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
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Develops a dissipativity and contraction theory framework for convergence analysis of distributed optimization algorithms, producing LMI conditions for arbitrary network structures.
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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.
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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.