Speakers
Description
Convex optimization is fundamental to modern statistics and machine learning, underpinning methods from least squares and ridge regression to support vector machines (SVMs) and portfolio optimization. While Python users have long enjoyed state-of-the-art convex optimization through CVXPY, R users now have access to the same capabilities through CVXR 1.8.x---a complete rewrite using R's S7 class system that brings full parity with CVXPY 1.8.1. Participants will work through a rich set of examples and be introduced to powerful new features. We show how to use different solvers, both open source (authored on CRAN by us) and commercial grade (MOSEK, Gurobi, CPLEX). The tutorial concludes with an open problem-solving session where attendees will be invited to bring their own problems to formulate in CVXR and solve them.
Introduction: (20min): Convex problems in statistics and ML, Basic examples from regression, nonlinear least squares.
Disciplined Convex Programming (20 min): DCP rules, automatic convexity verification. CVXR's variables, parameters, constraints, objectives, documentation
Examples I (40min): Elastic-net, lasso, ridge, Huber, logistic and quantile regression. Sparse Inverse Covariance estimation.
Examples II: (40min) Portfolio optimization, log-concanve density estimation, survey calibration.
Log-concave density estimation.
Efficiency: (25min) Disciplined Parameterized Programming for efficient parameter sweeps with compilation caching, visualization, debugging
Misc (20min): Mixed-integer programming, solver choices, open source and commercial.
Open Problem-Solving Session + Q&A (15 min): Attendees bring and solve their own optimization problems with instructor guidance.
Target audience (only for tutorials)
Statisticians, machine learning practitioners, scientific researchers using any optimization, quantitative finance analysts, engineers, physicists and social scientists.
Learning goals (only for tutorials)
1) Learning how to formulate and solve linear, quadratic and mixed quadratic programs easily in R
2) Use CVXR in their own teaching, research or work
3) Use CVXR in their own CRAN packages
Additional Material or Paper
Website: https://cvxr.rbind.io
CRAN: https://cran.r-project.org/package=CVXR
Paper: @Article{cvxr2020,
title = {{CVXR}: An {R} Package for Disciplined Convex Optimization},
author = {Anqi Fu and Balasubramanian Narasimhan and Stephen Boyd},
journal = {Journal of Statistical Software},
year = {2020},
volume = {94},
number = {14},
pages = {1--34},
doi = {10.18637/jss.v094.i14},
}
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
No AI tools/services were used.
Prerequisites (only for tutorials)
Intermediate R programming (familiarity with data frames, functions, and basic matrix operations). Basic knowledge of linear algebra and probability. A laptop with R and the CVXR v1.8.1 package, soon to be on CRAN. No prior optimization background is required, though basic exposure is helpful.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Convex Optimization, Statistical Modeling, Machine Learning, Mathematical Programming, Disciplined Convex Programming |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | naras@stanford.edu |