Almost Linear Time Graph Algorithms

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Course Description

Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. Possible topics include but are not limited to, spectral graph theory, sparsification, oblivious routing, local partitioning, Laplacian system solving, and maximum flow. Prerequisites: calculus and linear algebra.

Cross Listed Courses

Grading Basis

ROP - Letter or Credit/No Credit

Min

3

Max

3

Course Repeatable for Degree Credit?

No

Course Component

Lecture

Enrollment Optional?

No