Rating: **4.4**/5.0 (17 Votes)

Category: Homework

C lass *announcements* will accumulate here over the term. Be certain to check here regularly.

Also, be certain to *refresh* this page via your browser when you visit to ensure that you are not looking at an old, cached copy. Otherwise, you can miss the latest message.

Marks will accumulate on *ePost* (for if you have an EECS account).

Tutorial 6-8pm Thursday 17 December in #3033 Lassonde Builing.

Tutorial 6-8pm Thursday 10 December in #3033 Lassonde Builing.

Final exam is 7:00pm–10:00pm Tuesday 22 December at TC VIVA.

Tutorial 4-6pm Friday 20 November in #3033 Lassonde Builing.

*Test #2* is the first half of lecture time on Monday 23 November.

It is closed notes, closed books. It covers the material from §2.4 & 2.5, §3.1–3.3, §5.1–5.3, and what we cover in class on *Monday 16 November*. which was §6.1 & 6.3, as in the textbook and as we covered in lecture.

Problems will be very similar to those in the book and in the assignments. See these “test #1's” from previous terms as examples.

Of course, each of the tests from above from different terms had slightly different coverage of material. So only pay attention to those questions that are from the *sections* that our test #2 covers.

*Test #1* is the first half of lecture time on Monday 19 October.

It is closed notes, closed books. It covers the material from §1.1–1.8 and §2.1–2.3 as in the textbook and as we covered in lecture. (Know just the basic definitions for functions from §2.3; the test will not do into depth on this, as we covered it briefly.)

Problems will be very similar to those in the book and in the assignments. See these “test #1's” from previous terms as examples.

The Lassonde Student Government offers free tutoring via Excellassonde.

Tutorials (i.e. review sessions) start this week on 6-8pm Thursday 1 October. They will be weekly each week ending with 6-8pm Thursday 3 December. They will take place in CFA #312. They are not obligatory.

Apologies. No tutorial for Thursday night 24 September; I could not get a room scheduled for it.

We should be scheduled for next week onward, though, so will start with a tutorial on 6-8pm Thursday on 1 October.

Welcome to the class!

A ssignments *A-1* & *A-2* must be done online. You need to register for a free trial of *Connect* and submit the homeworks electronically. McGraw-Hill Connect is a learning tool that allows, among other things, automatic homework submission and evaluation. You will find the instructions on where to find Connect here. Make sure you register for appropriate section. When you register for *Connect* . remember to include your first and last name *exactly* as they appear in York's records; otherwise, we will not be able to identify your homework, and you will not receive credit for it.

*McGraw Hill Connect Notes for our class*. PDF. PPT.

For Assignments *A-3* through *A-10*. you may either keep submitting homeworks online via *Connect* (given you decided to purchase it), *or* you may submit them in hardcopy.

On *Connect* . *§1.1*. 2a–f, 12a–f, 24a–h (choose 10 of 20, 2pt each) *§1.2*. 10, 16 (5pt each) *§1.3*. 6, 8a–d, 10a–d, 30, 34a–c (choose 10 of 13, 2pt each)

Due before lecture on Monday 28 Sept

A1 (PDF)

On *Connect* . *§1.4*. 8a–d, 20a–e, 24a–e (2pt each) *§1.5*. 6a–f (2pt each)

Due before lecture on Monday 28 Sept

A2 (PDF)

*§1.6*. 8, 14, & 24 *§1.7*. 6, 8, 16, 24, & 26 *§1.8*. 4 & 14 *Each will be graded out of 4 marks: 40pt total.*

Due before lecture on Monday 5 Oct

A3 (PDF)

Miguel �. Carreira-Perpi��n

Professor

Electrical Engineering and Computer Science

School of Engineering

University of California, Merced

mcarreira-perpinan-[at]-ucmerced.edu; 209-2284545

Office: 217, Science & Engineering Building 2

Office hours: Tuesdays 2:45-3:45pm (SE2-217).

Lectures: Mondays/Wednesdays 4:30-5:45pm (COB2-264).

