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


convex relaxation for distributed optimal control



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

Click to access Tron_Fast_Multi-Image_Matching_ICCV_2017_paper.pdf



Finding MaxCut approximation of a graph is a classic one



Quantum Semidefinite programming course

Density matrices have a semidefinite constrina (non negative probabilities)



Sum of Squares is a semidefinite program that can guarantee that lyapunov functions actually work



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