A scaled gradient-momentum framework achieves global finite-time convergence by linking gradient-dominance properties of the objective to finite-time stability via state-dependent scaling.
Opti- mization algorithms as robust feedback controllers
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Develops a dissipativity and contraction theory framework for convergence analysis of distributed optimization algorithms, producing LMI conditions for arbitrary network structures.
PRIME is a proximal primal-dual feedback optimization scheme whose quadratic incentives make self-interested actors' best responses match a centralized optimal power flow update.
citing papers explorer
-
Finite-Time Optimization via Scaled Gradient-Momentum Flows
A scaled gradient-momentum framework achieves global finite-time convergence by linking gradient-dominance properties of the objective to finite-time stability via state-dependent scaling.
-
Convergence Analysis of Distributed Optimization: A Dissipativity Framework
Develops a dissipativity and contraction theory framework for convergence analysis of distributed optimization algorithms, producing LMI conditions for arbitrary network structures.
-
PRIME: Fast Primal-Dual Feedback Optimization for Markets with Application to Optimal Power Flow
PRIME is a proximal primal-dual feedback optimization scheme whose quadratic incentives make self-interested actors' best responses match a centralized optimal power flow update.