MATH 415 P13/Q13 SPRING 2012

HW is collected on Thursdays at the beginning of class.
HW can also be dropped in the box outside my office before Friday 11:30 am.

Week 1 January 17, 19
Tu in class we did problems (1.1) 27, 37 and (1.2) 29. 61 HW1 : (1.1) 2, 4, 7, 29 due Th 19
Th we did matrices in RREF and elementary row operations (resp. top and bottom of page 16) and two ways to evaluate a product Ax (top page 29 and bottom page 30) HW2 : (1.1) 18, 23 (1.2) 2, 4, 18, 22 (1.3) 14, 36, 58 due Th 26
Week 2Janunary 24, 26
Tu we did problems (1.1) 17 (1.2) 46 (1.3) 37 and defined linear transformations (Defn 2.1.1 and Thm 2.1.3)
Th we pointed out the connection between composition of linear transformations and matrix products (L after T has matrix BA - Defn 2.3.1 / Thm 2.3.4) and we used row reduction techniques from Chapter 1 to find the inverse of a matrix (pages 81 and 82)HW3 : (2.1) 6, 8, 44, 52 (2.3) 4, 20 (2.4) 12, 18 due Th 2
Week 3 February 31, 2
Tu we showed that projections on a line or a plane are linear transformations, we found a short formula for projection on a line (x goes to kw where k = (x.w)/(w.w)), and we derived in three different ways the matrix A for a projection (for this part use notes from class rather than section (2.2) from the book). This completes Chapter 2 Notes from class
Th we went through sections (3.1), (3.2), including important definitions: the image im(T) and kernel ker(T), linear combinations and span, linearly independent / linearly dependent and basis, and the Theorems 3.1.3, 3.1.4, 3.1.6, 3.2.2HW4 : (3.1) 10, 16, 30, 34, 42, 50, 52 (3.2) 2, 6, 8, 18, 32 due Th 9
Week 4 February 7, 9
Tu Section (3.3) dimension of a vector space, how to find the image and kernel for a given matrix A
Th Section (3.4) matrix of a linear transformation wrt a given basisHW5 : (3.3) 2, 6, 24, 26, 30, 38 (3.4) 8, 10, 26, 40, 56 due Th 16
Week 5 February 14, 16
Tu Sections (4.1) (4.2) Definition of linear spaces and examples (R^n, P_2, R^{2x2}), linear transformations T: V --> W
Th Section (4.3) Matrices A and B of a linear transformation wrt different bases (a1,...,an) and (b1,...,bn) and their relation (AS = SB)Example
Week 6 February 21, 23
FIRST MIDTERM FEB 21 (IN CLASS)Exam1-test
Ch.5 (5.1) Definitions: inner product, orthogonal, norm. Theorem: Cauchy-Schwartz inequality, we gave two different proofsHW6 : (5.1) 12, 40-46 (5.2) 8, 22
Week 7 February 28, 1
Ch.5 (5.2) Gram-Schmidt Process, QR Factorization
Ch.5 Projection on span(v1,...,vn) (the book considers in Theorem 5.1.5 the special case that v1,...,vn are orthonormal, in class we discussed the general case where v1,...,vn are arbitrary vectors), (5.3) A matrix A is orthogonal if A^T A = I, (5.4) Defn 5.4.4, Thms 5.4.5 - 5.4.6 - 5.4.7 (in class we discussed the relation between Ax=b and the projection of b on im A) HW7
Week 8 March 6, 8
Ch.5 (5.4) A(A^T A)^-1 A^T becomes Q Q^T for A=QR with Q orthogonal