Lab class: Thursdays 7:30-10:20pm (Linux Lab, SE1-138).

Course web page: http://faculty.ucmerced.edu/mcarreira-perpinan/EECS260

Optimization problems arise in multiple areas of science, engineering and business. This course introduces theory and numerical methods for continuous multivariate optimization (constrained and unconstrained), including: line-search and trust-region strategies; conjugate-gradient, Newton, quasi-Newton and large-scale methods; linear programming; quadratic programming; penalty and augmented Lagrangian methods; sequential quadratic programming; and interior-point methods. The primary programming tool for this course is Matlab.

Prerequisites: MATH 23, MATH 24, MATH 141 or equivalent (undergraduate courses in linear algebra and multivariate calculus), MATH 131 (numerical analysis I). Basic concepts will be briefly reviewed during the course.

- Jorge Nocedal and Stephen J. Wright:
*Numerical Optimization*. second ed. Springer-Verlag, 2006. This book is accessible online from within UC Merced.

Other recommended books:

- R. Fletcher:
*Practical Methods of Optimization*. Wiley, 1987. - David G. Luenberger and Yinyu Ye:
*Linear and Nonlinear Programming*. 3rd ed. Springer, 2008. Available online from within UC Merced. - Stephen Boyd and Lieven Vandenberghe:
*Convex Optimization*. Cambridge University Press, 2004. Available online.

If you want to refresh your knowledge of linear algebra and multivariate calculus, the following are helpful (any edition or similar book is fine):

- Seymour Lipschutz:
*Schaum's Outline of Linear Algebra*. McGraw-Hill. - Murray Spiegel:
*Schaum's Outline of Vector Analysis*. McGraw-Hill. - Frank Ayres:
*Schaum's Outline of Matrices*. McGraw-Hill. - Richard Bronson:
*Schaum's Outline of Matrix Operations*. McGraw-Hill. - Erwin Kreyszig:
*Advanced Engineering Mathematics*. Wiley. - Gilbert Strang:
*Introduction to Linear Algebra*. Wellesley-Cambridge Press. - Gilbert Strang:
*Linear Algebra and Its Applications*. Brooks Cole.

Before each class, you should have read the corresponding part of the textbook and the notes. I will teach the material in the order below (which is more or less the order in the book).

- Introduction (ch. 1: all) and math review (appendix A: skim it)
- Unconstrained optimization:
- Fundamentals of unconstrained optimization (ch. 2: all)
- Line search methods (ch. 3: pp. 30-49; skip proofs in sec. 3.3)
- Trust-region methods (ch. 4: pp. 66-71)
- Conjugate gradient methods (ch. 5: all, but skim pp. 113-114, 119-120, 125-133)
- Quasi-Newton methods (ch. 6: pp. 135-146; skip 147-152; skim rest)
- Large-scale unconstrained optimization (ch. 7: pp. 164-170, 176-181, 185-190; skip 171-175, 182-184)
- Calculating derivatives (ch. 8: pp. 193-206; skim 199-201, 203; skip rest)
- Derivative-free optimization (ch. 9: all except pp. 226-229, 237)
- Least-squares problems (ch. 10: all but skim pp. 251-252, 259-268)
- Nonlinear equations (ch. 11: all but skim 277-279, 287-295; skip 283-284)

- Constrained optimization:
- Theory of constrained optimization (ch. 12: all except as follows: from section 12.2 see defs. 12.3, 12.4 and skip the rest; from section 12.5 skip the proofs of second-order conditions; skip sections 12.4, 12.6, 12.7)
- Linear programming: the simplex method (ch. 13: all except as follows: skip sections 13.4 to 13.7)
- Linear programming: interior-point methods (ch. 14: all except as follows: skip lemmas 14.1, 14.2 and the proof of th. 14.3; skim pp. 410, 412-413; skip section 14.3)
- Fundamentals of algorithms for nonlinear constrained optimization (ch. 15: pp. 421-437)
- Quadratic programming (ch. 16: all except as follows: skip sections 16.2-3, skim section 16.5 and skim pp. 482-485)
- Penalty and augmented Lagrangian methods (ch. 17: all except as follows: skip def. 17.1, skim section 17.4)
- Sequential quadratic programming (ch. 18: sections 18.1-2; skip rest)
- Interior-point methods for nonlinear programming (ch. 19: sections 19.1, 19.2 and 19.6; skip rest)

