Risk-averse risk-constrained optimal control

Pantelis Sopasakis, Mathijs Schuurmans, Panagiotis Patrinos

Research output: Contribution to conferencePaperpeer-review

20 Citations (Scopus)


Multistage risk-averse optimal control problems with nested conditional risk mappings are gaining popularity in various application domains. Risk-averse formulations interpolate between the classical expectation-based stochastic and minimax optimal control. This way, risk-averse problems aim at hedging against extreme low-probability events without being overly conservative. At the same time, risk-based constraints may be employed either as surrogates for chance (probabilistic) constraints or as a robustification of expectation-based constraints. Such multistage problems, however, have been identified as particularly hard to solve. We propose a decomposition method for such nested problems that allows us to solve them via efficient numerical optimization methods. Alongside, we propose a new form of risk constraints which accounts for the propagation of uncertainty in time.
Original languageEnglish
Number of pages7
Publication statusPublished - Jun 2019
EventEuropean Control Conference - Hotel Royal Continental, Naples, Naples, Italy
Duration: 25 Jun 201828 Jun 2019


ConferenceEuropean Control Conference
Abbreviated titleECC
Internet address


  • Risk measures
  • Risk-averse optimal control
  • Optimal control
  • Optimization
  • Convex optimization


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