Lecture 2. LINEAR DIFFERENTIAL SYSTEMS p.1/22

Size: px
Start display at page:

Download "Lecture 2. LINEAR DIFFERENTIAL SYSTEMS p.1/22"

Transcription

1 LINEAR DIFFERENTIAL SYSTEMS Harry Trentelman University of Groningen, The Netherlands Minicourse ECC 2003 Cambridge, UK, September 2, 2003 LINEAR DIFFERENTIAL SYSTEMS p1/22

2 Part 1: Generalities LINEAR DIFFERENTIAL SYSTEMS p2/22

3 Linear differential systems We discuss the theory of dynamical systems that are LINEAR DIFFERENTIAL SYSTEMS p3/22

4 Linear differential systems We discuss the theory of dynamical systems that are 1 linear, LINEAR DIFFERENTIAL SYSTEMS p3/22

5 Linear differential systems We discuss the theory of dynamical systems that are 1 linear, meaning ; LINEAR DIFFERENTIAL SYSTEMS p3/22

6 Linear differential systems We discuss the theory of dynamical systems that are 1 linear, meaning ; 2 time-invariant, LINEAR DIFFERENTIAL SYSTEMS p3/22

7 Linear differential systems We discuss the theory of dynamical systems that are 1 linear, meaning ; 2 time-invariant, meaning where denotes the shift,, LINEAR DIFFERENTIAL SYSTEMS p3/22

8 Linear differential systems We discuss the theory of dynamical systems that are 1 linear, meaning ; 2 time-invariant, meaning where denotes the shift,, 3 differential, LINEAR DIFFERENTIAL SYSTEMS p3/22

9 Linear differential systems We discuss the theory of dynamical systems that are 1 linear, meaning ; 2 time-invariant, meaning where denotes the shift,, 3 differential, meaning consists of the solutions of a system of differential equations LINEAR DIFFERENTIAL SYSTEMS p3/22

10 Linear constant coefficient differential equations Variables: equations, up to times differentiated, Coefficients : 3 indices! LINEAR DIFFERENTIAL SYSTEMS p4/22

11 Linear constant coefficient differential equations Variables: equations, up to times differentiated, Coefficients : 3 indices! for the -th differential equation, LINEAR DIFFERENTIAL SYSTEMS p4/22

12 Linear constant coefficient differential equations Variables: equations, up to times differentiated, Coefficients : 3 indices! for the -th differential equation, for the variable involved, LINEAR DIFFERENTIAL SYSTEMS p4/22

13 Linear constant coefficient differential equations Variables: equations, up to times differentiated, Coefficients : 3 indices! for the -th differential equation, for the variable involved, for the order of differentiation LINEAR DIFFERENTIAL SYSTEMS p4/22

14 In vector/matrix notation: LINEAR DIFFERENTIAL SYSTEMS p5/22

15 In vector/matrix notation: Yields with LINEAR DIFFERENTIAL SYSTEMS p5/22

16 Combined with the polynomial matrix (in the indeterminate ) we obtain for this the short notation LINEAR DIFFERENTIAL SYSTEMS p6/22

17 Combined with the polynomial matrix (in the indeterminate ) we obtain for this the short notation Including latent variables with LINEAR DIFFERENTIAL SYSTEMS p6/22

18 Polynomial matrices A polynomial matrix is a polynomial with matrix coefficients: with LINEAR DIFFERENTIAL SYSTEMS p7/22

19 Polynomial matrices A polynomial matrix is a polynomial with matrix coefficients: with We may view also as a matrix of polynomials: with the s polynomials with real coefficients LINEAR DIFFERENTIAL SYSTEMS p7/22

20 Polynomial matrices A polynomial matrix is a polynomial with matrix coefficients: with We may view also as a matrix of polynomials: with the s polynomials with real coefficients Notation:,,, LINEAR DIFFERENTIAL SYSTEMS p7/22

21 What do we mean by the behavior of this system of differential equations? LINEAR DIFFERENTIAL SYSTEMS p8/22

22 What do we mean by the behavior of this system of differential equations? When shall we define? to be a solution of LINEAR DIFFERENTIAL SYSTEMS p8/22

23 What do we mean by the behavior of this system of differential equations? When shall we define? to be a solution of Possibilities: LINEAR DIFFERENTIAL SYSTEMS p8/22

24 What do we mean by the behavior of this system of differential equations? When shall we define? to be a solution of Possibilities: Strong solutions? LINEAR DIFFERENTIAL SYSTEMS p8/22

25 What do we mean by the behavior of this system of differential equations? When shall we define? to be a solution of Possibilities: Strong solutions? Weak solutions? LINEAR DIFFERENTIAL SYSTEMS p8/22

26 What do we mean by the behavior of this system of differential equations? When shall we define? to be a solution of Possibilities: Strong solutions? Weak solutions? (infinitely differentiable) solutions? LINEAR DIFFERENTIAL SYSTEMS p8/22

27 What do we mean by the behavior of this system of differential equations? When shall we define? to be a solution of Possibilities: Strong solutions? Weak solutions? (infinitely differentiable) solutions? Distributional solutions? LINEAR DIFFERENTIAL SYSTEMS p8/22

28 We will be pragmatic, and take the easy way out: solutions! LINEAR DIFFERENTIAL SYSTEMS p9/22

29 We will be pragmatic, and take the easy way out: solutions! -solution: is a -solution of if 1 is infinitely differentiable ( := ), and 2 LINEAR DIFFERENTIAL SYSTEMS p9/22

30 We will be pragmatic, and take the easy way out: solutions! -solution: is a -solution of if 1 is infinitely differentiable ( := ), and 2 Transmits main ideas, easier to handle, easy theory, sometimes (too) restrictive LINEAR DIFFERENTIAL SYSTEMS p9/22

31 Whence, defines the system with LINEAR DIFFERENTIAL SYSTEMS p10/22

32 Whence, defines the system with Proposition: This system is linear and time-invariant LINEAR DIFFERENTIAL SYSTEMS p10/22

33 Whence, defines the system with Proposition: This system is linear and time-invariant Note that is equal to the kernel of the operator We will therefore call or the behavior a kernel representation of this system, LINEAR DIFFERENTIAL SYSTEMS p10/22

