  This paper proposes a robust approximation method for solving chance
constrained optimization (CCO). The studied problems are defined with
individual chance constraints that is affine in the decision variables. We find
a robust approximation of the CCO by replacing the chance constraint with a
robust constraint over some uncertainty set. A heuristic algorithm is presented
to find efficient uncertainty sets. When the objective function is linear or a
SOS-convex polynomial, the robust approximation can be equivalently transformed
into linear conic optimization. Semidefinite algorithms are proposed to solve
these linear conic transformations and find candidate optimal solutions to the
original CCO. Convergent properties are proved and numerical experiments are
given to show the efficiency of our method.
