of different data sets even for very small problems to handle generic algorithms optimization using such a specification is quite difficult even using the recently developed techniques of convex optimization 5 8 our method works as follows 1 an optimal solution i e a solution for which a metric a function of uncertain parameters is maximized for a random allowable set of input data is determined using standard algorithms without uncertainty 2 the worst case performance of this solution for the complete ensemble of possible input data is determined together with the corresponding worst case input data if the metric to be optimized is linear in the uncertain input data this worst case input data can be determined using linear programming 3 if step 2 does not result in acceptable performance another solution is generated by either a perturbing the original solution e g randomly changing it b computing a new solution which is optimal at the worst case input data determined in step 2 4 step 3 is repeated till a solution with an acceptable value of the metric is found or till the set of all solutions found jointly have an acceptable value of the metric the word jointly means that for every candidate input dataset allowed by the constraints we choose the solution which has best metric given this candidate dataset this last step can be done using linear programming for linear metric the steps 1 2 and 3 can be used u2014 a new path is repeatedly determined using either random perturbation of an original path step.