34 Whence, defines the system with Proposition: This system is linear and time-invariant Note that is equal to the kernel of the operator We will therefore call or the behavior a kernel representation of this system, determines uniquely, the converse is not true! LINEAR DIFFERENTIAL SYSTEMS p10/22

35 Notation and nomenclature all such systems (with any - finite - number of variables) with variables (no ambiguity regarding ) LINEAR DIFFERENTIAL SYSTEMS p11/22

36 Notation and nomenclature all such systems (with any - finite - number of variables) with variables (no ambiguity regarding ) Elements of linear differential systems has behavior or : the system induced by LINEAR DIFFERENTIAL SYSTEMS p11/22

37 Part 2: Inputs and outputs LINEAR DIFFERENTIAL SYSTEMS p12/22

38 Linear differential system: LINEAR DIFFERENTIAL SYSTEMS p13/22

39 Linear differential system: induced by a polynomial matrix : variable Idea: there are degrees of freedom in the differential equation In other words: the requirement leaves some of the components unconstrained These components are arbitrary functions LINEAR DIFFERENTIAL SYSTEMS p13/22

40 Linear differential system: induced by a polynomial matrix : variable Idea: there are degrees of freedom in the differential equation In other words: the requirement leaves some of the components unconstrained These components are arbitrary functions inputs LINEAR DIFFERENTIAL SYSTEMS p13/22

41 Linear differential system: induced by a polynomial matrix : variable Idea: there are degrees of freedom in the differential equation In other words: the requirement leaves some of the components unconstrained These components are arbitrary functions inputs After choosing these free components, the remaining components are determined up to initial conditions LINEAR DIFFERENTIAL SYSTEMS p13/22

42 Linear differential system: induced by a polynomial matrix : variable Idea: there are degrees of freedom in the differential equation In other words: the requirement leaves some of the components unconstrained These components are arbitrary functions inputs After choosing these free components, the remaining components are determined up to initial conditions outputs LINEAR DIFFERENTIAL SYSTEMS p13/22

43 Linear differential system: induced by a polynomial matrix : variable Idea: there are degrees of freedom in the differential equation In other words: the requirement leaves some of the components unconstrained These components are arbitrary functions inputs After choosing these free components, the remaining components are determined up to initial conditions outputs LINEAR DIFFERENTIAL SYSTEMS p13/22

44 Example Position of point mass subject to a force : LINEAR DIFFERENTIAL SYSTEMS p14/22

45 Example Position of point mass subject to a force : Three (differential) equations, six variables does not put constraints on : is allowed to be any function After choosing, is determined up to and LINEAR DIFFERENTIAL SYSTEMS p14/22

46 Example Position of point mass subject to a force : Three (differential) equations, six variables does not put constraints on : is allowed to be any function After choosing, is determined up to and Also: does not put constraints on : is allowed to be any function After choosing, is determined uniquely LINEAR DIFFERENTIAL SYSTEMS p14/22

47 Free variables Let, Let, The functions from only the components in the index set, form again a linear differential system (the elimination theorem, see lecture 3) Denote it by obtained by selecting LINEAR DIFFERENTIAL SYSTEMS p15/22

48 Free variables Let, Let, The functions from only the components in the index set, form again a linear differential system (the elimination theorem, see lecture 3) Denote it by obtained by selecting The set of variables is called free in if where, the cardinality of the set LINEAR DIFFERENTIAL SYSTEMS p15/22

49 Maximally free Let, Let The set of variables maximally free in such that we have if it is free, and if for any is called LINEAR DIFFERENTIAL SYSTEMS p16/22

50 Maximally free Let, Let The set of variables maximally free in such that we have if it is free, and if for any is called So: if is maximally free, then any set of variables obtained by adding to this set one or more of the remaining variables is no longer free LINEAR DIFFERENTIAL SYSTEMS p16/22

51 Input/output partition Let, Possibly after permutation of its components, a partition of into, with an input/output partition in free, is called if and is maximally LINEAR DIFFERENTIAL SYSTEMS p17/22

52 Input/output partition Let, Possibly after permutation of its components, a partition of into, with an input/output partition in free, is called if and is maximally In that case, is called an input of, and is called an output of Usually, we write for, and for LINEAR DIFFERENTIAL SYSTEMS p17/22

53 Input/output partition Let, Possibly after permutation of its components, a partition of, with an input/output partition in free if, is called and into is maximally In that case, is called an input of, and is called an output of Usually, we write for, and for Non-uniqueness: for given, the manifest variable in general allows more than one input/output partition LINEAR DIFFERENTIAL SYSTEMS p17/22

54 Input/output representations Let be the system with kernel representation where, and LINEAR DIFFERENTIAL SYSTEMS p18/22

55 Input/output representations Let be the system with kernel representation where, and Question: Under what conditions on the polynomial matrices and is an input/output partition (with input and output )? LINEAR DIFFERENTIAL SYSTEMS p18/22

56 Input/output representations Let be the system with kernel representation where, and Question: Under what conditions on the polynomial matrices and is an input/output partition (with input and output )? Proposition: is an input/output partition of with input, output if and only if is square and LINEAR DIFFERENTIAL SYSTEMS p18/22

57 Input/output representations Let be the system with kernel representation where, and Question: Under what conditions on the polynomial matrices and is an input/output partition (with input and output )? Proposition: is an input/output partition of with input, output if and only if is square and The representation input/output representation of is then called an The rational matrix is called the transfer matrix of wrt the given input/output partition LINEAR DIFFERENTIAL SYSTEMS p18/22

58 Does every have an input/output representation? LINEAR DIFFERENTIAL SYSTEMS p19/22

59 Does every have an input/output representation? Theorem: Let, with manifest variable There exists (possibly after permutation of the components) a componentwise partition of into, and polynomial matrices,,, such that LINEAR DIFFERENTIAL SYSTEMS p19/22

60 Does every have an input/output representation? Theorem: Let, with manifest variable There exists (possibly after permutation of the components) a componentwise partition of into, and polynomial matrices,,, such that There even exists such a partition such that is proper LINEAR DIFFERENTIAL SYSTEMS p19/22

61 Input and output cardinality has many input/output partitions However: the number of input components in any input/output partition of is fixed This number is denoted by and is called the input cardinality of : is free in LINEAR DIFFERENTIAL SYSTEMS p20/22

