Bounds on eigenvalues of complex interval matrices
|
|
- Erik Benson
- 5 years ago
- Views:
Transcription
1 Bounds on eigenvalues of complex interval matrices Milan Hladík Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic SWIM 20, Bourges, France June 4 5 M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 / 8
2 Introduction Interval matrix A := [A,A] = {A R n n A A A}, The corresponding center and radius matrices A c := (A + A), 2 A := (A A). 2 M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 2 / 8
3 Introduction Interval matrix A := [A,A] = {A R n n A A A}, The corresponding center and radius matrices A c := (A + A), 2 A := (A A). 2 Complex interval matrix A + ib. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 2 / 8
4 Introduction Interval matrix A := [A,A] = {A R n n A A A}, The corresponding center and radius matrices A c := (A + A), 2 A := (A A). 2 Complex interval matrix A + ib. Eigenvalue set Λ(A + ib) = {λ + iµ A A B B x + iy 0 : (A + ib)(x + iy) = (λ + iµ)(x + iy)}. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 2 / 8
5 Introduction Selected publications Deif, 99: exact bounds under strong assumptions Mayer, 994: enclosure for the complex case based on Taylor expansion Kolev and Petrakieva, 2005: enclosure for real parts by solving nonlinear system of equations Rohn, 998: a cheap formula for an enclosure Hertz, 2009: extension of Rohn s formula to the complex case M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 3 / 8
6 Introduction Selected publications Deif, 99: exact bounds under strong assumptions Mayer, 994: enclosure for the complex case based on Taylor expansion Kolev and Petrakieva, 2005: enclosure for real parts by solving nonlinear system of equations Rohn, 998: a cheap formula for an enclosure Hertz, 2009: extension of Rohn s formula to the complex case Other directions approximations Hurwitz / Schur stability checking M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 3 / 8
7 Introduction Notation ρ( ) spectral radius λ ( ) λ n ( ) eigenvalues of a symmetric matrix Theorem (Rohn, 998) For each eigenvalue λ + iµ Λ(A) we have ( ) ( ) λ λ 2 (A c + A T c ) + ρ 2 (A + A T ), ( ) ( ) λ λ n 2 (A c + A T c ) ρ 2 (A + A T ), ( 0 2 µ λ (A c A T c ) ) ( ρ (A + A T ) ) 2 (AT c A c ) 0 2 (AT + A, ) 0 ( 0 2 µ λ (A c A T c ) ) ( 0 2 n ρ (A + A T ) ) 2 (AT c A c ) 0 2 (AT + A. ) 0 M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 4 / 8
8 Introduction Theorem (Hertz, 2009) For each eigenvalue λ + iµ Λ(A + ib) we have ««λ λ 2 (Ac + AT c ) + ρ 2 (A + A T ) 0 + λ 2 (BT c Bc) «0 2 (Bc BT c ) 0 + ρ 2 (BT + B «) 2 (B + B T) 0, ««λ λ n 2 (Ac + AT c ) ρ 2 (A + A T ) «0 + λ n 2 (BT c B c) 0 2 (Bc ρ 2 (BT + B «) BT c ) 0 2 (B + B T ) 0, ««µ λ 2 (Bc + BT c ) + ρ 2 (B + B T ) 0 + λ 2 (Ac AT c ) «0 2 (AT c Ac) 0 + ρ 2 (A + A T ) «2 (AT + A, ) 0 ««µ λ n 2 (Bc + BT c ) ρ 2 (B + B T ) 0 + λ n 2 (Ac «AT c ) 0 ρ 2 (A + A T ) «2 (AT c A c) 0 2 (AT + A. ) 0 M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 5 / 8
9 Symmetric case Our approach Reduction to the symmetric case. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 6 / 8
10 Symmetric case Our approach Reduction to the symmetric case. Symmetric interval matrix M S = {M M M = M T }. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 6 / 8
11 Symmetric case Our approach Reduction to the symmetric case. Symmetric interval matrix M S = {M M M = M T }. Eigenvalue sets Let λ (M) λ 2 (M) λ n (M). be eigenvalues of a symmetric M R n n. Then λ i (M S ) = [λ i (M S ),λ i (M S )] := { λ i (M) M M S}, i =,...,n. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 6 / 8
12 Enclosures for the symmetric case Theorem (Rohn, 2005) λ i (A S ) [λ i (A c ) ρ(a ),λ i (A c ) + ρ(a )], i =,...,n. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 7 / 8
13 Enclosures for the symmetric case Theorem (Rohn, 2005) λ i (A S ) [λ i (A c ) ρ(a ),λ i (A c ) + ρ(a )], i =,...,n. Theorem (Hertz, 992) Define Z := {} {±} n = {(, ±,..., ±)} and for a z Z define A z,a z AS in this way: { { a ij if z i = z j, (a z ) ij = a ij if z i z j,, (a z ) a ij = ij if z i = z j, a ij if z i z j. Then λ (A S ) = max z Z λ (A z ), λ n (A S ) = min z Z λ n(a z ). M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 7 / 8
