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.
Finite-time convergent gradient flows with applications to network consensus,
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Nonlinearly preconditioned gradient flows admit global solutions with sublinear or exponential convergence and are dual to mirror descent, solving an infinite-horizon optimal control problem whose value function is the Bregman divergence of the cost.
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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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Nonlinearly preconditioned gradient flows
Nonlinearly preconditioned gradient flows admit global solutions with sublinear or exponential convergence and are dual to mirror descent, solving an infinite-horizon optimal control problem whose value function is the Bregman divergence of the cost.