62 Input and output cardinality has many input/output partitions However: the number of input components in any input/output partition of is fixed This number is denoted by and is called the input cardinality of : is free in The output cardinality of, denoted by, is the number of output components in any input/output partition of Obviously: LINEAR DIFFERENTIAL SYSTEMS p20/22

63 Input and output cardinality has many input/output partitions However: the number of input components in any input/output partition of is fixed This number is denoted by and is called the input cardinality of : is free in The output cardinality of, denoted by, is the number of output components in any input/output partition of Obviously: For : LINEAR DIFFERENTIAL SYSTEMS p20/22

64 Summarizing Linear differential systems: those decribed by linear constant coefficient differential equations, etc LINEAR DIFFERENTIAL SYSTEMS p21/22

65 Summarizing Linear differential systems: those decribed by linear constant coefficient differential equations, etc Behavior := the set of solutions (for convenience) LINEAR DIFFERENTIAL SYSTEMS p21/22

66 Summarizing Linear differential systems: those decribed by linear constant coefficient differential equations, etc Behavior := the set of solutions (for convenience) polynomial matrix, representation of the system induced by a kernel LINEAR DIFFERENTIAL SYSTEMS p21/22

67 Summarizing Linear differential systems: those decribed by linear constant coefficient differential equations, etc Behavior := the set of solutions (for convenience) polynomial matrix, representation of the system induced by a kernel For a given system, is an input/output partition if the set of components of is maximally free: these components can be chosen arbitrarily The components of are then determined up to initial conditions LINEAR DIFFERENTIAL SYSTEMS p21/22

68 Summarizing Linear differential systems: those decribed by linear constant coefficient differential equations, etc Behavior := the set of solutions (for convenience) polynomial matrix, representation of the system induced by a kernel For a given system, is an input/output partition if the set of components of is maximally free: these components can be chosen arbitrarily The components of are then determined up to initial conditions An input/output representation of is a special kind of kernel representation:,, with partition Equivalent with: is an input/output LINEAR DIFFERENTIAL SYSTEMS p21/22

69 A system has in general many i/o representations, so also i/o partitions LINEAR DIFFERENTIAL SYSTEMS p22/22

70 A system has in general many i/o representations, so also i/o partitions Given, the number of components of is the same in any i/o partition This number is called the input cardinality of LINEAR DIFFERENTIAL SYSTEMS p22/22

71 End of LINEAR DIFFERENTIAL SYSTEMS p23/22

Stabilization, Pole Placement, and Regular Implementability

Stabilization, Pole Placement, and Regular Implementability IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 47, NO. 5, MAY 2002 735 Stabilization, Pole Placement, and Regular Implementability Madhu N. Belur and H. L. Trentelman, Senior Member, IEEE Abstract In this

More information

The Behavioral Approach to Systems Theory

The Behavioral Approach to Systems Theory The Behavioral Approach to Systems Theory Paolo Rapisarda, Un. of Southampton, U.K. & Jan C. Willems, K.U. Leuven, Belgium MTNS 2006 Kyoto, Japan, July 24 28, 2006 Lecture 2: Representations and annihilators

More information

Linear Hamiltonian systems

Linear Hamiltonian systems Linear Hamiltonian systems P. Rapisarda H.L. Trentelman Abstract We study linear Hamiltonian systems using bilinear and quadratic differential forms. Such a representation-free approach allows to use the

More information

Permutation groups/1. 1 Automorphism groups, permutation groups, abstract

Permutation groups/1. 1 Automorphism groups, permutation groups, abstract Permutation groups Whatever you have to do with a structure-endowed entity Σ try to determine its group of automorphisms... You can expect to gain a deep insight into the constitution of Σ in this way.

More information

POLYNOMIAL EMBEDDING ALGORITHMS FOR CONTROLLERS IN A BEHAVIORAL FRAMEWORK

POLYNOMIAL EMBEDDING ALGORITHMS FOR CONTROLLERS IN A BEHAVIORAL FRAMEWORK 1 POLYNOMIAL EMBEDDING ALGORITHMS FOR CONTROLLERS IN A BEHAVIORAL FRAMEWORK H.L. Trentelman, Member, IEEE, R. Zavala Yoe, and C. Praagman Abstract In this paper we will establish polynomial algorithms

More information

DISTANCE BETWEEN BEHAVIORS AND RATIONAL REPRESENTATIONS

DISTANCE BETWEEN BEHAVIORS AND RATIONAL REPRESENTATIONS DISTANCE BETWEEN BEHAVIORS AND RATIONAL REPRESENTATIONS H.L. TRENTELMAN AND S.V. GOTTIMUKKALA Abstract. In this paper we study notions of distance between behaviors of linear differential systems. We introduce

More information

Dynamical system. The set of functions (signals) w : T W from T to W is denoted by W T. W variable space. T R time axis. W T trajectory space

Dynamical system. The set of functions (signals) w : T W from T to W is denoted by W T. W variable space. T R time axis. W T trajectory space Dynamical system The set of functions (signals) w : T W from T to W is denoted by W T. W variable space T R time axis W T trajectory space A dynamical system B W T is a set of trajectories (a behaviour).

More information

Lecture 3. Jan C. Willems. University of Leuven, Belgium. Minicourse ECC 2003 Cambridge, UK, September 2, 2003

Lecture 3. Jan C. Willems. University of Leuven, Belgium. Minicourse ECC 2003 Cambridge, UK, September 2, 2003 Lecture 3 The ELIMINATION Problem Jan C. Willems University of Leuven, Belgium Minicourse ECC 2003 Cambridge, UK, September 2, 2003 Lecture 3 The ELIMINATION Problem p.1/22 Problematique Develop a theory

More information

The Behavioral Approach to Systems Theory

The Behavioral Approach to Systems Theory The Behavioral Approach to Systems Theory... and DAEs Patrick Kürschner July 21, 2010 1/31 Patrick Kürschner The Behavioral Approach to Systems Theory Outline 1 Introduction 2 Mathematical Models 3 Linear

More information

Jordan normal form notes (version date: 11/21/07)

Jordan normal form notes (version date: 11/21/07) Jordan normal form notes (version date: /2/7) If A has an eigenbasis {u,, u n }, ie a basis made up of eigenvectors, so that Au j = λ j u j, then A is diagonal with respect to that basis To see this, let

More information

Lecture 3 Eigenvalues and Eigenvectors

Lecture 3 Eigenvalues and Eigenvectors Lecture 3 Eigenvalues and Eigenvectors Eivind Eriksen BI Norwegian School of Management Department of Economics September 10, 2010 Eivind Eriksen (BI Dept of Economics) Lecture 3 Eigenvalues and Eigenvectors

More information

Linear Algebra. Min Yan

Linear Algebra. Min Yan Linear Algebra Min Yan January 2, 2018 2 Contents 1 Vector Space 7 1.1 Definition................................. 7 1.1.1 Axioms of Vector Space..................... 7 1.1.2 Consequence of Axiom......................

