Some random links on uses of Convex relaxations, in particular Semidefinite programming

Problems involving outer products of vector variables can be relaxed into semidefinite programs. That’s a general trick. Then the low rank bit from SVD is an approixmate solution for the vector

https://en.wikipedia.org/wiki/Matrix_completion

convex relaxation for distributed optimal control

http://ieeexplore.ieee.org/document/7464306/

graph matching in relation to Image correspondence

Permutation matrices have sum of rows and columns must be 1 constraint, is one relaxation.

quickMatch. Actually, not convex programming but was the root of the chain of references I ‘m digging through