Explores learning the kernel solution in convex optimization, focusing on predicting outputs using a linear classifier and addressing possible numerical issues.
Explores polynomial optimization, emphasizing SOS and nonnegative polynomials, including the representation of polynomials as quadratic functions of monomials.
Explores Sum of Squares polynomials and Semidefinite Programming in Polynomial Optimization, enabling the approximation of non-convex polynomials with convex SDP.
Explores portfolio optimization models and strategies under uncertainty, emphasizing decision criteria like value-at-risk and mean-variance functional.
