TY - GEN
T1 - Optimization Filters for Stochastic Time-Varying Convex Optimization
AU - Simonetto, Andrea
AU - Massioni, Paolo
N1 - Publisher Copyright:
© 2023 EUCA.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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 dynamical system and a measurement equation, respectively, yielding the notion of filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a linear matrix inequality condition in the constrained case. Some special cases and variations are discussed, and supporting numerical results are presented from real data sets in ride-hailing scenarios.
AB - 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 dynamical system and a measurement equation, respectively, yielding the notion of filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a linear matrix inequality condition in the constrained case. Some special cases and variations are discussed, and supporting numerical results are presented from real data sets in ride-hailing scenarios.
U2 - 10.23919/ECC57647.2023.10178237
DO - 10.23919/ECC57647.2023.10178237
M3 - Conference contribution
AN - SCOPUS:85166470841
T3 - 2023 European Control Conference, ECC 2023
BT - 2023 European Control Conference, ECC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 European Control Conference, ECC 2023
Y2 - 13 June 2023 through 16 June 2023
ER -