Homeworks

Assignments are due by the start of lecture (2:59 pm) on the due date unless otherwise specified.

The handin bin for human-graded homework is located in the second floor of the CIT, at the top of the entrance stairs.

The stencil files for auto-graded homework can be downloaded from http://grading.codingthematrix.com/edition1/index.html. To handin, run cs053_submit <the stencil file>

Practice problems are to help your understanding and they will not be graded. Don't turn these in, but please do come to TA hours if you want to discuss them.

*You are expected to read sections related to the lecture.

# Homework Released Deadline Late Deadline Reading
1 hw09_06 Wednesday, 9/6 Friday, 9/8 Wednesday, 9/13 Chapter 0
2
  • Practice: Problems 0.8.1, 0.8.2, 0.8.3, 0.8.4
  • Human-graded: Problems 0.8.5, 0.8.6, 0.8.7, 0.8.9
  • Practice: Problems 1.7.4, 1.7.5, 1.7.6, 1.7.7, 1.7.8, 1.7.9
  • Auto-graded: 1.7.10, 1.7.13
 Friday, 9/8 Monday, 9/11 Friday, 9/15 *
3 hw09_11 Monday, 9/11 Wednesday, 9/13 Monday, 9/18 *
4
  • Auto-graded: 2.14.1, 2.14.2, 2.14.3, 2.14.4, 2.14.5
 Wednesday, 9/13 Friday, 9/15 Wednesday, 9/20 *
5
  • Auto-graded: 2.14.6, 2.14.7
  • Human-graded 2.14.8
  • Auto-graded: Part of Problem 2.14.10: the procedures getitem, setitem, and equal
 Friday, 9/15 Monday, 9/18 Friday, 9/22 *
6 hw09_18 Monday, 9/18 Wednesday, 9/20 Monday, 9/25 *
7 hw09_20 Wednesday, 9/20 Friday, 9/22 Wednesday, 9/27 *
8 hw09_22 Friday, 9/22 Monday, 9/25 Friday, 9/29 *
9 hw09_25 Monday, 9/25 Wednesday, 9/27 Monday, 10/2 *
10 hw09_27 Wednesday, 9/27 Friday, 9/29 Wednesday, 10/4 *
11
  • The_Matrix_problems.py: 4.17.5, 4.17.6
  • null_space.py: 4.7.4 (Section 4.7)
  • mat.py: the procedures transpose, matrix_vector_mul, matrix_matrix_mul
 Friday, 9/29 Monday, 10/2 Friday, 10/6 *
12
  • The_Matrix_problems.py: 4.17.7 and 4.17.9
 Monday, 10/2 Wednesday, 10/4 Monday, 10/9 *
13
  • Review sheet
instructions		 Wednesday, 10/4 Friday, 10/6 Wednesday, 10/11 *
14
  • Auto-graded: 4.17.11 and 4.17.22
  • Paper-handin: 4.17.23
 Friday, 10/6 Wednesday, 10/11 Friday, 10/13 *
15
  • Auto-graded (The_Basis_problems.py): 5.14.1, 5.14.3, 5.14.4
 Wednesday, 10/11 Friday, 10/13 Wednesday, 10/18 *
16
  • Auto-graded (The_Basis_problems.py): 5.14.5, 5.14.6, 5.14.7, 5.14.8
 Friday, 10/13 Monday, 10/16 Friday, 10/20 *
17
  • Auto-graded (The_Basis_problems.py): 5.14.13, 5.14.14, 5.14.15
 Monday, 10/16 Wednesday, 10/18 Monday, 10/23 *
18 Autograded (The_Basis_other_problems.py): Problems 1 and 2 of hw10-18.pdf Wednesday, 10/18 Friday, 10/20 Wednesday, 10/25 *
19 Monday, 10/23 Wednesday, 10/25 Monday, 10/30 *
20 Wednesday, 10/25 Friday, 10/27 Wednesday, 11/1 *
21
  • Auto-graded (Dimension_problems.py) 6.7.6, 6.7.7, 6.7.9, and 6.7.11
 Friday, 10/27 Monday, 10/30 Friday, 11/3 *
22
  • Auto-graded (Dimension_problems.py) 6.7.12 and 6.7.13
  • Auto-graded (Gaussian_Elimination_problems.py) 7.9.2, 7.9.3
 Monday, 10/30 Wednesday, 11/1 Monday, 11/6 *
23
  • Human-graded: 7.9.1
  • Human-graded: three questions about the material addressed in 11/1's quiz
  • Be prepared to prove "If M is invertible then Row MA = Row A" and "a basis for U unioned with a basis for V is a basis for the direct sum of U and V"
 Wednesday, 11/1 Friday, 11/3 Wednesday, 11/8 *
24 Friday, 11/3 Monday, 11/6 Friday, 11/10 *
25 Monday, 11/6 Wednesday, 11/8 Monday, 11/13 *
26
  • Autograded (The_Inner_Product_problems.py): 8.6.1, 8.6.2
  • Review 11/8's quiz & HW 11-06 (buttons)
 Wednesday, 11/8 Friday, 11/10 Wednesday, 11/15 *
27 Friday, 11/10 Monday, 11/13 Friday, 11/17 *
28 Monday, 11/13 Wednesday, 11/15 Monday, 11/20 *
29 Wednesday, 11/15 Friday, 11/17 Wednesday, 11/22 *
30
  • Auto-graded: 9.11.9, 9.11.10, 9.11.11
  • Stencil is Orthogonalization_problems.py
  • Make sure to use the support module aug_orthog not orthogonalization
 Friday, 11/17 Monday, 11/20 Friday, 11/24 *
31
  • Auto-graded (Orthogonalization_problems.py): 9.11.13, 9.11.14, 9.11.15
  • For 9.11.14, use procedure QR_factor(A) defined in Orthogonalization_problems.py (which uses your procedures aug_orthonormalize from Orthogonalization_problems.py and dict2list and list2dict from dictutil.py)
 Monday, 11/27 Wednesday, 11/29 Monday, 12/4 *
32
  • Auto-graded (The_SVD_problems.py): 11.8.4
  • Human-graded: update your review sheet with definitions, concepts, mathematical results. Be sure to include results on Gaussian elimination, projection onto/orthogonal to, orthogonalization, QR factorization, least squares using QR factorization, linear regression using least squares, the definition and use of SVD
 Friday, 12/1 Monday, 12/4 Friday, 12/8 *
33
  • Auto-graded (The_SVD_problems.py): 11.8.5
 Monday, 12/4 Wednesday, 12/6 Monday, 12/11 *
34
  • Auto-graded (The_Eigenvector_problems.py): 12.14.1, 12.14.2, 12.14.3
 Wednesday, 12/6 Friday, 12/8 Wednesday, 12/13 *
35
  • Auto-graded (The_Eigenvector_problems.py): 12.14.11
  • On paper: 12.14.6
  • On paper: 12.14.8 also on paper. You are welcome to use Python (including the solver module) to help you. Please include in your hand-in a printout of your interaction with Python. Your goal for this problem is to find an approximate eigenvector corresponding to the eigenvalue of smallest absolute value. Your method should be analogous to the power method but somehow using a procedure for solving a matrix-vector equation.
 Friday, 12/8 Monday, 12/11 Friday, 12/15 *