Jacob Mattingley

Jacob Mattingley () Ph.D. Candidate Department of Electrical Engineering Stanford University Advisor: Professor Stephen Boyd

Contact

Packard 243, Stanford, CA 94305
(650) 384-5388
jacobm@stanford.edu

Interests and current research

• Computer modelling of convex problems

• Engineering applications of convex optimization

• Signal processing

Software

• CVXMOD, a Python tool for convex optimization (currently in alpha)

• jemdoc, a light, equation supporting, text-based markup language for creating websites

• vim:essentials, a short page describing some of the essential features of the vim text editor.

Teaching

Teaching assistant for:

• Introduction to Linear Dynamical Systems (EE263), Autumn 2006 and Autumn 2007

• Convex Optimization I (EE364a), Winter 2008

• Convex Optimization II (EE364b), Winter 2007 and Spring 2008

Education

• M.S., Electrical Engineering, Stanford University, June 2007

• B.E. (Hons) (First class), Electrical and Computer Engineering, Canterbury University, April 2005

80 character Matlab LP solver

for i=1:80,p=diag(x)^2;r=p*(c-A'*(A*p*A'\A*p*c));m=x./r;x=x-.9*r*min(m(r>0));end

This is Dikin’s method for minimizing , subject to and . Start with x at a feasible point.

A = randn(100, 300);
x0 = rand(300, 1);
b = A*x0;
c = rand(300, 1);
x = x0;
for i=1:80,p=diag(x)^2;r=p*(c-A'*(A*p*A'\A*p*c));m=x./r;x=x-.9*r*min(m(r>0));end