- Some Matlab code to plot contours of functions, steepest descent, backtracking line search, convergence order estimation, numerical gradient and Hessian, etc.
- The notes to accompany the textbook (bring the corresponding part to each class):

Carreira-Perpi��n, M. �. (2015): EECS260 Optimization: Lecture notes. University of California, Merced, 2005-2015. - Homework (to do on your own, not graded):
- Homework #1 (covering chapters 1-4 and A from the book)
- Homework #2 (covering chapters 5-11 from the book)
- Homework #3 (covering chapters 12-15 from the book)
- Homework #4 (covering chapters 16-19 from the book)

- Projects (to be submitted and graded):
- Project #1 (implementing unconstrained optimization methods)
*due Oct. 16*. in groups of 2 students - Project #2 (implementing a constrained optimization method)
*due Nov. 28*. in groups of 2 students - Project #3 (presenting and discussing several papers in class), individual (no groups)

For projects done in groups, briefly describe in the report what each member of the group did. Note that each member should understand the whole project. Project 2 will build on your project 1 solution.

**Course grading**The course grading will be based on three projects and a final exam, as follows (but note that too low a grade in the exams cannot be compensated by a high grade in the projects or vice versa):

- Project 1 (15%): implementing a method for unconstrained optimization problems
- Project 2 (25%): implementing a method for constrained optimization problems
- Project 3 (10%): discussing research papers related to optimization
- Midterm exam (25%): in-class, closed-book, consisting of problems and conceptual questions
- Final exam (25%): as the midterm

While I encourage you to discuss your work with other students, the projects and the exam must be the result of your own work without collaboration.

I will also give homework exercises (mainly from the textbook) of two types, pencil-and-paper and Matlab programming. I will not ask you to solve them, i.e. they will not count towards your grade. I will give the solutions and solve some of the exercises in class.

*However, I strongly recommend that you try to solve all the exercises on your own.***Optimization links****Matlab tutorials**If you have never used Matlab. there are many online tutorials, for example:

- Project #1 (implementing unconstrained optimization methods)

EECS E6892 Topics in Information Processing

Bayesian Models for Machine Learning

Columbia University, Spring 2014

Instructor: John Paisley

Location: 403 International Affairs Building

Time: Thursday 4:10-6:40

Office hours: Monday 11-12, Shapiro CEPSR 712

TA: Dawen Liang, dl2771@columbia.edu

Synopsis: This course provides an introduction to Bayesian approaches to machine learning. Topics will include mixed-membership models, latent factor models and Bayesian nonparametric methods. We will also focus on mean-field variational Bayesian inference, an optimization-based approach to approximate posterior learning. Applications of these methods include image processing, topic modeling, collaborative filtering and recommendation systems. We will discuss a selection of these in class.

Text: There is no required text. There will be suggested readings from the literature, and much of the material will come from the following two textbooks:

Christopher Bishop, Pattern Recognition and Machine Learning

David MacKay, Information Theory, Inference, and Learning Algorithms (can be found online for free)

Grading: 4 homeworks (15% each), final project (40%, write-up and short presentation)

Probability review, Bayes rule, conjugate priors, exponential family

Bishop Ch. 1.2, 2.1-2.4

MacKay Ch. 2, 3, 23

Bayesian approaches to regression and classification

Bishop Ch. 1.5, 3.1-3.4, 4

MacKay Ch. 27

Homework 1 (Due Feb. 13)

Hierarchical models, matrix factorization, sparse regression models, sampling

EM algorithm, mixture models, factor models

Bishop Ch. 9, 12.1-12.2

MacKay Ch. 22

Homework 2 (Due Feb. 27)

Variational inference I, mixture of Gaussians

Bishop Ch. 10.1-10.3

MacKay Ch. 33

Variational inference II, conjugate exponential models, latent Dirichlet allocation

