Nonlinear optimization filters for stochastic time‐varying convex optimization
We look at a stochastic time‐varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a nonlinear dynamical system and a measurement equa...
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| Published in | International journal of robust and nonlinear control Vol. 34; no. 12; pp. 8065 - 8089 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Bognor Regis
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01.08.2024
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| Abstract | We look at a stochastic time‐varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a nonlinear dynamical system and a measurement equation, respectively, yielding the notion of nonlinear filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a bilinear matrix inequality condition in the constrained case. Some special cases and variations are discussed, notably the case of parametric filters, yielding certificates based on LPV analysis and, if one wishes, matrix sum‐of‐squares relaxations. Supporting numerical results are presented from real data sets in ride‐hailing scenarios. The results are encouraging, especially when predictions are accurate, a case which is often encountered in practice when historical data is abundant. |
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| AbstractList | We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a nonlinear dynamical system and a measurement equation, respectively, yielding the notion of nonlinear filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a bilinear matrix inequality condition in the constrained case. Some special cases and variations are discussed, notably the case of parametric filters, yielding certificates based on LPV analysis and, if one wishes, matrix sum-of-squares relaxations. Supporting numerical results are presented from real data sets in ride-hailing scenarios. The results are encouraging, especially when predictions are accurate, a case which is often encountered in practice when historical data is abundant. |
| Author | Massioni, Paolo Simonetto, Andrea |
| Author_xml | – sequence: 1 givenname: Andrea orcidid: 0000-0003-2923-3361 surname: Simonetto fullname: Simonetto, Andrea email: andrea.simonetto@ensta-paris.fr organization: Institut Polytechnique de Paris – sequence: 2 givenname: Paolo orcidid: 0000-0001-8620-9507 surname: Massioni fullname: Massioni, Paolo organization: UMR5505 |
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| Snippet | We look at a stochastic time‐varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms... We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms... |
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| SubjectTerms | Algorithms Convexity Design optimization dissipativity Extended Kalman filter Filter design (mathematics) Mathematics Nonlinear filters Optimization and Control stochastic Optimization time‐varying Optimization |
| Title | Nonlinear optimization filters for stochastic time‐varying convex optimization |
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