Quasar-convex functions admit high-order proximal algorithms with linear convergence for p=2 and superlinear for p>2 under suitable conditions.
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PANOC-lite is a proximal-gradient linesearch method with a cheaper backtracking procedure and novel merit function that achieves global subsequential convergence and local superlinear convergence under standard assumptions.
Robust learning problems are formulated as quasar-convex optimization, and HiPPA is proposed as an inexact high-order proximal method with global and superlinear convergence guarantees.
citing papers explorer
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Quasar-Convex Optimization: Fundamental Properties and High-Order Proximal-Point Methods
Quasar-convex functions admit high-order proximal algorithms with linear convergence for p=2 and superlinear for p>2 under suitable conditions.
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PANOC-lite: A simpler and more efficient algorithm for composite minimization
PANOC-lite is a proximal-gradient linesearch method with a cheaper backtracking procedure and novel merit function that achieves global subsequential convergence and local superlinear convergence under standard assumptions.
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Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods
Robust learning problems are formulated as quasar-convex optimization, and HiPPA is proposed as an inexact high-order proximal method with global and superlinear convergence guarantees.