More information

TC10 / 3. Finite fields S. Xambó

TC10 / 3. Finite fields S. Xambó TC10 / 3. Finite fields S. Xambó The ring Construction of finite fields The Frobenius automorphism Splitting field of a polynomial Structure of the multiplicative group of a finite field Structure of the

More information

1 Last time: linear systems and row operations

1 Last time: linear systems and row operations 1 Last time: linear systems and row operations Here s what we did last time: a system of linear equations or linear system is a list of equations a 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22

More information

Lecture 2 (Limits) tangent line secant line

Lecture 2 (Limits) tangent line secant line Lecture 2 (Limits) We shall start with the tangent line problem. Definition: A tangent line (Latin word 'touching') to the function f(x) at the point is a line that touches the graph of the function at

More information

The Chromatic Number of Ordered Graphs With Constrained Conflict Graphs

The Chromatic Number of Ordered Graphs With Constrained Conflict Graphs The Chromatic Number of Ordered Graphs With Constrained Conflict Graphs Maria Axenovich and Jonathan Rollin and Torsten Ueckerdt September 3, 016 Abstract An ordered graph G is a graph whose vertex set

More information

Discrete Mathematics 2007: Lecture 5 Infinite sets

Discrete Mathematics 2007: Lecture 5 Infinite sets Discrete Mathematics 2007: Lecture 5 Infinite sets Debrup Chakraborty 1 Countability The natural numbers originally arose from counting elements in sets. There are two very different possible sizes for

More information

MODEL ANSWERS TO HWK #10

MODEL ANSWERS TO HWK #10 MODEL ANSWERS TO HWK #10 1. (i) As x + 4 has degree one, either it divides x 3 6x + 7 or these two polynomials are coprime. But if x + 4 divides x 3 6x + 7 then x = 4 is a root of x 3 6x + 7, which it

More information

Notes on Row Reduction

Notes on Row Reduction Notes on Row Reduction Francis J. Narcowich Department of Mathematics Texas A&M University September The Row-Reduction Algorithm The row-reduced form of a matrix contains a great deal of information, both

More information

On a correlation inequality of Farr

On a correlation inequality of Farr On a correlation inequality of Farr Colin McDiarmid, Department of Statistics Oxford University Abstract Suppose that each vertex of a graph independently chooses a colour uniformly from the set {1,...,

More information

Lecture 7 Cyclic groups and subgroups

Lecture 7 Cyclic groups and subgroups Lecture 7 Cyclic groups and subgroups Review Types of groups we know Numbers: Z, Q, R, C, Q, R, C Matrices: (M n (F ), +), GL n (F ), where F = Q, R, or C. Modular groups: Z/nZ and (Z/nZ) Dihedral groups:

More information

PERIODIC POINTS OF THE FAMILY OF TENT MAPS

PERIODIC POINTS OF THE FAMILY OF TENT MAPS PERIODIC POINTS OF THE FAMILY OF TENT MAPS ROBERTO HASFURA-B. AND PHILLIP LYNCH 1. INTRODUCTION. Of interest in this article is the dynamical behavior of the one-parameter family of maps T (x) = (1/2 x

More information

Linear Algebra Short Course Lecture 2

Linear Algebra Short Course Lecture 2 Linear Algebra Short Course Lecture 2 Matthew J. Holland matthew-h@is.naist.jp Mathematical Informatics Lab Graduate School of Information Science, NAIST 1 Some useful references Introduction to linear

More information

BASIC GROUP THEORY : G G G,

BASIC GROUP THEORY : G G G, BASIC GROUP THEORY 18.904 1. Definitions Definition 1.1. A group (G, ) is a set G with a binary operation : G G G, and a unit e G, possessing the following properties. (1) Unital: for g G, we have g e

More information

Real Time Value Iteration and the State-Action Value Function

Real Time Value Iteration and the State-Action Value Function MS&E338 Reinforcement Learning Lecture 3-4/9/18 Real Time Value Iteration and the State-Action Value Function Lecturer: Ben Van Roy Scribe: Apoorva Sharma and Tong Mu 1 Review Last time we left off discussing

More information

5 Set Operations, Functions, and Counting

5 Set Operations, Functions, and Counting 5 Set Operations, Functions, and Counting Let N denote the positive integers, N 0 := N {0} be the non-negative integers and Z = N 0 ( N) the positive and negative integers including 0, Q the rational numbers,

More information

Irreducible subgroups of algebraic groups

Irreducible subgroups of algebraic groups Irreducible subgroups of algebraic groups Martin W. Liebeck Department of Mathematics Imperial College London SW7 2BZ England Donna M. Testerman Department of Mathematics University of Lausanne Switzerland

More information

Chapter 2 Linear Transformations

Chapter 2 Linear Transformations Chapter 2 Linear Transformations Linear Transformations Loosely speaking, a linear transformation is a function from one vector space to another that preserves the vector space operations. Let us be more

More information

LINEAR EQUATIONS WITH UNKNOWNS FROM A MULTIPLICATIVE GROUP IN A FUNCTION FIELD. To Professor Wolfgang Schmidt on his 75th birthday

LINEAR EQUATIONS WITH UNKNOWNS FROM A MULTIPLICATIVE GROUP IN A FUNCTION FIELD. To Professor Wolfgang Schmidt on his 75th birthday LINEAR EQUATIONS WITH UNKNOWNS FROM A MULTIPLICATIVE GROUP IN A FUNCTION FIELD JAN-HENDRIK EVERTSE AND UMBERTO ZANNIER To Professor Wolfgang Schmidt on his 75th birthday 1. Introduction Let K be a field

More information

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 8 MATRICES III Rank of a matrix 2 General systems of linear equations 3 Eigenvalues and eigenvectors Rank of a matrix

More information

Expressions for the covariance matrix of covariance data

Expressions for the covariance matrix of covariance data Expressions for the covariance matrix of covariance data Torsten Söderström Division of Systems and Control, Department of Information Technology, Uppsala University, P O Box 337, SE-7505 Uppsala, Sweden

More information

Department of Computer Science University at Albany, State University of New York Solutions to Sample Discrete Mathematics Examination I (Spring 2008)

Department of Computer Science University at Albany, State University of New York Solutions to Sample Discrete Mathematics Examination I (Spring 2008) Department of Computer Science University at Albany, State University of New York Solutions to Sample Discrete Mathematics Examination I (Spring 2008) Problem 1: Suppose A, B, C and D are arbitrary sets.