14 Enclosures for the symmetric case Theorem (Rohn, 2005) λ i (A S ) [λ i (A c ) ρ(a ),λ i (A c ) + ρ(a )], i =,...,n. Theorem (Hertz, 992) Define Z := {} {±} n = {(, ±,..., ±)} and for a z Z define A z,a z AS in this way: { { a ij if z i = z j, (a z ) ij = a ij if z i z j,, (a z ) a ij = ij if z i = z j, a ij if z i z j. Then λ (A S ) = max z Z λ (A z ), λ n (A S ) = min z Z λ n(a z ). Others Hladík, Daney and Tsigaridas, 200, 20: filtering,... M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 7 / 8
15 Main result Theorem For each eigenvalue λ + iµ Λ(A + ib) we have ( λ 2 (A + ) AT S ( ) 2 (BT B) n 2 (B BT ) 2 (A + λ λ 2 (A + ) AT S ) 2 (BT B) AT ) 2 (B BT ) 2 (A +, AT ) ( λ 2 (B + BT ) 2 (A ) S ( AT ) n 2 (AT A) 2 (B + µ λ 2 (B + BT ) 2 (A ) S AT ) BT ) 2 (AT A) 2 (B +. BT ) M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 8 / 8
16 Main result Corollary For each λ + iµ Λ(A + ib) we have 2 λ λ (Ac +! AT c ) 2 (BT c B c) 2 (Bc BT c ) 2 (Ac + + ρ AT c ) 2 λ λ (Ac +! AT c ) 2 (BT c B c) n 2 (Bc BT c ) 2 (Ac + ρ AT c ) 2 µ λ (Bc + BT c ) 2 (Ac! AT c ) 2 (AT c A c) 2 (Bc + + ρ BT c ) 2 µ λ (Bc + BT c ) 2 (Ac! AT c ) n 2 (AT c A c) 2 (Bc + ρ BT c ) (A 2 + A T ) (B 2 + B ) T (A 2 + A T ) (B 2 + B ) T (B 2 + B ) T 2 (AT + A ) (B 2 + B ) T 2 (AT + A )! 2 (BT + B ) (A 2 + A T, )! 2 (BT + B ) (A 2 + A T, ) (A! 2 + A T ) (B, 2 + B ) T (A! 2 + A T ) (B 2 + B ) T. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 9 / 8
17 Main result Corollary For each λ + iµ Λ(A + ib) we have 2 λ λ (Ac +! AT c ) 2 (BT c B c) 2 (Bc BT c ) 2 (Ac + + ρ AT c ) 2 λ λ (Ac +! AT c ) 2 (BT c B c) n 2 (Bc BT c ) 2 (Ac + ρ AT c ) 2 µ λ (Bc + BT c ) 2 (Ac! AT c ) 2 (AT c A c) 2 (Bc + + ρ BT c ) 2 µ λ (Bc + BT c ) 2 (Ac! AT c ) n 2 (AT c A c) 2 (Bc + ρ BT c ) (A 2 + A T ) (B 2 + B ) T (A 2 + A T ) (B 2 + B ) T (B 2 + B ) T 2 (AT + A ) (B 2 + B ) T 2 (AT + A )! 2 (BT + B ) (A 2 + A T, )! 2 (BT + B ) (A 2 + A T, ) (A! 2 + A T ) (B, 2 + B ) T (A! 2 + A T ) (B 2 + B ) T. Others using simple bounds for the symmetric case, but: for B = 0 we have the same bounds as Rohn, 998, in general, the bouds are as good as Hertz, 2009 M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 9 / 8
18 Examples Example (Seyranian, Kirillov, and Mailybaev, 2005) Let s i s 2 i s 3 B(s) = i i s + i s 2 0 s 3, i s 3 s 3 0 where s s = ([0,0.2],[0.9797,],0). Hertz enclosure: λ [ , 8.534], µ [ 7.43,.8843]. Our approach simple bounds: λ [ , 7.646], µ [ , ]. by filtering: λ [ , 6.742], µ [ 5.407, ]. best bounds: λ [ 4.580, 6.303], µ [ , ]. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 0 / 8
19 Examples Example (con t) Monter Carlo simulation: M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 / 8
20 Examples Example (Petkovski, 99) Let 0 A = 2 [.399, 0.00] Hertz enclosure: λ [.9068, ], µ [ 2.59, 2.59]. Our approach simple bounds: λ [.9068, ], µ [ 2.59, 2.59]. by filtering: λ [.6474, ], µ [ 2.934, 2.934]. best bounds: λ [.6474, ], µ [ 2.2, 2.2]. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 2 / 8
21 Examples Example (con t) Monter Carlo simulation: M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 3 / 8
22 Examples Example (Xiao and Unbehauen, 2000) Let 0 [, ] A = 0 [, ] [, ] [, ] 0. Hertz enclosure: λ [ 2.443,.543], µ [.443,.443]. Our approach simple bounds: λ [ 2.443,.543], µ [.443,.443]. by filtering: λ [ ,.532], µ [.443,.443]. best bounds: λ [.9674,.0674], µ [.443,.443]. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 4 / 8
23 Examples Example (con t) Monter Carlo simulation: M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 5 / 8
24 Examples Example (Wang, Michel and Liu, 994) Let [ 3, 2] [4, 5] [4, 6] [,.5] A = [ 4, 3] [ 4, 3] [ 4, 3] [, 2] [ 5, 4] [2, 3] [ 5, 4] [, 0] [, 0.] [0, ] [, 2] [ 4, 2.5] Hertz enclosure: λ [ 8.822, ], µ [ , ]. Our approach simple bounds: λ [ 8.822, ], µ [ , ]. by filtering: λ [ , 3.384], µ [ , ]. best bounds: λ [ 7.369, ], µ [ , ]. M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 6 / 8
