What | Where |
---|---|
Instructor | Edgar Solomonik (solomon2@illinois.edu) |
TA | Samah Karim (swkarim2@illinois.edu) |
Time/place | Tu/Thu 9:30am-10:45am, (hybrid from 1302 Siebel from 1/25), Zoom link: https://illinois.zoom.us/j/83363404302 (email Edgar for password) |
Office Hours | Edgar: Thu 10:45-11:45, 4229 Siebel Center |
Lecture Recordings | https://mediaspace.illinois.edu/channel/CS+598+EVS%3A+Tensor+Computations_+Spring+2022/243978282 |
Discussion / Announcements | https://chat.labpna.net/cs598evs-sp22/channels/town-square |
Class URL | https://relate.cs.illinois.edu/course/cs598evs-s22/ |
The course will cover theory, algorithms, and applications of tensor decompositions and tensor networks. The key prerequisite for this material is familiarity with numerical linear algebra topics and algorithmic analysis.
The planned syllabus for the course is below. The major learning objectives for the courser are to establish fluency/intuition of understanding for computations involving tensor contractions and decompositions, to provide a general understanding of theoretical foundations of associated nonlinear optimization problems, to develop a methodology for efficient implementation of tensor algorithms, and to provide a view of the application landscape of tensor computations.
Review of Topics in Numerical Analysis
Tensor Algebra
Tensor Decompositions
Tensor Networks
Tensor Eigenvalues
Lectures will be supplemented with short in-class web assignments, which can also be completed asynchronously. Three short homework assignments are planned to test understanding, which can be completed individually or in small groups. Students have the choice of giving a presentation or preparing a report on a research project or review. In-class assignments, homeworks, and the presentation/report will each be worth 1/3 of the grade.
The homeworks and in-class activities will be posted here on a rolling basis. See the webpage for the 2020 offering to get an idea of the assignments.
Homework 1: Preconditioning via Low-Rank Approximation »
Homework 2: Accelerating CP-ALS using Tucker »
Homework 3: Review of Tensor Computations »
Please complete these within 2 weeks of the lecture in which they are covered (or by Feb 15th for early quizzes). Active sessions will be expired sometime after that unless an extension is requested.
Quiz 2: Computing the Maximum Ritz Value »
Quiz 3: Low Rank Approximation: Randomized SVD vs Krylov Subspace Methods »
Quiz 4: Finding Minimum of Quadratic Function in a Subspace »
Quiz 5: Graph Partitioning via the Fiedler Vector »
Quiz 7: Interior Point Methods for Quadratic Programming »
Quiz 8: Kronecker Product as a Tensor Operation »
Quiz 9: Converting a CP Decomposition to a Tucker Decomposition »
Quiz 11: Bilinear Algorithms for Convolution »
Quiz 12: Bilinear Algorithm for Multiplication of a Symmetric Matrix and a Vector »
Quiz 13: Alternating Least Squares for CP Decomposition »
Quiz 14: Dimension Trees for CP ALS »
Quiz 15: Gauss-Newton Method for CP Decomposition »
Quiz 16: Sparse Tensor times Matrix using CSF Format »
Quiz 17: Nonnegative Tensor Factorization »
Quiz 18: Imaginary Time Evolution »
Quiz 19: Tensor Network Canonical Forms »
Quiz 20: Density Matrix Renormalization Group (DMRG) »
Quiz 21: Computing Eigenvalues of a Symmetric Tensor »
Quiz 22: Computing an Exact Low Rank CP Decomposition »
CS 598 EVS: Tensor Computations, Fall '20
CS 598 EVS: Provably Efficient Algorithms for Numerical and Combinatorial Problems, Spring '20
CS 450: Numerical Analysis, Spring '21
We will be using Python with the libraries numpy, scipy and matplotlib for in-class work and assignments.
Python Workshop Material
Numpy Help
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