More information

Lecture 11: Finish Gaussian elimination and applications; intro to eigenvalues and eigenvectors (1)

Lecture 11: Finish Gaussian elimination and applications; intro to eigenvalues and eigenvectors (1) Lecture 11: Finish Gaussian elimination and applications; intro to eigenvalues and eigenvectors (1) Travis Schedler Tue, Oct 18, 2011 (version: Tue, Oct 18, 6:00 PM) Goals (2) Solving systems of equations

More information

Null Controllability of Discrete-time Linear Systems with Input and State Constraints

Null Controllability of Discrete-time Linear Systems with Input and State Constraints Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, 2008 Null Controllability of Discrete-time Linear Systems with Input and State Constraints W.P.M.H. Heemels and

More information

Algebra Homework, Edition 2 9 September 2010

Algebra Homework, Edition 2 9 September 2010 Algebra Homework, Edition 2 9 September 2010 Problem 6. (1) Let I and J be ideals of a commutative ring R with I + J = R. Prove that IJ = I J. (2) Let I, J, and K be ideals of a principal ideal domain.

More information

SETS AND FUNCTIONS JOSHUA BALLEW

SETS AND FUNCTIONS JOSHUA BALLEW SETS AND FUNCTIONS JOSHUA BALLEW 1. Sets As a review, we begin by considering a naive look at set theory. For our purposes, we define a set as a collection of objects. Except for certain sets like N, Z,

More information

18.06 Problem Set 3 - Solutions Due Wednesday, 26 September 2007 at 4 pm in

18.06 Problem Set 3 - Solutions Due Wednesday, 26 September 2007 at 4 pm in 8.6 Problem Set 3 - s Due Wednesday, 26 September 27 at 4 pm in 2-6. Problem : (=2+2+2+2+2) A vector space is by definition a nonempty set V (whose elements are called vectors) together with rules of addition

More information

Hermite normal form: Computation and applications

Hermite normal form: Computation and applications Integer Points in Polyhedra Gennady Shmonin Hermite normal form: Computation and applications February 24, 2009 1 Uniqueness of Hermite normal form In the last lecture, we showed that if B is a rational

More information

INVARIANT IDEALS OF ABELIAN GROUP ALGEBRAS UNDER THE MULTIPLICATIVE ACTION OF A FIELD, II

INVARIANT IDEALS OF ABELIAN GROUP ALGEBRAS UNDER THE MULTIPLICATIVE ACTION OF A FIELD, II PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 00, Number 0, Pages 000 000 S 0002-9939(XX)0000-0 INVARIANT IDEALS OF ABELIAN GROUP ALGEBRAS UNDER THE MULTIPLICATIVE ACTION OF A FIELD, II J. M.

More information

RING ELEMENTS AS SUMS OF UNITS

RING ELEMENTS AS SUMS OF UNITS 1 RING ELEMENTS AS SUMS OF UNITS CHARLES LANSKI AND ATTILA MARÓTI Abstract. In an Artinian ring R every element of R can be expressed as the sum of two units if and only if R/J(R) does not contain a summand

More information

FREE VIBRATION RESPONSE OF UNDAMPED SYSTEMS

FREE VIBRATION RESPONSE OF UNDAMPED SYSTEMS Lecture Notes: STRUCTURAL DYNAMICS / FALL 2011 / Page: 1 FREE VIBRATION RESPONSE OF UNDAMPED SYSTEMS : : 0, 0 As demonstrated previously, the above Equation of Motion (free-vibration equation) has a solution

More information

The local equivalence of two distances between clusterings: the Misclassification Error metric and the χ 2 distance

The local equivalence of two distances between clusterings: the Misclassification Error metric and the χ 2 distance The local equivalence of two distances between clusterings: the Misclassification Error metric and the χ 2 distance Marina Meilă University of Washington Department of Statistics Box 354322 Seattle, WA

More information

ACI-matrices all of whose completions have the same rank

ACI-matrices all of whose completions have the same rank ACI-matrices all of whose completions have the same rank Zejun Huang, Xingzhi Zhan Department of Mathematics East China Normal University Shanghai 200241, China Abstract We characterize the ACI-matrices

More information

Solving Homogeneous Systems with Sub-matrices

Solving Homogeneous Systems with Sub-matrices Pure Mathematical Sciences, Vol 7, 218, no 1, 11-18 HIKARI Ltd, wwwm-hikaricom https://doiorg/112988/pms218843 Solving Homogeneous Systems with Sub-matrices Massoud Malek Mathematics, California State

More information

Outline. Complexity Theory. Introduction. What is abduction? Motivation. Reference VU , SS Logic-Based Abduction

Outline. Complexity Theory. Introduction. What is abduction? Motivation. Reference VU , SS Logic-Based Abduction Complexity Theory Complexity Theory Outline Complexity Theory VU 181.142, SS 2018 7. Logic-Based Abduction Reinhard Pichler Institut für Informationssysteme Arbeitsbereich DBAI Technische Universität Wien

More information

CONVEX OPTIMIZATION OVER POSITIVE POLYNOMIALS AND FILTER DESIGN. Y. Genin, Y. Hachez, Yu. Nesterov, P. Van Dooren

CONVEX OPTIMIZATION OVER POSITIVE POLYNOMIALS AND FILTER DESIGN. Y. Genin, Y. Hachez, Yu. Nesterov, P. Van Dooren CONVEX OPTIMIZATION OVER POSITIVE POLYNOMIALS AND FILTER DESIGN Y. Genin, Y. Hachez, Yu. Nesterov, P. Van Dooren CESAME, Université catholique de Louvain Bâtiment Euler, Avenue G. Lemaître 4-6 B-1348 Louvain-la-Neuve,

