Computer Science 284/484 - Advanced Algorithms - Fall 2026

Daniel Stefankovic

Textbooks: there is no required textbook; see each section for the recommended reading (mostly online; I will also supply handouts).

Other resources/recommended reading:

[CLRS09] Introduction to Algorithms (3rd edition), T. Cormen, C. Leiserson, R. Rivest, and C. Stein, 2009.
[K91] The Design and Analysis of Algorithms, D. Kozen, 1991.
[KT05] Algorithm Design, J. Kleinberg and E. Tardos, 2005.
[AHU74] The Design and Analysis of Computer Algorithms, A. Aho, J. Hopcroft, J. Ulman, 1974.

[MU05] Probability and Computing: Randomized Algorithms and Probabilistic Analysis, M. Mitzenmacher, E. Upfal, 2005.
[MR95] Randomized Algorithms, R. Motwani, P. Raghavan, 1995.
[H02] Finite Markov chains and algorithmic applications, O. Haggstrom, 2002.
[LPW09]Markov Chains and Mixing Times, D. Levin, Y. Peres and E. Wilmer, 2009.
[G97] Counting, sampling and integrating: algorithms and complexity, M. Jerrum, 2003.

[WS11] The Design of Approximation Algorithms, D.Williamson, D. Shmoys, 2011
[V04] Approximation Algorithms, V. Vazirani, 2004.

[BE98] Online Computation and Competitive Analysis, A. Borodin, R. El-Yaniv, 1998.
[M05] Data Streams: Algorithms and Applications, S. Muthukrishnan, 2005.

[G97] Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology, D. Gusfield, 1997.
[BCKO08] Computational Geometry: Algorithms and Applications, M. de Berg, O. Cheong, M. van Kreveld, M. Overmars, 2008.
[PS85] Computational Geometry: An Introduction, F. Preparata, M. Shamos, 1985.
[DO11] Discrete and Computational Geometry, S. Devadoss, J. O'Rourke, 2011.
[M93] Computational Geometry: An Introduction Through Randomized Algorithms, K. Mulmuley, 1993.

Schedule

TOPIC 1: Max-Flow

TOPIC 2: String Algorithms

TOPIC 3: Randomized Algorithms (with introduction to probability theory)

TOPIC 4: Streaming Algorithms

TOPIC 5: Sampling/Counting, Markov chain Monte Carlo

TOPIC 6: Computational Geometry

TOPIC 7: Approximation Algorithms

Grading

grade

40% homework: (theoretical and applied) there will be 6 problem sets.
40% in-class worksheets: formative assessment: graded for completion/honest attempt; you can miss 6 in-class worksheets and still receive full credit for this component. In-class worksheets are submitted by the students on gradescope after class.
20% final exam.

Homework Rules

Theoretical homework should be typeset (LaTeX preferred) and submitted on Gradescope. Applied homework should be submitted on Gradescope.
You may work with other people on the homework, but you must each write up your solutions separately. Do not share your written solutions. Do not share code.
You cannot use any written aid when writing your answers or code---this includes any responses generated by AI systems (you can ask AI systems for code, you can look at the code, you cannot copy the code).
AI tools may not be used to write any part of your code (you can use AI for debugging your code, you have to implement the changes in the code yourself).
If you work with other people, indicate who you worked with on your solution.
No late homework will be accepted without instructor's permission (a permission must be requested at least 24 hours before the due date).

AI policy

AI use is allowed with the following two rules: The AI prompts and responses have to meaningfully go through your brain. The AI use has to be transparent. This means:
- Copying assignment into AI prompt in not allowed (you are allowed to ask AI about the assignment---the prompt has to be generated by your brain).
- Copying code or sentences from an AI response is not allowed (you are allowed to look at the AI responses and internalize them---the code and sentences that are in your solutions have be generated by your brain).
- When you use AI on an assignment, indicate which AI system you used and append the chat to the assignment (this includes prompts and AI responses).

This course follows The University Policy on Academic Honesty .