CSCI 532, Spring 2026

CSCI 532: Algorithms

Spring 2026

Date	Lecture Topic	Homework
13 Jan 15 Jan	Background/Skills Check Greedy (MST)	HW 1 (Due 10 Feb)
20 Jan 22 Jan	Greedy (Scheduling) Greedy (Scheduling)
27 Jan 29 Jan	Divide and Conquer (Closest Pair) Dynamic Programming (Longest Path)
03 Feb 05 Feb	Dynamic Programming (Vertex Cover) Dynamic Programming (Edit Distance)
10 Feb 12 Feb	Flow Networks (Intro) Test 1
17 Feb 19 Feb	Flow Networks (Applications) Flow Networks (Validity)	HW 2 (Due 10 Mar)
24 Feb 26 Feb	Flow Networks (Optimality) Linear Programming (Intro)
03 Mar 05 Mar	Linear Programming (Applications) Linear Programming (Simplex)
10 Mar 12 Mar	Linear Programming (Duality) Test 2
17 Mar 19 Mar	No Class - Spring Break No Class - Spring Break
24 Mar 26 Mar	Integer Linear Programming (Intro) Integer Linear Programming (TU)	HW 3 (Due 28 Apr)
31 Mar 02 Apr	Linear Programming/NP-Completeness Approximation (APX)
07 Apr 09 Apr	Approximation (Non-APX) Approximation (PTAS)
14 Apr 16 Apr	Approximation (Inapproximability) Randomized (Intro)
21 Apr 23 Apr	Randomized (Randomized Rounding) Randomized (Chernoff Bounds)
28 Apr 30 Apr	Randomized (Closest Pair) Test 3
07 May	Presentations, 12:00 - 1:50 pm

Schedule subject to change. Refresh webpage (or hit F5) to view current page.

Lecture

Tuesday, Thursday 12:15 - 1:30 pm in Wilson 1144
Lectures will be videotaped and put on this website.

Instructor

Sean Yaw

E-mail: sean.yaw (at) montana.edu (email me whenever, I'll respond as soon as I get it)
Office: Barnard Hall 360
Office Hours: Tuesday 1:30 - 3:00 pm, Thursday 10:00 - 11:30 am and by appointment.

Textbook

Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (3rd edition).

Course Objectives

MSU course description: Concrete time and space complexity; combinatorial algorithms; greedy algorithms; dynamic programming; probabilistic and randomized algorithms; branch-and-bound algorithms.

At the end of the course, my goal is for you to be able to:

Given a problem, understand it and develop a clear, efficient plan to solve it.
Understand a broad set of algorithmic tools and have an intuition for when to apply which tools, including:

Dynamic Programming.
Greedy Approaches.
Graph Representations.
Linear Programming.
Approximation Techniques.

Understand and be able to comment on the time and space complexity of an algorithm, including being able to characterize recursive relations.
Understand what NP-Complete problems are, have an intuition for the solvability of new problems, and have familiarity with techniques to deal with NP-Complete problems.

Grading

Homework - 20%
Test 1, 2, 3 - 20% each
Project - 20%

At the end of the semester, grades will be determined (after any curving takes place) based on your class average as follows:

93+: A
90+: A-
87+: B+
83+: B
80+: B-
77+: C+
73+: C
70+: C-
67+: D+
63+: D
60+: D-
0+: F

Project

There are two types of projects (research projects and reading projects) as described below. PhD students and thesis-based MS students need to do a research project and courses-only MS students may do either a research or reading project.

Research Project. Find an algorithmic problem that interests you and attempt to do work that has not been done before. This could be an implementation/experimentation study or theoretical in nature (e.g., try to prove some result or design a new algorithm for a problem) or a combination of both. The goal of this is to do some novel research. The expectation is not that you end up with something publishable, so don't stress it too much, but that is the motivation. Feel free to take a risk on something.
Reading Project. Find a reasonably modern paper with a strong, non-trivial, algorithm component and read/understand it. You must have me approve your paper before you present it, so leave enough time to find another if I tell you to. Your final report should describe the problem/solution/evaluation and should include your analysis of the work (regurgitating the authors future work is not your analysis).

For both projects, you will have to write a report (due 07 May at 12:00 pm) and make a presentation to the class during the finals time slot (07 May at 12:00 pm). Don't hesitate to come talk to me about project/paper ideas. You will be graded based on quality of research (if applicable), quality of report writing, and quality of presentation. Per the collaboration policy, you can work in groups of up to two people.

Late Policy

No late submissions will be accepted.

Collaboration Policy

You are encouraged to do homework assignments in groups of two people. You must indicate on the submission everyone that contributed. If someone did not substantially contribute to a submission, they cannot be included on it.
Exams are to be taken individually.
You may not copy or modify solutions that are not your own (e.g. from the Internet, from a classmate not listed as a contributor, from our AI overlords,...) for any graded material.

AI Policy

You may use AI tools to help you understand concepts, guide your problem-solving process, or improve your writing. However, you are expected to fully understand all of the work you submit. If you submit work that you cannot explain or demonstrate understanding of, then you are submitting work that is not yours. In such cases, grades may be adjusted retroactively to reflect this. You will not have access to AI on the in-class tests.