Homework 3 (Due March 13)

Variational inference III, approximations for non-conjugate models

Variational inference IV, inference for big data sets

No class (spring break)

Bayesian nonparameterics I, Dirichlet process and Chinese restaurant process

Homework 4 (Due April 17)

Bayesian nonparameterics II, Dirichlet process and stick-breaking

Bayesian nonparameterics III, Gaussian process

Bayesian nonparameterics IV, beta process and sparse latent factor models

Project presentations I & II (Shapiro CEPSR 414)

Project write-up due May 5

If you do not agree with the grade for a homework assignment, please email the TA who graded it, and set up an appointment to see him or her.

Since answers are provided for homework, the homework grade depends on you showing HOW you obtained the solution.

Please underline your last name on front page, and each answer.

� 8/26: Section 1.1 (page 9), 1,6 (only y_{2} ), 15, 22, 33. Due 8/28. Grader: Garcia. Solution.

� 8/28: Section 1.2 (page 17-18), 5, 9, 16, 21, 25 (acceleration should be dv/dt rather than dy/dx), 32. Due 8/30. Grader: Tian. Solution .

� 8/30: Section 1.3 (pages 25-26), 4, 16, Due 9/4. Grader Garcia. Solution.

� 9/4: Section 1.4 (page 40), 7, 9, 25, 29. Due 9/6. Grader Garcia. Solution.

� 9/6: Section 1.4 (page 41), 31, 39, 44, 47. Due 9/9. Grader Tian. Solution.

� 9/9: Section 1.5 (page 51-2), 5, 7, 13, 23. Due 9/11. Grader Garcia. Solution.

� 9/11: Section 1.5 (page 52), 27, 33, 41. Due 9/13. Solution. Grader Garcia

� 9/13: Section 1.7 (page 79) 3, 5, 7, 11, 13. Due 9/16. Grader Tian. Solution

� 9/16: Section 1.8( pages 89-91) 2, 7, 20, 28. Due 9/18. Grader Garcia. Solution.

� 9/23: Section 2.1 (pages 106-107) 3, 6, 17, 29. Due 9/25. Solution. Grader Tian.

� 9/25: Section 2.1 (page 107) 20, 21, 33, 39. Section 2.2 (page 119) 5. Due 9/27. Solution. Grader Tian.

� 9/27: Section 2.2 (pages 119-120) 9, 11, 21, 30, 33. Due 9/30. Solution. Grader Tian.

� 9/30: Section 2.3 (page 131): 1, 9, 21, 32. Due 10/2. Solution. Grader Tian.

� 10/2: Section 2.3 (page 131) 46, 47, 48. Due 10/4. Solution. Grader Tian.

� 10/4: Section 2.4 (page 140) 3, 8, 10, 14, 23, 24, 34. Due 10/7. Solution. Grader Tian.

� 10/7: Section 2.5 (pages 156-157): 3, 7, 17, 21, 26, 34. Due 10/9. Solution. Grader Tian.

� 10/11: Section 2.6 (pages 167-168): 4, 6, 7, 14. Due 10/14. Solution. Grader Tian.

� 10/14: Section 2.6 (pages 168-169): 17, 25, 30. Due 10/16. Solution. Grader Tian.

� 10/16: Section 2.7 (pages 176-7): 1, 3, 7, 11, 17. Due 10/18. Solution. Grader Tian.

� 10/18: Section 2.8 (page189): 3,7. Due 10/21. Solution. Grader Tian.

� 10/25: Section 4.1 (pages 272-3): 5, 7, 11, 19. Due 10/28. Solution. Grader Garcia.

� 10/28: Section 4.1 (page 273): 25, 29, 35, 37. Due 10/30. Solution. Grader Tian.

� 10/30: Section 4.2 (page 282): 1, 5, 9, 27. Due 11/1. Solution. Grader Garcia.

� 11/1: Section 4.3 (page 291): 1,3,7,9. Due 11/4. Solution. Grader Tian.

