CVX Research

Michael 

Michael C. Grant, Ph.D.
d/b/a CVX Research
1104 Claire Ave.
Austin, TX 78703-2502
V: (650) 323-1925
F: (650) 323-1926
mcg@cvxr.com

Consultancy

Available for short-term and part-time contracts:

  • Custom algorithm design and implementation

  • Application-related modeling assistance

  • Training and short courses

  • Commercial support for CVX (see below)

My areas of interest include:

  • Convex optimization

    • Modeling methodologies, automated transformations: see CVX

    • Compressive sampling algorithms and applications

    • Machine learning and statistical regression models

    • Numerical algorithms: interior-point methods, optimal first-order methods

    • Applications: engineering design, scientific, financial

  • Fast, efficient computational algorithms

    • Acceleration through exploitation of problem structure

    • CPU, GPU, DSP, and ASIC implementations

CVX: Matlab Software for Disciplined Convex Programming

CVX is a popular modeling framework for disciplined convex programming. CVX turns Matlab into a modeling language, allowing constraints and objectives to be specified using standard Matlab expression syntax. The result is a powerful tool for the rapid prototyping of models and algorithms incorporating convex optimization.

Click here to learn more and to download a copy. As with many free open-source offerings, standard support is limited to bug fixes and simple improvements as time permits. Commercial entities should feel free to contact me for more formal support options:

  • Expedited and/or exclusive enhancements and interfaces

  • Alternative licensing arrangements

  • Porting CVX models to standalone applications

Current projects and affiliations

Guest lecturer, The University of Texas at Austin
Department of Mechanical Engineering
Operations Research and Industrial Engineering
Course: Nonlinear Programming (focus: convex optimization)

Staff Scientist, California Institute of Technology
Department of Applied and Computational Mathematics
Division of Engineering and Applied Science
Advisor: Emmanuel Candès
Project: GPU acceleration of compressed sensing for wireless applications

Chief Engineer, Cardinal Optimization Inc.
Project: Rapid localization of large-scale ad-hoc sensor networks

Other projects subject to NDA.