Johannes Brust (Instr.)Yifan Xiao (TA)Haoyu Zhang (TA)
jjbrust@ucsd.eduyix001@ucsd.eduhaz053@ucsd.edu
Office/student hrs:Office/student hrs:Office/student hrs:
M,W,F: 9-10a (APM 1111)M: 1:30-3:30p (APM 5748)R: 3-5p (HSS 4012)

Welcome to Math 170B (Lecture A, Spring 2023)

This page supplements materials to
``Introduction to Numerical Analysis: Approximation and Nonlinear Equations''

Like learned in Math 170A, numerical analysis is about computational methods. As such this class involves a valuable amount of numerical reasoning in connection with practical methods. One may use the materials to further learn Numerical Analysis in Math 170C or even as tools to numerically solve problems from research, industry and applications accross different subjects.

Because of this, algorithms and computer implementations are important components of the class. Examples are in [Matlab] (available for free to UCSD students), yet you are free to choose a language you like.

Memo to the student:

Remember, this class is here to help succeed in learning the subject.

  • Homework, lectures and textbook will help prepare for Quiz, Midterm and Final questions
  • •Being active in Lecture, Discussion and office hours will help to learn
  • •Ask for help when feeling stuck

Announcements

April 7 • Discord server, kindly provided by a classmate: [link]
Mar 31 • Lectures start Monday Apr. 3rd, 8am-8:50am HSS 1330

Syllabus

All in Pacific Time

Lectures: MWF, 8a-8:50a in HSS 1330

Discussions: M, CENTR 203
A01: 4p-4:50p
A02: 5p-5:50p
A03: 6p-6:50p
A04: 7p-7:50p

Schedule

(Note: Schedule and policies may be updated throughout the quarter)





Week Monday Tuesday Wednesday Thursday Friday
1
03 Apr
1.1: Basics
04 Apr 05 Apr
1.2: Order
06 Apr 07 Apr
2.1: Floats
HW 1
2
10 Apr
2.1: Floats II
11 Apr 12 Apr
2.2: Errors
13 Apr 14 Apr
3.1: Bisec. Mth.
HW 2
3
17 Apr
3.2: Newtn. Mth.
18 Apr 19 Apr
3.3: Secnt. Mth.
20 Apr
Quiz 1
21 Apr
3.4: Fxd. ptn.
HW 3
4
24 Apr
6.1: Poly. interp.
25 Apr 26 Apr
6.2: Div. diff.
27 Apr 28 Apr
6.3: Hermite interp.
HW 4
5
01 May
6.4: Spl. interp.
02 May 03 May
Review
04 May 05 May
Midterm (8a-8:50a)
6
08 May
6.5: B-Spl.
09 May 10 May
6.10: High dim. interp.
11 May 12 May
6.14: Adaptive approx.
HW 5
7
15 May
6.12: Trig. interp.
16 May 17 May
6.13: FFT
18 May
Quiz 2
19 May
11.1: 1 dim. optim.
HW 6
8
22 May
11.2: Descent Mth.
23 May 24 May
11.3: Anal. quad. obj.
25 May 26 May
11.4: Quad. fit. algs.
HW 7
9
29 May
Holiday (Memorial)
30 May 31 May
11.5: Nelder-Mead
01 Jun
Quiz 3
02 Jun
11.6: Sim. anneal.
HW 8
10
05 Jun
11.7: Genetic alg.
06 Jun 07 Jun
11.8: Convex prog.
08 Jun 09 Jun
Review
11
        16 Jun
Final exam (8a-11a)

