PCD is a new gradient-based optimizer for hierarchical multi-objective problems that prioritizes primary descent with minimal controlled distortion for secondary objectives via a single tau parameter.
Gallego-Posada, J
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Pore-scale disorder accelerates fluid stretching in porous media, producing quadratic time growth and faster mixing than the linear growth seen in ordered structures.
Fixed penalty methods in deep learning do not reliably solve problems with hard non-negotiable constraints, so the constrained formulation should be the starting point instead.
citing papers explorer
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Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization
PCD is a new gradient-based optimizer for hierarchical multi-objective problems that prioritizes primary descent with minimal controlled distortion for secondary objectives via a single tau parameter.
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How pore-scale disorder controls fluid stretching in porous media
Pore-scale disorder accelerates fluid stretching in porous media, producing quadratic time growth and faster mixing than the linear growth seen in ordered structures.
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Position: Adopt Constraints Over Fixed Penalties in Deep Learning
Fixed penalty methods in deep learning do not reliably solve problems with hard non-negotiable constraints, so the constrained formulation should be the starting point instead.