25 Examples Example (con t) Monter Carlo simulation: M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 7 / 8
26 Conclusion Conclusion reduction of the problem to real symmetric one cheap and tight enclosure outperforms Rohn (998) and Hertz (2009) formulae M. Hladík (CUNI) Eigenvalues of complex interval matrices June 4 5, 20 8 / 8
Global Optimization by Interval Analysis
Global Optimization by Interval Analysis Milan Hladík Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic, http://kam.mff.cuni.cz/~hladik/
More informationarxiv: v1 [cs.na] 7 Jul 2017
AE regularity of interval matrices Milan Hladík October 4, 2018 arxiv:1707.02102v1 [cs.na] 7 Jul 2017 Abstract Consider a linear system of equations with interval coefficients, and each interval coefficient
More informationInterval linear algebra
Charles University in Prague Faculty of Mathematics and Physics HABILITATION THESIS Milan Hladík Interval linear algebra Department of Applied Mathematics Prague 204 Contents Preface 3 Interval linear
More informationOptimal Preconditioning for the Interval Parametric Gauss Seidel Method
Optimal Preconditioning for the Interval Parametric Gauss Seidel Method Milan Hladík Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic http://kam.mff.cuni.cz/~hladik/ SCAN,
More informationSolving Global Optimization Problems by Interval Computation
Solving Global Optimization Problems by Interval Computation Milan Hladík Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic, http://kam.mff.cuni.cz/~hladik/
More informationarxiv: v1 [math.na] 28 Jun 2013
A New Operator and Method for Solving Interval Linear Equations Milan Hladík July 1, 2013 arxiv:13066739v1 [mathna] 28 Jun 2013 Abstract We deal with interval linear systems of equations We present a new
More informationTotal Least Squares and Chebyshev Norm
Total Least Squares and Chebyshev Norm Milan Hladík 1 & Michal Černý 2 1 Department of Applied Mathematics Faculty of Mathematics & Physics Charles University in Prague, Czech Republic 2 Department of
More informationPositive Semidefiniteness and Positive Definiteness of a Linear Parametric Interval Matrix
Positive Semidefiniteness and Positive Definiteness of a Linear Parametric Interval Matrix Milan Hladík Charles University, Faculty of Mathematics and Physics, Department of Applied Mathematics, Malostranské
More informationA NOTE ON CHECKING REGULARITY OF INTERVAL MATRICES
A NOTE ON CHECKING REGULARITY OF INTERVAL MATRICES G. Rex and J. Rohn Abstract It is proved that two previously known sufficient conditions for regularity of interval matrices are equivalent in the sense
More informationCheap and Tight Bounds: The Recent Result by E. Hansen Can Be Made More Efficient
Interval Computations No 4, 1993 Cheap and Tight Bounds: The Recent Result by E. Hansen Can Be Made More Efficient Jiří Rohn Improving the recent result by Eldon Hansen, we give cheap and tight bounds
More informationChecking Properties of Interval Matrices 1
Checking Properties of Interval Matrices 1 Jiří Rohn 2 1 The text, or a part of it, will appear in the book by Kreinovich, Lakeyev, Rohn and Kahl [24]. This work was supported by the Charles University
More informationA filtering method for the interval eigenvalue problem
A filtering method for the interval eigenvalue problem Milan Hladik, David Daney, Elias P. Tsigaridas To cite this version: Milan Hladik, David Daney, Elias P. Tsigaridas. A filtering method for the interval
More informationBounds on the radius of nonsingularity
Towards bounds on the radius of nonsingularity, Milan Hladík Department of Applied Mathematics, Charles University, Prague and Institute of Computer Science of the Academy of Czech Republic SCAN 2014,
More informationarxiv: v1 [math.na] 11 Sep 2018
DETERMINANTS OF INTERVAL MATRICES JAROSLAV HORÁČEK, MILAN HLADÍK, AND JOSEF MATĚJKA arxiv:1809.03736v1 [math.na] 11 Sep 2018 Abstract. In this paper we shed more light on determinants of interval matrices.
