Name
Abstract | ||
|---|---|---|
It has recently become clear that many control problems are too difficult to admit analytic solutions. New results have also emerged to show that the computational complexity of some “solved” control problems is prohibitive. Many of these control problems can be reduced to decidability problems or to optimization questions. Even though such questions may be too difficult to answer analytically, or may not be answered exactly given a reasonable amount of computational resources, researchers have shown that we can “approximately” answer these questions “most of the time”, and have “high confidence” in the correctness of the answers. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0096-3003(99)00283-0 | Applied Mathematics and Computation |
Keywords | Field | DocType |
Empirical processes,Statistical learning,Robust control,Optimization | Optimal control,Correctness,Algorithm,Decidability,Probability distribution,Robust control,Stopping time,Mathematics,Computational complexity theory,Stochastic control | Journal |
Volume | Issue | ISSN |
120 | 1-3 | 0096-3003 |
Citations | PageRank | References |
4 | 0.44 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Koltchinskii | 1 | 89 | 9.61 |
| C. T. Abdallah | 2 | 246 | 28.44 |
| M. Ariola | 3 | 228 | 25.36 |
| P. Dorato | 4 | 224 | 47.00 |