The hierarchy of local minimums in polynomial optimization
This paper studies the hierarchy of local minimums of a polynomial in the vector space \(\mathbb {R}^n\). For this purpose, we first compute \(H\)-minimums, for which the first and second order necessary optimality conditions are satisfied. To compute each \(H\)-minimum, we construct a sequence of semidefinite relaxations, based on optimality conditions. We prove that each constructed sequence has finite convergence, under some generic conditions. A procedure for computing all local minimums is given. When there are equality constraints, we have similar results for computing the hierarchy of critical values and the hierarchy of local minimums.
local minimum polynomial optimization optimality condition semidefinite relaxation critical point sum of squares 65k05 90c22 90c26