More information

Computer Science & Engineering 423/823 Design and Analysis of Algorithms

Computer Science & Engineering 423/823 Design and Analysis of Algorithms Bipartite Matching Computer Science & Engineering 423/823 Design and s Lecture 07 (Chapter 26) Stephen Scott (Adapted from Vinodchandran N. Variyam) 1 / 36 Spring 2010 Bipartite Matching 2 / 36 Can use

More information

Section 8.3 Partial Fraction Decomposition

Section 8.3 Partial Fraction Decomposition Section 8.6 Lecture Notes Page 1 of 10 Section 8.3 Partial Fraction Decomposition Partial fraction decomposition involves decomposing a rational function, or reversing the process of combining two or more

More information

NUMBERS WITH INTEGER COMPLEXITY CLOSE TO THE LOWER BOUND

NUMBERS WITH INTEGER COMPLEXITY CLOSE TO THE LOWER BOUND #A1 INTEGERS 12A (2012): John Selfridge Memorial Issue NUMBERS WITH INTEGER COMPLEXITY CLOSE TO THE LOWER BOUND Harry Altman Department of Mathematics, University of Michigan, Ann Arbor, Michigan haltman@umich.edu

More information

The z-transform Part 2

The z-transform Part 2 http://faculty.kfupm.edu.sa/ee/muqaibel/ The z-transform Part 2 Dr. Ali Hussein Muqaibel The material to be covered in this lecture is as follows: Properties of the z-transform Linearity Initial and final

More information

Discrete Mathematics. Benny George K. September 22, 2011

Discrete Mathematics. Benny George K. September 22, 2011 Discrete Mathematics Benny George K Department of Computer Science and Engineering Indian Institute of Technology Guwahati ben@iitg.ernet.in September 22, 2011 Set Theory Elementary Concepts Let A and

More information

Lecture Notes for Math 1000

Lecture Notes for Math 1000 Lecture Notes for Math 1000 Dr. Xiang-Sheng Wang Memorial University of Newfoundland Office: HH-2016, Phone: 864-4321 Office hours: 13:00-15:00 Wednesday, 12:00-13:00 Friday Email: swang@mun.ca Course

More information

A Short Overview of Orthogonal Arrays

A Short Overview of Orthogonal Arrays A Short Overview of Orthogonal Arrays John Stufken Department of Statistics University of Georgia Isaac Newton Institute September 5, 2011 John Stufken (University of Georgia) Orthogonal Arrays September

More information

Methods for Solving Linear Systems Part 2

Methods for Solving Linear Systems Part 2 Methods for Solving Linear Systems Part 2 We have studied the properties of matrices and found out that there are more ways that we can solve Linear Systems. In Section 7.3, we learned that we can use

More information

AN ALGEBRAIC INTERPRETATION OF THE q-binomial COEFFICIENTS. Michael Braun

AN ALGEBRAIC INTERPRETATION OF THE q-binomial COEFFICIENTS. Michael Braun International Electronic Journal of Algebra Volume 6 (2009) 23-30 AN ALGEBRAIC INTERPRETATION OF THE -BINOMIAL COEFFICIENTS Michael Braun Received: 28 February 2008; Revised: 2 March 2009 Communicated

More information

LINEAR TIME-INVARIANT DIFFERENTIAL SYSTEMS

LINEAR TIME-INVARIANT DIFFERENTIAL SYSTEMS p. 1/83 Elgersburg Lectures March 2010 Lecture II LINEAR TIME-INVARIANT DIFFERENTIAL SYSTEMS p. 2/83 Theme Linear time-invariant differential systems are important for several reasons. They occur often

More information

Equational Logic. Chapter Syntax Terms and Term Algebras

Equational Logic. Chapter Syntax Terms and Term Algebras Chapter 2 Equational Logic 2.1 Syntax 2.1.1 Terms and Term Algebras The natural logic of algebra is equational logic, whose propositions are universally quantified identities between terms built up from

More information

Semicontinuity of rank and nullity and some consequences

Semicontinuity of rank and nullity and some consequences Semicontinuity of rank and nullity and some consequences Andrew D Lewis 2009/01/19 Upper and lower semicontinuity Let us first define what we mean by the two versions of semicontinuity 1 Definition: (Upper

More information

1111: Linear Algebra I

1111: Linear Algebra I 1111: Linear Algebra I Dr. Vladimir Dotsenko (Vlad) Lecture 6 Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Lecture 6 1 / 14 Gauss Jordan elimination Last time we discussed bringing matrices to reduced

More information

On the centralizer and the commutator subgroup of an automorphism

On the centralizer and the commutator subgroup of an automorphism Noname manuscript No. (will be inserted by the editor) On the centralizer and the commutator subgroup of an automorphism Gérard Endimioni Primož Moravec the date of receipt and acceptance should be inserted

More information

On Antichains in Product Posets

On Antichains in Product Posets On Antichains in Product Posets Sergei L. Bezrukov Department of Math and Computer Science University of Wisconsin - Superior Superior, WI 54880, USA sb@mcs.uwsuper.edu Ian T. Roberts School of Engineering

More information

Regional Solution of Constrained LQ Optimal Control

Regional Solution of Constrained LQ Optimal Control Regional Solution of Constrained LQ Optimal Control José DeDoná September 2004 Outline 1 Recap on the Solution for N = 2 2 Regional Explicit Solution Comparison with the Maximal Output Admissible Set 3

More information

Research Article Minor Prime Factorization for n-d Polynomial Matrices over Arbitrary Coefficient Field

Research Article Minor Prime Factorization for n-d Polynomial Matrices over Arbitrary Coefficient Field Complexity, Article ID 6235649, 9 pages https://doi.org/10.1155/2018/6235649 Research Article Minor Prime Factorization for n-d Polynomial Matrices over Arbitrary Coefficient Field Jinwang Liu, Dongmei

More information

LIMITS AT INFINITY MR. VELAZQUEZ AP CALCULUS

LIMITS AT INFINITY MR. VELAZQUEZ AP CALCULUS LIMITS AT INFINITY MR. VELAZQUEZ AP CALCULUS RECALL: VERTICAL ASYMPTOTES Remember that for a rational function, vertical asymptotes occur at values of x = a which have infinite its (either positive or