More informationAN IMPROVED THREE-STEP METHOD FOR SOLVING THE INTERVAL LINEAR PROGRAMMING PROBLEMS
Yugoslav Journal of Operations Research xx (xx), Number xx, xx DOI: https://doi.org/10.2298/yjor180117020a AN IMPROVED THREE-STEP METHOD FOR SOLVING THE INTERVAL LINEAR PROGRAMMING PROBLEMS Mehdi ALLAHDADI
More informationREGULARITY RADIUS: PROPERTIES, APPROXIMATION AND A NOT A PRIORI EXPONENTIAL ALGORITHM
Electronic Journal of Linear Algebra Volume 33 Volume 33: Special Issue for the International Conference on Matrix Analysis and its Applications, MAT TRIAD 207 Article 2 208 REGULARITY RADIUS: PROPERTIES,
More informationSeparation of convex polyhedral sets with uncertain data
Separation of convex polyhedral sets with uncertain data Milan Hladík Department of Applied Mathematics Charles University Malostranské nám. 25 118 00 Prague Czech Republic e-mail: milan.hladik@matfyz.cz
More informationInstitute of Computer Science. Compact Form of the Hansen-Bliek-Rohn Enclosure
Institute of Computer Science Academy of Sciences of the Czech Republic Compact Form of the Hansen-Bliek-Rohn Enclosure Jiří Rohn http://uivtx.cs.cas.cz/~rohn Technical report No. V-1157 24.03.2012 Pod
More informationBounds on eigenvalues and singular values of interval matrices
Bounds on eigenvalues and singular values of interval matrices Milan Hladik, David Daney, Elias P. Tsigaridas To cite this version: Milan Hladik, David Daney, Elias P. Tsigaridas. Bounds on eigenvalues
More informationNP-hardness results for linear algebraic problems with interval data
1 NP-hardness results for linear algebraic problems with interval data Dedicated to my father, Mr. Robert Rohn, in memoriam J. Rohn a a Faculty of Mathematics and Physics, Charles University, Malostranské
More informationLecture 10 - Eigenvalues problem
Lecture 10 - Eigenvalues problem Department of Computer Science University of Houston February 28, 2008 1 Lecture 10 - Eigenvalues problem Introduction Eigenvalue problems form an important class of problems
More informationInterval Robustness in Linear Programming
Interval Robustness in Linear Programming Milan Hladík Department of Applied Mathematics Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic http://kam.mff.cuni.cz/~hladik/
More informationRoberto Castelli Basque Center for Applied Mathematics
A rigorous computational method to enclose the eigendecomposition of interval matrices Roberto Castelli Basque Center for Applied Mathematics Joint work with: Jean-Philippe Lessard Basque / Hungarian Workshop
More informationRepresentation for the generalized Drazin inverse of block matrices in Banach algebras
Representation for the generalized Drazin inverse of block matrices in Banach algebras Dijana Mosić and Dragan S. Djordjević Abstract Several representations of the generalized Drazin inverse of a block
More informationLINEAR INTERVAL INEQUALITIES
LINEAR INTERVAL INEQUALITIES Jiří Rohn, Jana Kreslová Faculty of Mathematics and Physics, Charles University Malostranské nám. 25, 11800 Prague, Czech Republic 1.6.1993 Abstract We prove that a system
More informationComputing the Norm A,1 is NP-Hard
Computing the Norm A,1 is NP-Hard Dedicated to Professor Svatopluk Poljak, in memoriam Jiří Rohn Abstract It is proved that computing the subordinate matrix norm A,1 is NP-hard. Even more, existence of
More informationSection 3.9. Matrix Norm
3.9. Matrix Norm 1 Section 3.9. Matrix Norm Note. We define several matrix norms, some similar to vector norms and some reflecting how multiplication by a matrix affects the norm of a vector. We use matrix
More informationAn Eigenvalue Symmetric Matrix Contractor
An Eigenvalue Symmetric Matrix Contractor Milan Hladík Charles University, Faculty of Mathematics and Physics, Dept. of Applied Mathematics, Malostranské nám. 25, 11800, Prague, Czech Republic milan.hladik@matfyz.cz
More informationMath Introduction to Numerical Analysis - Class Notes. Fernando Guevara Vasquez. Version Date: January 17, 2012.
