Introduces static and dynamic feedback controllers for Lagrange multipliers in a proximal augmented Lagrangian plant model, producing two new optimization algorithms with global exponential convergence under strong convexity.
Proximal algorithms
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A modified projection in the ADMM z-update solves QPs with slack variables for feasibility without enlarging the original problem, and the method is proven equivalent to the standard expanded formulation.
The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.
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Feedback control of Lagrange multipliers for non-smooth constrained optimization
Introduces static and dynamic feedback controllers for Lagrange multipliers in a proximal augmented Lagrangian plant model, producing two new optimization algorithms with global exponential convergence under strong convexity.
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Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size
A modified projection in the ADMM z-update solves QPs with slack variables for feasibility without enlarging the original problem, and the method is proven equivalent to the standard expanded formulation.
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Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.