Institute for Mathematics in the Sciences Leipzig Head of Emmy Noether Research Group: Numerical and Probabilistic Nonlinear Algebra Since 04/2021 University of Kassel Substitute Professor for Computeralgebra 11/2020 – 03/2021 Akademie der Wissenschaften und der Literatur Mainz Member of the Junge Akademie 04/2020 – 03/2024 Parental leave 7 months in total 10/2019 – 11/2019 and 04/2020 – 10/2020 Technische Universität Berlin Postdoctoral researcher in the algorithmic algebra research group 04/2019 – 10/2020 Max-Plack-Institute for Mathematics in the Sciences Leipzig Postdoctoral researcher in the nonlinear algebra research group 10/2017 – 03/2019 Technische Universität Berlin PhD student with Prof. Dr. Bürgisser 12/2013 – 09/2017 Date of thesis defense: July 25, 2017. Evaluation ’summa cum laude’. Simons Institute for the Theory of Computing Visiting graduate student 08/2014 – 10/2014 Algorithms and Complexity in Algebraic Geometry Education Georg-August-Universität Göttingen Master of Science 10/2011 – 11/2013 Evaluation: excellent. Universidad de Sevilla Undergraduate studies, part of the Erasmus exchange program 02/2011 – 09/2011 Georg-August Universität Göttingen Bachelor of Science 10/2008 – 09/2011 Languages........................................................................................................................ -
Lectures on Algebraic Statistics
Lectures on Algebraic Statistics Mathias Drton, Bernd Sturmfels, Seth Sullivant September 27, 2008 2 Contents 1 Markov Bases 7 1.1 Hypothesis Tests for Contingency Tables . 7 1.2 Markov Bases of Hierarchical Models . 17 1.3 The Many Bases of an Integer Lattice . 26 2 Likelihood Inference 35 2.1 Discrete and Gaussian Models . 35 2.2 Likelihood Equations for Implicit Models . 46 2.3 Likelihood Ratio Tests . 54 3 Conditional Independence 67 3.1 Conditional Independence Models . 67 3.2 Graphical Models . 75 3.3 Parametrizations of Graphical Models . 85 4 Hidden Variables 95 4.1 Secant Varieties in Statistics . 95 4.2 Factor Analysis . 105 5 Bayesian Integrals 113 5.1 Information Criteria and Asymptotics . 113 5.2 Exact Integration for Discrete Models . 122 6 Exercises 131 6.1 Markov Bases Fixing Subtable Sums . 131 6.2 Quasi-symmetry and Cycles . 136 6.3 A Colored Gaussian Graphical Model . 139 6.4 Instrumental Variables and Tangent Cones . 143 6.5 Fisher Information for Multivariate Normals . 150 6.6 The Intersection Axiom and Its Failure . 152 6.7 Primary Decomposition for CI Inference . 155 6.8 An Independence Model and Its Mixture . 158 7 Open Problems 165 4 Contents Preface Algebraic statistics is concerned with the development of techniques in algebraic geometry, commutative algebra, and combinatorics, to address problems in statis- tics and its applications. On the one hand, algebra provides a powerful tool set for addressing statistical problems. On the other hand, it is rarely the case that algebraic techniques are ready-made to address statistical challenges, and usually new algebraic results need to be developed. -
The Geometry of Algorithms with Orthogonality Constraints∗
SIAM J. MATRIX ANAL. APPL. c 1998 Society for Industrial and Applied Mathematics Vol. 20, No. 2, pp. 303–353 " THE GEOMETRY OF ALGORITHMS WITH ORTHOGONALITY CONSTRAINTS∗ ALAN EDELMAN† , TOMAS´ A. ARIAS‡ , AND STEVEN T. SMITH§ Abstract. In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal processing. In addition to the new algorithms, we show how the geometrical framework gives penetrating new insights allowing us to create, understand, and compare algorithms. The theory proposed here provides a taxonomy for numerical linear algebra algorithms that provide a top level mathematical view of previously unrelated algorithms. It is our hope that developers of new algorithms and perturbation theories will benefit from the theory, methods, and examples in this paper. Key words. conjugate gradient, Newton’s method, orthogonality constraints, Grassmann man- ifold, Stiefel manifold, eigenvalues and eigenvectors, invariant subspace, Rayleigh quotient iteration, eigenvalue optimization, sequential quadratic programming, reduced gradient method, electronic structures computation, subspace tracking AMS subject classifications. 49M07, 49M15, 53B20, 65F15, 15A18, 51F20, 81V55 PII. S0895479895290954 1. Introduction. Problems on the Stiefel and Grassmann manifolds arise with sufficient frequency that a unifying investigation of algorithms designed to solve these problems is warranted. Understanding these manifolds, which represent orthogonality constraints (as in the symmetric eigenvalue problem), yields penetrating insight into many numerical algorithms and unifies seemingly unrelated ideas from different areas. The optimization community has long recognized that linear and quadratic con- straints have special structure that can be exploited.