Scenario optimization

For other uses, see Scenario (disambiguation).

The scenario approach or scenario optimization approach is a technique for obtaining solutions to robust optimization and chance-constrained optimization problems based on a sample of the constraints. It also relates to inductive reasoning in modeling and decision-making. The technique has existed for decades as a heuristic approach and has more recently been given a systematic theoretical foundation.

In optimization, robustness features translate into constraints that are parameterized by the uncertain elements of the problem. In the scenario method,[1][2][3] a solution is obtained by only looking at a random sample of constraints (heuristic approach) called scenarios and a deeply-grounded theory tells the user how “robust” the corresponding solution is related to other constraints. This theory justifies the use of randomization in robust and chance-constrained optimization.

  1. ^ Calafiore, Giuseppe; Campi, M.C. (2005). "Uncertain convex programs: Randomized solutions and confidence levels". Mathematical Programming. 102: 25–46. doi:10.1007/s10107-003-0499-y. S2CID 1063933.
  2. ^ Calafiore, G.C.; Campi, M.C. (2006). "The Scenario Approach to Robust Control Design". IEEE Transactions on Automatic Control. 51 (5): 742–753. doi:10.1109/TAC.2006.875041. S2CID 49263.
  3. ^ Campi, M. C.; Garatti, S. (2008). "The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs". SIAM Journal on Optimization. 19 (3): 1211–1230. doi:10.1137/07069821X.