� 11/4: Section 4.3 (page 291): 13, 17, 27, 31. Due 11/6. Solution. Grader Tian

� 11/6: Section 4.4: (p. 299): 3, 10, 11. Due 11/8. Solution. Grader Garcia.

� 11/8: Section 4.4: (p. 299): 16, 21, 23, 26. Due 11/13. Solution. Grader Tian.

� 11/13: Section 4.5 (p. 311): 3, 5, 9, 11, 13. Due 11/18. Solution. Grader Garcia.

� 11/18: Section 4.5 (p. 311) 21, 31, 33, 39. Due 11/20. Solution. Grader Tian.

� 11/20: Section 4.6 (p. 322) 3, 5, 7, 11. Due 11/22. Solution. Grader Tian.

� 11/22: Section 4.6 (p. 322) 14, 15. Due 11/25. Solution. Grader Garcia.

� 11/25: Section 3.1: (p. 204) 5, 7, 13, 21, 24. Due 12/6. Solution. Grader Tian.

� 12/6: Section 3.2 (p. 215) 1, 3, 7, 23, 27. Due 12/11. Solution. Grader Garcia.

Homeworks will be submitted either electronically or via hardcopy using the drop box labelled "CS 161" in 283 Soda, as stated in the assignment. Homework solutions must be legible; we may mark off for difficult-to-read solutions, or even refrain from grading them entirely. When submitting hardcopy, you must print your name, your class account name (e.g. cs161-xy), your TA's name, the discussion section time where you want to pick up the homework, and the homework number prominently on the first page. Staple all pages together. *You risk receiving no credit for any homework lacking this information!**No late homeworks accepted.*

There will be 2 course projects. We will penalize late project submissions as follows: less than 24 hours late, you lose 10%; less than 48 hours late, you lose 20%; less than 72 hours late, you lose 40%; at or after 72 hours, late submissions *no longer accepted*. (There are no "slip days".)

Note that this late policy applies only to projects, not homeworks (which cannot be turned in late).

There will be one midterm and one final exam.

The midterm will be given on- Solutions for blue and gold versions of the midterm.

- Final with solutions.

- Homeworks: 20%
- Projects: 30%
- Midterm: 20%
- Final exam: 30%

*Contact information:* If you have a question, the best way to contact us is via the class Piazza site. The staff (instructors and TAs) will check the site regularly, and if you use it, other students will be able to help you too. *Please avoid posting answers to homework questions before the homework is due.*

If your question is personal or not of interest to other students, send email to `vern@eecs.berkeley.edu`. or to one of the TAs if you prefer. If you wish to talk with one of us individually in person, you are welcome to come to our office hours. If the office hours are not convenient, you can make an appointment with any of us by email. Please reserve email for the questions you can't get answered in office hours, in discussion sections, or through the newsgroup.

*Announcements:* The instructors and TAs will periodically post announcements, clarifications, etc. to the Piazza site. Hence it is important that you check the it reguarly throughout the semester.

*Prerequisites:* The prerequisites for CS 161 are CS 61B, CS61C, and either CS70 or Math 55. *We assume basic knowledge of both Java and C.* You will need to have a basic familiarity using Unix systems.

*Collaboration:* Assignments will specify whether they must be done on your own or may be done in groups. Either way, *you must write up your solutions entirely on your own*. You must *never* read or copy the solutions of other students, and you must not share your own solutions with other students. You may use books or online resources to help solve homework problems, but you *must always credit all such sources* in your writeup and you must never copy material verbatim. Not only is this good scholarly conduct, it also protects you from accusations of theft of your colleagues' ideas. You must not receive specific help on homework assignments from students who have taken the course in previous years, and you must not review homework solutions from previous years.

We believe that most students can distinguish between helping other students understand course material and cheating. Explaining a subtle point from lecture or discussing course topics is an interaction that we encourage, but you should never read another student's homework solution or partial solution, nor have it in your possession, either electronically or on paper. You must never share your written solutions, or a partial solutions, with another student, even with the explicit understanding that it will not be copied -- not even with students in your homework group. You must write your homework solution strictly by yourself.

