Abstract
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 language | English |
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Pages | 1-7 |
Number of pages | 7 |
Publication status | Published - Jun 2019 |
Event | European Control Conference - Hotel Royal Continental, Naples, Naples, Italy Duration: 25 Jun 2018 → 28 Jun 2019 https://ecc19.eu/ |
Conference
Conference | European Control Conference |
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Abbreviated title | ECC |
Country | Italy |
City | Naples |
Period | 25/06/2018 → 28/06/2019 |
Internet address |
Keywords
- Risk measures
- Risk-averse optimal control
- Optimal control
- Optimization
- Convex optimization