More information

A SIMPLE PROOF OF THE MARKER-STEINHORN THEOREM FOR EXPANSIONS OF ORDERED ABELIAN GROUPS

A SIMPLE PROOF OF THE MARKER-STEINHORN THEOREM FOR EXPANSIONS OF ORDERED ABELIAN GROUPS A SIMPLE PROOF OF THE MARKER-STEINHORN THEOREM FOR EXPANSIONS OF ORDERED ABELIAN GROUPS ERIK WALSBERG Abstract. We give a short and self-contained proof of the Marker- Steinhorn Theorem for o-minimal expansions

More information

On at systems behaviors and observable image representations

On at systems behaviors and observable image representations Available online at www.sciencedirect.com Systems & Control Letters 51 (2004) 51 55 www.elsevier.com/locate/sysconle On at systems behaviors and observable image representations H.L. Trentelman Institute

More information

Lecture 6. Numerical methods. Approximation of functions

Lecture 6. Numerical methods. Approximation of functions Lecture 6 Numerical methods Approximation of functions Lecture 6 OUTLINE 1. Approximation and interpolation 2. Least-square method basis functions design matrix residual weighted least squares normal equation

More information

CSC 2541: Bayesian Methods for Machine Learning

CSC 2541: Bayesian Methods for Machine Learning CSC 2541: Bayesian Methods for Machine Learning Radford M. Neal, University of Toronto, 2011 Lecture 4 Problem: Density Estimation We have observed data, y 1,..., y n, drawn independently from some unknown

More information

ENTRY GROUP THEORY. [ENTRY GROUP THEORY] Authors: started Mark Lezama: October 2003 Literature: Algebra by Michael Artin, Mathworld.

ENTRY GROUP THEORY. [ENTRY GROUP THEORY] Authors: started Mark Lezama: October 2003 Literature: Algebra by Michael Artin, Mathworld. ENTRY GROUP THEORY [ENTRY GROUP THEORY] Authors: started Mark Lezama: October 2003 Literature: Algebra by Michael Artin, Mathworld Group theory [Group theory] is studies algebraic objects called groups.

More information

Homework #5 Solutions

Homework #5 Solutions Homework #5 Solutions p 83, #16. In order to find a chain a 1 a 2 a n of subgroups of Z 240 with n as large as possible, we start at the top with a n = 1 so that a n = Z 240. In general, given a i we will

More information

ALGEBRA II: RINGS AND MODULES OVER LITTLE RINGS.

ALGEBRA II: RINGS AND MODULES OVER LITTLE RINGS. ALGEBRA II: RINGS AND MODULES OVER LITTLE RINGS. KEVIN MCGERTY. 1. RINGS The central characters of this course are algebraic objects known as rings. A ring is any mathematical structure where you can add

More information

Chapter 25 Finite Simple Groups. Chapter 25 Finite Simple Groups

Chapter 25 Finite Simple Groups. Chapter 25 Finite Simple Groups Historical Background Definition A group is simple if it has no nontrivial proper normal subgroup. The definition was proposed by Galois; he showed that A n is simple for n 5 in 1831. It is an important

More information

OPTIMAL H 2 MODEL REDUCTION IN STATE-SPACE: A CASE STUDY

OPTIMAL H 2 MODEL REDUCTION IN STATE-SPACE: A CASE STUDY OPTIMAL H 2 MODEL REDUCTION IN STATE-SPACE: A CASE STUDY RLM Peeters, B Hanzon, D Jibetean Department of Mathematics, Universiteit Maastricht, PO Box 66, 6200 MD Maastricht, The Netherlands, E-mail: ralfpeeters@mathunimaasnl

More information

Ahlswede Khachatrian Theorems: Weighted, Infinite, and Hamming

Ahlswede Khachatrian Theorems: Weighted, Infinite, and Hamming Ahlswede Khachatrian Theorems: Weighted, Infinite, and Hamming Yuval Filmus April 4, 2017 Abstract The seminal complete intersection theorem of Ahlswede and Khachatrian gives the maximum cardinality of

More information

Weyl s Character Formula for Representations of Semisimple Lie Algebras

Weyl s Character Formula for Representations of Semisimple Lie Algebras Weyl s Character Formula for Representations of Semisimple Lie Algebras Ben Reason University of Toronto December 22, 2005 1 Introduction Weyl s character formula is a useful tool in understanding the

More information

Chapter 12. The cross ratio Klein s Erlanger program The projective line. Math 4520, Fall 2017

Chapter 12. The cross ratio Klein s Erlanger program The projective line. Math 4520, Fall 2017 Chapter 12 The cross ratio Math 4520, Fall 2017 We have studied the collineations of a projective plane, the automorphisms of the underlying field, the linear functions of Affine geometry, etc. We have

More information

Involutions by Descents/Ascents and Symmetric Integral Matrices. Alan Hoffman Fest - my hero Rutgers University September 2014

Involutions by Descents/Ascents and Symmetric Integral Matrices. Alan Hoffman Fest - my hero Rutgers University September 2014 by Descents/Ascents and Symmetric Integral Matrices Richard A. Brualdi University of Wisconsin-Madison Joint work with Shi-Mei Ma: European J. Combins. (to appear) Alan Hoffman Fest - my hero Rutgers University

More information

2.23 Theorem. Let A and B be sets in a metric space. If A B, then L(A) L(B).

2.23 Theorem. Let A and B be sets in a metric space. If A B, then L(A) L(B). 2.23 Theorem. Let A and B be sets in a metric space. If A B, then L(A) L(B). 2.24 Theorem. Let A and B be sets in a metric space. Then L(A B) = L(A) L(B). It is worth noting that you can t replace union

More information

A Do It Yourself Guide to Linear Algebra

A Do It Yourself Guide to Linear Algebra A Do It Yourself Guide to Linear Algebra Lecture Notes based on REUs, 2001-2010 Instructor: László Babai Notes compiled by Howard Liu 6-30-2010 1 Vector Spaces 1.1 Basics Definition 1.1.1. A vector space

More information

1 Invariant subspaces

1 Invariant subspaces MATH 2040 Linear Algebra II Lecture Notes by Martin Li Lecture 8 Eigenvalues, eigenvectors and invariant subspaces 1 In previous lectures we have studied linear maps T : V W from a vector space V to another

More information

Matrix Algebra Lecture Notes. 1 What is Matrix Algebra? Last change: 18 July Linear forms

