Linear algbra

1. Dan Witzner Hansen Email: witzner@itu.dk Linear algbra

2. Last week?

3. Groups? Improvements – what is missing? Misc

4. The goal is to be able to solve linear equations • Continue with linear algebra • Linear mappings • Basis vectors & independence • Solving linear equations & Determinants • Inverse & Least squares • SVD • Lot’s of stuff. Don’t despair – you will be greatly rewarded in the future Today

5. Linear systems

6. What is a linear equation? • A linear equation is an equation of the form, anxn+ an-1xn-1+ . . . + a1x1 = b.

7. What is a system of linear equations? • A system of linear equations is simply a set of linear equations. i.e. a1,1x1+ a1,2x2+ . . . + a1,nxn = b1 a2,1x1+ a2,2x2+ . . . + a2,nxn = b2 . . . am,1x1+ am,2x2+ . . . + am,nxn = bm

8. Matrix Form of Linear System Compact notation Ax=b

9. Linear Mappings Affine mapping

10. Species 1: eats 5 units of A and 3 of B. Species 2: eats 2 units of A and 4 of B. Everyday a total of 900 units of A and 960 units of B are eaten. How many animals of each species are there? Example Species

11. Example:

12. Matlab code A = [5 2; 3 4]; b = [900 960]; x = linspace(0,150,100); y1 = (-A(1,1)*x+b(1))/A(1,2); %made for clarity y2 = (-A(2,1)*x+b(2))/A(2,2); Plot(x,y1,'r-','LineWidth',3); hold on Plot(x,y2,'b-','LineWidth',3); hold off title('Linear equations and their solution')

13. Basis, Independence and subspaces

14. Subspaces Independent vectors Basis vectors / Orthonomal basis An now for some formalism

15. A subspaceis a vector space contained in another vector space Subspaces

16. Independent vectors Can it happen that y=0 if x is nonzero? • If y is non-zero for all non-zero x, then the column vectors of A are said to be linear independent. • These vectors form a set of basis vectors • Orthonormal basis when the vectors are unit size and orthogonal.

17. Basis vectors - example Change of basis

18. Show that it is an Orthonormal basis

19. What happens with this one? A = [1 4 2;2 8 6; 3 124]; [X,Y,Z] = meshgrid(-10:10,-10:10,-10:10); x = [X(:),Y(:),Z(:)]’; p = A*x; plot3(p(1,:),p(2,:),p(3,:),'rx')

20. A closer look at matrix multiplication

21. 2D Example

22. What happens with this one? 4x =

23. Solving linear equations

24. A solution to a system of equations is simply an assignment of values to the variables that satisfies (is a solution to) all of the equations in the system. If a system of equations has at least one solution, we say it is consistent. If a system does not have any solutions we say that it is inconsistent. Solutions of linear equations

25. Recall Solution

26. Solving systems algebraically Which solution(s)? Can we always do this? How many solutions are there?

27. For A (2x2 matrix) • When det A ≠0 a unique solution exists (nonsingular) • When det A =0 the matrix is singular (lines same slope) and are therefore the columns are linear dependent • Coincident (infinitely many solutions) • Parallel (no solutions) • Determinant can be used when solving linear equations (Cramers’ rule), but not useful in practice Determinant >>det(A)

28. What to do when the dimension and the number of data points is large? How many data points are needed to solve for the unknown parameters in x? What if?

29. Solve simple linear equation Matrix inverse: A (unique) inverse exist if det(A) ≠ 0 (NxN matrices) Matlab: >>invA =inv(A) Matrix Inverse

30. Solving Linear Systems • If m=n (A is a square matrix & Det(A)!=0), then we can obtain the solution by simple inversion (: • If m>n, then the system is over-constrained and Ais not invertible • If n>m then under constrained.

31. Solve Ax=b (notice multiply from right): Matrix inverse example

32. Don’tuse for solvingthe linear system. It is mostlymeant for notationalconvenience. It is faster and more accurate (numerically) to write (solve)x=A\bthaninv(A)*b: Notice: implementation

33. Diagonal matrices Orthogonal matrices Simple inversion of (some) matrices

34. Fitting Lines • A 2-D point x = (x, y) is on a line with slope m and intercept b if and only if y =mx + b • Equivalently, • So the line defined by two points x1, x2 is the solution to the following system of equations:

35. Example: Fitting a Line • Suppose we have points (2, 1), (5, 2), (7, 3), and (8, 3)?????

36. With more than two points, there is no guarantee that they will all be on the same line Fitting Lines courtesy of Vanderbilt U.

37. Least squares Objective: Find the vector Fx in the column range of F, which is closest to the right-hand side vector y. The residual r=y-Fx

38. Fitting Lines Solution: Use the pseudoinverse A+ =(ATA)-1AT to obtain least-squares solutionx=A+b courtesy of Vanderbilt U.

39. and x=A+b=(0.3571, 0.2857)T Example: Fitting a Line • Suppose we have points (2, 1), (5, 2), (7, 3), and (8, 3) • Then????

40. Example: Fitting a Line (2, 1), (5, 2), (7, 3), and (8, 3)

41. Homogeneous Systems of Equations • Suppose we want to solve Ax = 0 • There is a trivial solution x = 0, but we don’t want this. For what other values of x is Ax close to 0? • This is satisfied by computing the singular value decomposition (SVD) A = UDVT (a non-negative diagonal matrix between two orthogonal matrices) and taking x as the last column of V • In Matlab[U, D, V] = svd(A)

42. Singular Value Decomposition

43. Null space & Image space

44. When the columns of A =UDV are independent then all Tells how close to singular A is. Inverse and pseudoinverse The columns of U corresponding to nonzeros singular values span the range of A, the columns of V corresponding to zero singular values the nullspace. Properties of SVD

45. example: Line-Fitting as a Homogeneous System A 2-D homogeneous point x = (x, y, 1)T is on the line l = (a, b, c)T only when ax+ by + c = 0 We can write this equation with a dot product: x.l= 0,and hence the following system is implied for multiple points x1, x2, ..., xn:

46. Example: Homogeneous Line-Fitting Again we have 4 points, but now in homogeneous form: (2, 1, 1), (5, 2, 1), (7, 3, 1), and (8, 3, 1) • The system of equations is: • Taking the SVD of A, we get: compare tox =(0.3571, 0.2857)T

47. Robust methods • So what about outliers • Other metrics such as other norms • More about this later

48. Next week