Materials

Textbook:Numerical Analysis: Mathematics of Scientific Computing (3rd ed.),
D. Kincaid and W. Cheney
Content:We will cover relevant parts of chapters 1,2,3,6 and 11
Homework:We will use 8 HW sets (to learn and practice the subject)
Assessment: 8 Homework, 3 Quizzes, 1 Midterm, and 1 Final exam
Quizzes available for 24 hours via Canvas
Midterm and Final exams are in person
Quiz 1 (start: R. Apr. 20, 12pm -- end: F. Apr. 21, 12pm),
(Content: Secs. 1.1,1.2,2.1,2.2)
Midterm (F. May 5, 8am -- 8:50am, HSS 1330),
(Content: all sections covered)
Quiz 2 (start: R. May 18, 12pm -- end: F. May 19, 12pm),
(Content: Secs. 6.5,6.10,6.14,6.12,6.13)
Quiz 3 (start: R. Jun. 1, 12pm -- end: F. Jun. 2, 12pm),
(Content: Secs. 11.1,11.2,11.3,11.4,11.5)
Final (F. Jun. 16, 8:00a-10:59a),
(Content: comprehensive)

Grading

Weighted final scores from the best of two approaches:

30% Homework + 25% Quizzes + 45% Midterm & FinalOR
30% Homework + 15% Best Quiz + 55% Midterm & Final

Letter grades from weighted final scores and the best of two options

  • (Option A):
  •  A+   A   A-   B+   B   B-   C+   C   C-   D   F 
     97   93   90   87   83   80   77   73   70   60   60> 

  • (Option B):
  • A curve where the median corresponds to B-/C+

Resources

Weblinks:

Canvas (Course page)
Gradescope (Examination system)
Matlab (Computing software)


Homework:

The homwork and solutions are uploaded via Canvas.

(Due/ Sections)
Homework 1 (due Apr. 7, Secs. 1.1,1.2), [Homework 1]
Homework 2 (due Apr. 14, Secs. 2.1,2.2,2.3)
Homework 3 (due Apr. 21, Secs. 3.1,3.2,3.3)
Homework 4 (due Apr. 28, Secs. 3.4,6.1,6.2)
Homework 5 (due May 12, Secs. 6.5,6.10)
Homework 6 (due May 19, Secs. 6.14,6.12,6.13)
Homework 7 (due May 26, Secs. 11.1,11.2,11.3)
Homework 8 (due Jun. 2, Secs. 11.4,11.5)


Instructions (homework):

• Total of 8 HW sets. Cumulative HW grade based on the best 7 out of 8.
HW 1 -- 8 submitted to Gradescope by Friday 11:00 pm Pacific Time.
(Note: To be prepared for unforeseen technical difficulties, we will accept homework submitted within 24 hours from the due date, i.e., Saturday 11:00 pm, without a penalty.)
• In view of the above arrangement, NO late homework will be accepted.
• You can work with classmates, but need to write down your own version. Copying solutions from others is not accepted and is considered cheating.
• Include an brief explanation of how a method works and an image (screenshot) of the code and results for programming problems.


Notes:

Please notice that outside factors, including the need for a certain grade for admission/retention in any academic program, scholarship or transfer credit, graduation requirements or personal desire for a specific grade DO NOT appear in the determination of course grades. Effort, improvement, class attendance and participation will all dramatically improve your grade in the course in that they will enable you to learn the materials. They will NOT, however, actively participate in the calculation of course grades.
Remember that your instructor or TA are there for you if you need help in learning the course content.

Accommodations: Students requesting accommodations and services due to a disability for this course are asked to provide a current Authorization for Accommodation (AFA) letter issued by the Office for Students with Disabilities (OSD), prior to eligibility for requests. Receipt of AFAs in advance is necessary for appropriate planning for the provision of reasonable accommodations. OSD Academic Liaisons also need to receive current AFA letters. Students can find department-specific information on exam accommodations on the following Math Department webpage: http://www.math.ucsd.edu/programs/undergraduate/exam_accommodations.php

    Academic Dishonesty:  Academic dishonesty is considered a serious offense at UCSD.  Students caught cheating will face an administrative sanction which may include suspension or expulsion from the university.  It is in the student's very best interest to maintain academic integrity. (Click here for more information.)