*Warning:* Your attention is drawn to the Department's Policy on Academic Dishonesty. In particular, you should be aware that copying or sharing solutions, in whole or in part, from other students in the class *or any other source* without acknowledgment constitutes cheating. Any student found to be cheating risks automatically failing the class and referral to the Office of Student Conduct.

*Ethics:* We will be discussing *attacks* in this class, some of them quite nasty. *None of this is in any way an invitation to undertake these attacks in any fashion* other than with *informed consent* of all involved and affected parties. The existence of a security hole is no excuse. These issues concern not only professional ethics, but also UCB policy and *state and federal law*. If there is any question in your mind about what conduct is allowable, contact the instructors first.

*Computer accounts:* We will use 'class' accounts this semester. You will need to obtain an account form with a username and password from your section TA. When you first log into your account, you will be prompted to enter information about yourself; that will register you with our grading software. If you want to check that you are registered correctly with our grading software, you can run `check-register` at any time.

*Textbook:* The class does not have a required textbook. That said, we particularly recommend *Introduction to Computer Security* by Goodrich & Tamassia. Another book optionally recommended as a partial resource is *Security Engineering, 2nd ed.* by Ross Anderson. The first edition of this book is also available online at Ross Anderson's web site.

*Lecture notes:* We will provide lecture notes and/or slides for many of the lectures. We do *not* advise viewing the availability of lecture notes or slides as a substitute for attending class, as our discussion in class may deviate from the written material. You are ultimately resposible for material *as presented in lecture and section*.

*Discussion sections:* Attendance at discussion sections is expected, and sections may cover important material not covered in lecture. Outside of your discussion section, you should feel free to attend any of the staff office hours (not just your section TA's office hours) and ask any of us for help.

- The problem(s) you want to be re-graded.
- For each of these problems a clear description of why you think the problem was misgraded.

Bear in mind that a primary aim in grading is consistency, so that all students are treated the same. For this reason, we are unlikely to adjust the score of individual students on an issue of partial credit if the score allocated is consistent with the grading policy we adopted for that problem.

*More on homeworks:* If a problem can be interpreted in more than one way, clearly state the assumptions under which you solve the problem. In writing up your homework you are allowed to consult any book, paper, or published material, except solutions from previous classes or elsewhere, as stated under the Collaboration section. If you consult external sources, you *must* cite your source(s). We will make model solutions available after the due date, and will return graded problem sets in a later discussion section.

*Late homework policy:* We will give no credit for homework turned in after the deadline. Please don't ask for extensions. We don't mean to be harsh, but we prefer to make model solutions available shortly after the due date, which makes it impossible to accept late homeworks.

*Don't be afraid to ask for help!* Are you struggling? We'd much rather you approached us for help than gradually fall behind over the semester until things become untenable. Sometimes this happens when students fear a possibly unpleasant conversation with a professor if they admit to not understanding something. We would much rather resolve/remedy your misunderstanding early than have it expand into further problems later. Even if you are convinced that you are the only person in the class that doesn't understand the material, and think it must be entirely your fault for falling behind, please overcome this concern and ask for help as soon as you need it--Remember helping you learn the material is in fact what we're paid to do, after all!

*Advice:* The following tips are offered based on our experience with CS 161:

*1. Don't wait until the last minute to start projects!* The projects can be time-consuming. Pace yourself. Students who procrastinate generally suffer.

*2. Make use of office hours!* The instructors and TAs hold office hours expressly to help you. It is often surprising how many students do not take advantage of this service. You are free to attend as many office hours as you wish. You are not constrained just to use the office hours of your section TA. You will likely get more out of an office hour visit if you have spent some time in advance thinking about the questions you have, and formulating them precisely. (In fact, this process can often lead you to a solution yourself!)

*3. Participate actively in discussion sections!* Discussion sections are *not* auxiliary lectures. They are an opportunity for interactive learning. The success of a discussion section depends largely on the willingness of students to participate actively in it. As with office hours, the better prepared you are for the discussion, the more you are likely to get out of it.

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