Math 5620 - Introduction to Numerical Analysis - Class Notes Fernando Guevara Vasquez Version 1990. Date: January 17, 2012. 3 Contents 1. Disclaimer 4 Chapter 1. Iterative methods for solving linear systems
More informationTight Enclosure of Matrix Multiplication with Level 3 BLAS
Tight Enclosure of Matrix Multiplication with Level 3 BLAS K. Ozaki (Shibaura Institute of Technology) joint work with T. Ogita (Tokyo Woman s Christian University) 8th Small Workshop on Interval Methods
More informationELA INTERVAL SYSTEM OF MATRIX EQUATIONS WITH TWO UNKNOWN MATRICES
INTERVAL SYSTEM OF MATRIX EQUATIONS WITH TWO UNKNOWN MATRICES A RIVAZ, M MOHSENI MOGHADAM, AND S ZANGOEI ZADEH Abstract In this paper, we consider an interval system of matrix equations contained two equations
More informationNonlinear Programming Algorithms Handout
Nonlinear Programming Algorithms Handout Michael C. Ferris Computer Sciences Department University of Wisconsin Madison, Wisconsin 5376 September 9 1 Eigenvalues The eigenvalues of a matrix A C n n are
More informationCalculus C (ordinary differential equations)
Calculus C (ordinary differential equations) Lesson 9: Matrix exponential of a symmetric matrix Coefficient matrices with a full set of eigenvectors Solving linear ODE s by power series Solutions to linear
More informationNonlinear equations. Norms for R n. Convergence orders for iterative methods
Nonlinear equations Norms for R n Assume that X is a vector space. A norm is a mapping X R with x such that for all x, y X, α R x = = x = αx = α x x + y x + y We define the following norms on the vector
More informationLecture 15 Review of Matrix Theory III. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore
Lecture 15 Review of Matrix Theory III Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Matrix An m n matrix is a rectangular or square array of
More informationMarkov Chains, Random Walks on Graphs, and the Laplacian
Markov Chains, Random Walks on Graphs, and the Laplacian CMPSCI 791BB: Advanced ML Sridhar Mahadevan Random Walks! There is significant interest in the problem of random walks! Markov chain analysis! Computer
More informationFIXED POINT ITERATIONS
FIXED POINT ITERATIONS MARKUS GRASMAIR 1. Fixed Point Iteration for Non-linear Equations Our goal is the solution of an equation (1) F (x) = 0, where F : R n R n is a continuous vector valued mapping in
More informationMeasuring transitivity of fuzzy pairwise comparison matrix
Measuring transitivity of fuzzy pairwise comparison matrix Jaroslav Ramík 1 Abstract. A pair-wise comparison matrix is the result of pair-wise comparison a powerful method in multi-criteria optimization.
More informationDeterminants Chapter 3 of Lay
Determinants Chapter of Lay Dr. Doreen De Leon Math 152, Fall 201 1 Introduction to Determinants Section.1 of Lay Given a square matrix A = [a ij, the determinant of A is denoted by det A or a 11 a 1j
More informationAn interval-matrix branch-and-bound algorithm for bounding eigenvalues
Optimization Methods and Software ISSN: 1055-6788 (Print) 1029-4937 (Online) Journal homepage: https://www.tandfonline.com/loi/goms20 An interval-matrix branch-and-bound algorithm for bounding eigenvalues
More informationOn Delay-Independent Diagonal Stability of Max-Min Min Congestion Control
On Delay-Independent Diagonal Stability of Max-Min Min Congestion Control Yueping Zhang Joint work with Dmitri Loguinov Internet Research Lab Department of Computer Science Texas A&M University, College
More informationLectures on Linear Algebra for IT
Lectures on Linear Algebra for IT by Mgr. Tereza Kovářová, Ph.D. following content of lectures by Ing. Petr Beremlijski, Ph.D. Department of Applied Mathematics, VSB - TU Ostrava Czech Republic 11. Determinants
More informationSpectral radius, symmetric and positive matrices
Spectral radius, symmetric and positive matrices Zdeněk Dvořák April 28, 2016 1 Spectral radius Definition 1. The spectral radius of a square matrix A is ρ(a) = max{ λ : λ is an eigenvalue of A}. For an
More informationSome bounds for the spectral radius of the Hadamard product of matrices
Some bounds for the spectral radius of the Hadamard product of matrices Tin-Yau Tam Mathematics & Statistics Auburn University Georgia State University, May 28, 05 in honor of Prof. Jean H. Bevis Some
More informationA note on the unique solution of linear complementarity problem
COMPUTATIONAL SCIENCE SHORT COMMUNICATION A note on the unique solution of linear complementarity problem Cui-Xia Li 1 and Shi-Liang Wu 1 * Received: 13 June 2016 Accepted: 14 November 2016 First Published:
More informationInverse interval matrix: A survey
Electronic Journal of Linear Algebra Volume 22 Volume 22 (2011) Article 46 2011 Inverse interval matrix: A survey Jiri Rohn Raena Farhadsefat Follow this and additional works at: http://repository.uwyo.edu/ela
More informationLecture 5. Ch. 5, Norms for vectors and matrices. Norms for vectors and matrices Why?
KTH ROYAL INSTITUTE OF TECHNOLOGY Norms for vectors and matrices Why? Lecture 5 Ch. 5, Norms for vectors and matrices Emil Björnson/Magnus Jansson/Mats Bengtsson April 27, 2016 Problem: Measure size of
More informationOn the Hermitian solutions of the
Journal of Applied Mathematics & Bioinformatics vol.1 no.2 2011 109-129 ISSN: 1792-7625 (print) 1792-8850 (online) International Scientific Press 2011 On the Hermitian solutions of the matrix equation
More informationIAI If A= [au], then IAI = [laul] which is non-negative.