Ch.6 (6.2) The determinant is the unique map A -> det(A) that maps I -> 1 and such that after (1) multiplication of a row of A by k (2) swapping of two rows or (3) adding a multiple of a row to another row det(A) becomes (1) k det(A) (2) - det(A) or (3) det(A), respectively. From these properties we immediately obtain : (6.2.4) det(A) not 0 if and only if A is invertible, (6.2.10) the cofactor expansion of the determinant, (6.3.8) Cramer's rule and (6.3.9) the inverse of a matrix A^{-1} = adj(A) / det(A) HW8: (6.2) 6, 12, 14, 18, 26, 30, 50 (6.3) 22, 30
Week 9 March 13, 15
Ch.6 Computation of A^{-1} in three steps: matrix M of minors, cofactor matrix C and adjoint matrix adj(A). Start of Ch.7: Coyotes and roadrunners example, Cases 1, 2, 3, Figure 6
Ch.7 (7.1) Defn 7.1.1 - Eigenvectors and eigenvalues. (7.2) Thms 7.2.1, 7.2.5 - Characteristic equation, Characteristic polynomial, Thm 7.2.2 - Eigenvalues of a triangular matrix, Defn 7.2.6 - Algebraic multiplicity of an eigenvalue, (7.3) Defns 7.3.1, 7.3.2, 7.3.3 - Eigenspace, Geometric multiplicity of an eigenvalue, Eigenbasis, Thm 7.3.5 - A matrix with n distinct eigenvalues has an eigenbasis. If the columns of S form an eigenbasis for A then AS = SB for a diagonal matrix B (as in Section 3.4, page 145) HW9: (7.1) 6, 8, 10, 34, 46, 50 (7.2) 38
Week 10 March 20, 22
Spring Break
Spring Break
Week 11 March 27, 29
Ch.5 (5.5) Using defn 5.5.1 (Inner product for vector spaces other than R^n) most of the material in previous sections generalizes: norm, orthogonality, orthogonal projection, and least square approximation. Important vector spaces: C[a,b], continuous functions on the interval [a,b] with inner product given by the integral of f(x)g(x) from x=a to x=b.
Ch.7 (7.4) A is diagonalizable if it is similar to a diagonal matrix (if S^{-1}AS=D for some S)
Week 12 April 3, 5
SECOND MIDTERM APR 3 (IN CLASS)Exam2-test
Ch.7 (7.5) Dynamical systems with complex eigenvalues, glucose-insulin example, back to (2.2) - Interpretation of the 2x2 matrix [[a,-b],[b,a]] as rotation followed by scaling (thm 2.2.4)HW10: (7.3) 36 (7.4) 32, 36 (7.5) 14 (hint: thm 7.5.3) - accepted till Monday 4/16 when handed in before 11:30 in the box outside my office
Week 13 April 10, 12
Ch.7 (7.6) Dynamical systems with complex eigenvalues (thm 7.6.3)
No classHW11: (9.1) 26, 30 (9.2) 22-26, 32 - due 4/19 in class, or not later than 11:30 am 4/20 in the box outside my office
Week 14 April 17, 19
Ch.9 (9.1) Continuous dynamical systems, case of real eigenvalues (thm 9.1.3, compare with them 7.1.3)
Ch.9 (9.2) id., case of complex eigenvalues (thm 9.2.3, compare with thm 7.6.3)HW12: (8.1) 6, 8, 10 (8.3) 14 - due 4/26 in class, or not later than 11:30 am 4/27 in the box outside my office
Week 15 April 24, 26
Ch.8 (8.1) Properties of symmetric matrices (thms 8.1.1, 8.1.2, 8.1.3) (8.2) Quadratic form, principal axes (defns 8.2.1, 8.2.6)
Ch.8 (8.3) Singular value decomposition (thm 8.3.5)
Week 16 May 31-4
Last day of class Tuesday May 1
Question hours (tentatively): Friday May 4 at 4, Sunday May 6 at 2, Wednesday May 9 at 4
Week 17 May 7-11
Final Exam P13 (TR 11:00-12:20) Monday May 7, 1:30 - 4:30 pm
Final Exam Q13 (TR 12:30-1:50) Thursday May 10, 8:00 - 11:00 am