Matrix Algebra Lecture Notes. 1 What is Matrix Algebra? Last change: 18 July Linear forms Matrix Algebra Lecture Notes Last change: 18 July 2017 1 What is Matrix Algebra? 1.1 Linear forms It is well-known that the total cost of a purchase of amounts (in kilograms) g 1, g 2, g 3 of some goods

More information

Infinite-Dimensional Triangularization

Infinite-Dimensional Triangularization Infinite-Dimensional Triangularization Zachary Mesyan March 11, 2018 Abstract The goal of this paper is to generalize the theory of triangularizing matrices to linear transformations of an arbitrary vector

More information

Foundations of Mathematics

Foundations of Mathematics Foundations of Mathematics L. Pedro Poitevin 1. Preliminaries 1.1. Sets We will naively think of a set as a collection of mathematical objects, called its elements or members. To indicate that an object

More information

arxiv: v1 [math.rt] 7 Oct 2014

arxiv: v1 [math.rt] 7 Oct 2014 A direct approach to the rational normal form arxiv:1410.1683v1 [math.rt] 7 Oct 2014 Klaus Bongartz 8. Oktober 2014 In courses on linear algebra the rational normal form of a matrix is usually derived

More information

Ultraproducts of Finite Groups

Ultraproducts of Finite Groups Ultraproducts of Finite Groups Ben Reid May 11, 010 1 Background 1.1 Ultrafilters Let S be any set, and let P (S) denote the power set of S. We then call ψ P (S) a filter over S if the following conditions

More information

On cardinality of Pareto spectra

On cardinality of Pareto spectra Electronic Journal of Linear Algebra Volume 22 Volume 22 (2011) Article 50 2011 On cardinality of Pareto spectra Alberto Seeger Jose Vicente-Perez Follow this and additional works at: http://repository.uwyo.edu/ela

More information

European Embedded Control Institute. Graduate School on Control Spring The Behavioral Approach to Modeling and Control.

European Embedded Control Institute. Graduate School on Control Spring The Behavioral Approach to Modeling and Control. p. 1/50 European Embedded Control Institute Graduate School on Control Spring 2010 The Behavioral Approach to Modeling and Control Lecture VI CONTROLLABILITY p. 2/50 Theme Behavioral controllability, the

More information

Lecture Summaries for Linear Algebra M51A

Lecture Summaries for Linear Algebra M51A These lecture summaries may also be viewed online by clicking the L icon at the top right of any lecture screen. Lecture Summaries for Linear Algebra M51A refers to the section in the textbook. Lecture

More information

Chordal Coxeter Groups

Chordal Coxeter Groups arxiv:math/0607301v1 [math.gr] 12 Jul 2006 Chordal Coxeter Groups John Ratcliffe and Steven Tschantz Mathematics Department, Vanderbilt University, Nashville TN 37240, USA Abstract: A solution of the isomorphism

More information

Convolutional Codes. Telecommunications Laboratory. Alex Balatsoukas-Stimming. Technical University of Crete. November 6th, 2008

Convolutional Codes. Telecommunications Laboratory. Alex Balatsoukas-Stimming. Technical University of Crete. November 6th, 2008 Convolutional Codes Telecommunications Laboratory Alex Balatsoukas-Stimming Technical University of Crete November 6th, 2008 Telecommunications Laboratory (TUC) Convolutional Codes November 6th, 2008 1

More information

Lecture Note on Linear Algebra 1. Systems of Linear Equations

Lecture Note on Linear Algebra 1. Systems of Linear Equations Lecture Note on Linear Algebra 1 Systems of Linear Equations Wei-Shi Zheng, 2012 1 Why Learning Linear Algebra( 5 ê)? Solving linear equation system is the heart of linear algebra Linear algebra is widely

More information

ELEC system identification workshop. Behavioral approach

ELEC system identification workshop. Behavioral approach 1 / 33 ELEC system identification workshop Behavioral approach Ivan Markovsky 2 / 33 The course consists of lectures and exercises Session 1: behavioral approach to data modeling Session 2: subspace identification

More information

Spiral Review Probability, Enter Your Grade Online Quiz - Probability Pascal's Triangle, Enter Your Grade

Spiral Review Probability, Enter Your Grade Online Quiz - Probability Pascal's Triangle, Enter Your Grade Course Description This course includes an in-depth analysis of algebraic problem solving preparing for College Level Algebra. Topics include: Equations and Inequalities, Linear Relations and Functions,

More information

THE STABLE EMBEDDING PROBLEM

THE STABLE EMBEDDING PROBLEM THE STABLE EMBEDDING PROBLEM R. Zavala Yoé C. Praagman H.L. Trentelman Department of Econometrics, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands Research Institute for Mathematics

More information

Vector spaces. EE 387, Notes 8, Handout #12

Vector spaces. EE 387, Notes 8, Handout #12 Vector spaces EE 387, Notes 8, Handout #12 A vector space V of vectors over a field F of scalars is a set with a binary operator + on V and a scalar-vector product satisfying these axioms: 1. (V, +) is

More information

Vector Space Concepts

Vector Space Concepts Vector Space Concepts ECE 174 Introduction to Linear & Nonlinear Optimization Ken Kreutz-Delgado ECE Department, UC San Diego Ken Kreutz-Delgado (UC San Diego) ECE 174 Fall 2016 1 / 25 Vector Space Theory

More information

Math 109 September 1, 2016

Math 109 September 1, 2016 Math 109 September 1, 2016 Question 1 Given that the proposition P Q is true. Which of the following must also be true? A. (not P ) or Q. B. (not Q) implies (not P ). C. Q implies P. D. A and B E. A, B,

More information

Partitions and Algebraic Structures

Partitions and Algebraic Structures CHAPTER A Partitions and Algebraic Structures In this chapter, we introduce partitions of natural numbers and the Ferrers diagrams. The algebraic structures of partitions such as addition, multiplication

More information

Technische Universität München Zentrum Mathematik

Technische Universität München Zentrum Mathematik Technische Universität München Zentrum Mathematik Prof. Dr. Dr. Jürgen Richter-Gebert, Bernhard Werner Projective Geometry SS 8 https://www-m.ma.tum.de/bin/view/lehre/ss8/pgss8/webhome Solutions for Worksheet

More information

STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Computer Science! Department of Statistical Sciences! rsalakhu@cs.toronto.edu! h0p://www.cs.utoronto.ca/~rsalakhu/ Lecture 7 Approximate

More information