Engineering Journal ofthe University of Qatar, Vol.14, 2001, pp. 127-135 STABILITY OF SQUARE INTERVAL MATRICES FOR DISCRETE TIME SYSTEMS Mutah University Karak, Jordan Email: nrousan@yahoo.com ABSTRACT
More informationADI iterations for. general elliptic problems. John Strain Mathematics Department UC Berkeley July 2013
ADI iterations for general elliptic problems John Strain Mathematics Department UC Berkeley July 2013 1 OVERVIEW Classical alternating direction implicit (ADI) iteration Essentially optimal in simple domains
More informationTOLERANCE-CONTROL SOLUTIONS TO INTERVAL LINEAR EQUATIONS
International Conference on Artificial Intelligence and Software Engineering (ICAISE 013 TOLERANCE-CONTROL SOLUTIONS TO INTERVAL LINEAR EQUATIONS LI TIAN 1, WEI LI 1, QIN WANG 1 Institute of Operational
More informationLinGloss. A glossary of linear algebra
LinGloss A glossary of linear algebra Contents: Decompositions Types of Matrices Theorems Other objects? Quasi-triangular A matrix A is quasi-triangular iff it is a triangular matrix except its diagonal
More informationPerformance of ensemble Kalman filters with small ensembles
Performance of ensemble Kalman filters with small ensembles Xin T Tong Joint work with Andrew J. Majda National University of Singapore Sunday 28 th May, 2017 X.Tong EnKF performance 1 / 31 Content Ensemble
More informationLectures on Linear Algebra for IT
Lectures on Linear Algebra for IT by Mgr Tereza Kovářová, PhD following content of lectures by Ing Petr Beremlijski, PhD Department of Applied Mathematics, VSB - TU Ostrava Czech Republic 3 Inverse Matrix
More informationBranch-and-Bound Algorithm. Pattern Recognition XI. Michal Haindl. Outline
Branch-and-Bound Algorithm assumption - can be used if a feature selection criterion satisfies the monotonicity property monotonicity property - for nested feature sets X j related X 1 X 2... X l the criterion
More informationarxiv: v1 [math.oc] 27 Feb 2018
arxiv:1802.09872v1 [math.oc] 27 Feb 2018 Interval Linear Programming under Transformations: Optimal Solutions and Optimal Value Range Elif Garajová Milan Hladík Miroslav Rada February 28, 2018 Abstract
More informationLinear Algebra review Powers of a diagonalizable matrix Spectral decomposition
Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2016 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing
More informationHere is an example of a block diagonal matrix with Jordan Blocks on the diagonal: J
Class Notes 4: THE SPECTRAL RADIUS, NORM CONVERGENCE AND SOR. Math 639d Due Date: Feb. 7 (updated: February 5, 2018) In the first part of this week s reading, we will prove Theorem 2 of the previous class.
More informationOn the spectra of striped sign patterns
On the spectra of striped sign patterns J J McDonald D D Olesky 2 M J Tsatsomeros P van den Driessche 3 June 7, 22 Abstract Sign patterns consisting of some positive and some negative columns, with at
More informationCharacterization of half-radial matrices
Characterization of half-radial matrices Iveta Hnětynková, Petr Tichý Faculty of Mathematics and Physics, Charles University, Sokolovská 83, Prague 8, Czech Republic Abstract Numerical radius r(a) is the
More informationPerron Frobenius Theory
Perron Frobenius Theory Oskar Perron Georg Frobenius (1880 1975) (1849 1917) Stefan Güttel Perron Frobenius Theory 1 / 10 Positive and Nonnegative Matrices Let A, B R m n. A B if a ij b ij i, j, A > B
More informationPERTURBATION ANAYSIS FOR THE MATRIX EQUATION X = I A X 1 A + B X 1 B. Hosoo Lee
Korean J. Math. 22 (214), No. 1, pp. 123 131 http://dx.doi.org/1.11568/jm.214.22.1.123 PERTRBATION ANAYSIS FOR THE MATRIX EQATION X = I A X 1 A + B X 1 B Hosoo Lee Abstract. The purpose of this paper is
More informationAssignment 11 (C + C ) = (C + C ) = (C + C) i(c C ) ] = i(c C) (AB) = (AB) = B A = BA 0 = [A, B] = [A, B] = (AB BA) = (AB) AB
Arfken 3.4.6 Matrix C is not Hermition. But which is Hermitian. Likewise, Assignment 11 (C + C ) = (C + C ) = (C + C) [ i(c C ) ] = i(c C ) = i(c C) = i ( C C ) Arfken 3.4.9 The matrices A and B are both
More informationSome bounds for the spectral radius of the Hadamard product of matrices
Some bounds for the spectral radius of the Hadamard product of matrices Guang-Hui Cheng, Xiao-Yu Cheng, Ting-Zhu Huang, Tin-Yau Tam. June 1, 2004 Abstract Some bounds for the spectral radius of the Hadamard
More informationThe University of Texas at Austin Department of Electrical and Computer Engineering. EE381V: Large Scale Learning Spring 2013.
The University of Texas at Austin Department of Electrical and Computer Engineering EE381V: Large Scale Learning Spring 2013 Assignment Two Caramanis/Sanghavi Due: Tuesday, Feb. 19, 2013. Computational
More information1 Last time: least-squares problems
MATH Linear algebra (Fall 07) Lecture Last time: least-squares problems Definition. If A is an m n matrix and b R m, then a least-squares solution to the linear system Ax = b is a vector x R n such that
More informationSophomoric Matrix Multiplication
Sophomoric Matrix Multiplication Carl C. Cowen IUPUI (Indiana University Purdue University Indianapolis) Universidad de Zaragoza, 3 julio 2009 Linear algebra students learn, for m n matrices A, B, y C,
More informationFormal verification for numerical methods
Formal verification for numerical methods Ioana Paşca PhD thesis at INRIA Sophia Antipolis, Marelle Team Advisor: Yves Bertot 1 7 1 Robots 2 7 Robots efficiency 2 7 Robots efficiency safety 2 7 Inside
More informationProperties for the Perron complement of three known subclasses of H-matrices
Wang et al Journal of Inequalities and Applications 2015) 2015:9 DOI 101186/s13660-014-0531-1 R E S E A R C H Open Access Properties for the Perron complement of three known subclasses of H-matrices Leilei
More informationFeshbach-Fano-R-matrix (FFR) method
Feshbach-Fano-R-matrix (FFR) method Přemysl Kolorenč Institute of Theoretical Physics Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic Feshbach-Fano-R-matrix (FFR) method
More informationNotes on Linear Algebra and Matrix Theory
Massimo Franceschet featuring Enrico Bozzo Scalar product The scalar product (a.k.a. dot product or inner product) of two real vectors x = (x 1,..., x n ) and y = (y 1,..., y n ) is not a vector but a
More informationNumerical Methods for Differential Equations Mathematical and Computational Tools
Numerical Methods for Differential Equations Mathematical and Computational Tools Gustaf Söderlind Numerical Analysis, Lund University Contents V4.16 Part 1. Vector norms, matrix norms and logarithmic
More informationLinear Algebra Review (Course Notes for Math 308H - Spring 2016)
Linear Algebra Review (Course Notes for Math 308H - Spring 2016) Dr. Michael S. Pilant February 12, 2016 1 Background: We begin with one of the most fundamental notions in R 2, distance. Letting (x 1,
More informationInstitute of Computer Science. Verification of linear (in)dependence in finite precision arithmetic
Institute of Computer Science Academy of Sciences of the Czech Republic Verification of linear (in)depence in finite precision arithmetic Jiří Rohn http://uivtx.cs.cas.cz/~rohn Technical report No. V-1156
More informationKey words. GMRES method, convergence bounds, worst-case GMRES, ideal GMRES, field of values
THE FIELD OF VALUES BOUNDS ON IDEAL GMRES JÖRG LIESEN AND PETR TICHÝ 27.03.2018) Abstract. A widely known result of Elman, and its improvements due to Starke, Eiermann and Ernst, gives a bound on the worst-case
More informationVISUALIZING PSEUDOSPECTRA FOR POLYNOMIAL EIGENVALUE PROBLEMS. Adéla Klimentová *, Michael Šebek ** Czech Technical University in Prague
VSUALZNG PSEUDOSPECTRA FOR POLYNOMAL EGENVALUE PROBLEMS Adéla Klimentová *, Michael Šebek ** * Department of Control Engineering Czech Technical University in Prague ** nstitute of nformation Theory and
More informationGeneralization of Gracia's Results
Electronic Journal of Linear Algebra Volume 30 Volume 30 (015) Article 16 015 Generalization of Gracia's Results Jun Liao Hubei Institute, jliao@hubueducn Heguo Liu Hubei University, ghliu@hubueducn Yulei
More informationQM1 Problem Set 1 solutions Mike Saelim
QM Problem Set solutions Mike Saelim If you find any errors with these solutions, please email me at mjs496@cornelledu a We can assume that the function is smooth enough to Taylor expand: fa = n c n A
More informationMa/CS 6b Class 20: Spectral Graph Theory
Ma/CS 6b Class 20: Spectral Graph Theory By Adam Sheffer Recall: Parity of a Permutation S n the set of permutations of 1,2,, n. A permutation σ S n is even if it can be written as a composition of an
More informationLinear Algebra review Powers of a diagonalizable matrix Spectral decomposition
Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2018 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing
More informationA new upper bound for the eigenvalues of the continuous algebraic Riccati equation
Electronic Journal of Linear Algebra Volume 0 Volume 0 (00) Article 3 00 A new upper bound for the eigenvalues of the continuous algebraic Riccati equation Jianzhou Liu liujz@xtu.edu.cn Juan Zhang Yu Liu
More informationj=1 u 1jv 1j. 1/ 2 Lemma 1. An orthogonal set of vectors must be linearly independent.
Lecture Notes: Orthogonal and Symmetric Matrices Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk Orthogonal Matrix Definition. Let u = [u
More informationShort Review of Basic Mathematics
Short Review of Basic Mathematics Tomas Co January 1, 1 c 8 Tomas Co, all rights reserve Contents 1 Review of Functions 5 11 Mathematical Ientities 5 1 Drills 7 1 Answers to Drills 8 Review of ODE Solutions
More informationON THE EXISTENCE OF HOPF BIFURCATION IN AN OPEN ECONOMY MODEL
Proceedings of Equadiff-11 005, pp. 9 36 ISBN 978-80-7-64-5 ON THE EXISTENCE OF HOPF BIFURCATION IN AN OPEN ECONOMY MODEL KATARÍNA MAKOVÍNYIOVÁ AND RUDOLF ZIMKA Abstract. In the paper a four dimensional
More informationCOMPARISON OF EULER'S- AND TAYLOR'S EXPANSION METHODS FOR NUMERICAL SOLUTION OF NON-LINEAR SYSTEM OF DIFFERENTIAL EQUATION
COMPARISON OF EULER'S- AND TAYLOR'S EXPANSION METHODS FOR NUMERICAL SOLUTION OF NON-LINEAR SYSTEM OF DIFFERENTIAL EQUATION Branislav Dobrucký, Mariana Beňová, Daniela Gombárska Faculty of Electrical Engineering,
More informationSpectral inequalities and equalities involving products of matrices
Spectral inequalities and equalities involving products of matrices Chi-Kwong Li 1 Department of Mathematics, College of William & Mary, Williamsburg, Virginia 23187 (ckli@math.wm.edu) Yiu-Tung Poon Department
More informationElementary linear algebra
Chapter 1 Elementary linear algebra 1.1 Vector spaces Vector spaces owe their importance to the fact that so many models arising in the solutions of specific problems turn out to be vector spaces. The
More informationMotivation: Sparse matrices and numerical PDE's
Lecture 20: Numerical Linear Algebra #4 Iterative methods and Eigenproblems Outline 1) Motivation: beyond LU for Ax=b A little PDE's and sparse matrices A) Temperature Equation B) Poisson Equation 2) Splitting
More informationDeterminants. Chia-Ping Chen. Linear Algebra. Professor Department of Computer Science and Engineering National Sun Yat-sen University 1/40
1/40 Determinants Chia-Ping Chen Professor Department of Computer Science and Engineering National Sun Yat-sen University Linear Algebra About Determinant A scalar function on the set of square matrices
More informationA global optimization method, αbb, for general twice-differentiable constrained NLPs I. Theoretical advances
Computers Chem. Engng Vol. 22, No. 9, pp. 1137 1158, 1998 Copyright 1998 Elsevier Science Ltd All rights reserved. Printed in Great Britain PII: S0098-1354(98)00027-1 0098 1354/98 $19.00#0.00 A global
More informationOn Linear Copositive Lyapunov Functions and the Stability of Switched Positive Linear Systems
1 On Linear Copositive Lyapunov Functions and the Stability of Switched Positive Linear Systems O. Mason and R. Shorten Abstract We consider the problem of common linear copositive function existence for
More informationdet(ka) = k n det A.
Properties of determinants Theorem. If A is n n, then for any k, det(ka) = k n det A. Multiplying one row of A by k multiplies the determinant by k. But ka has every row multiplied by k, so the determinant
More informationTHE PERTURBATION BOUND FOR THE SPECTRAL RADIUS OF A NON-NEGATIVE TENSOR
THE PERTURBATION BOUND FOR THE SPECTRAL RADIUS OF A NON-NEGATIVE TENSOR WEN LI AND MICHAEL K. NG Abstract. In this paper, we study the perturbation bound for the spectral radius of an m th - order n-dimensional
More informationProperties of Matrices and Operations on Matrices
Properties of Matrices and Operations on Matrices A common data structure for statistical analysis is a rectangular array or matris. Rows represent individual observational units, or just observations,
More informationComponentwise perturbation analysis for matrix inversion or the solution of linear systems leads to the Bauer-Skeel condition number ([2], [13])
SIAM Review 4():02 2, 999 ILL-CONDITIONED MATRICES ARE COMPONENTWISE NEAR TO SINGULARITY SIEGFRIED M. RUMP Abstract. For a square matrix normed to, the normwise distance to singularity is well known to
More informationEfficient Solvers for Stochastic Finite Element Saddle Point Problems
Efficient Solvers for Stochastic Finite Element Saddle Point Problems Catherine E. Powell c.powell@manchester.ac.uk School of Mathematics University of Manchester, UK Efficient Solvers for Stochastic Finite
More informationGeneral method for simulating laboratory tests with constitutive models for geomechanics
General method for simulating laboratory tests with constitutive models for geomechanics Tomáš Janda 1 and David Mašín 2 1 Czech Technical University in Prague, Faculty of Civil Engineering, Czech Republic
More informationLecture 7: Positive Semidefinite Matrices
Lecture 7: Positive Semidefinite Matrices Rajat Mittal IIT Kanpur The main aim of this lecture note is to prepare your background for semidefinite programming. We have already seen some linear algebra.
More informationJuly 21 Math 2254 sec 001 Summer 2015
July 21 Math 2254 sec 001 Summer 2015 Section 8.8: Power Series Theorem: Let a n (x c) n have positive radius of convergence R, and let the function f be defined by this power series f (x) = a n (x c)
More information11.10a Taylor and Maclaurin Series
11.10a 1 11.10a Taylor and Maclaurin Series Let y = f(x) be a differentiable function at x = a. In first semester calculus we saw that (1) f(x) f(a)+f (a)(x a), for all x near a The right-hand side of
More information