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• W2893454762 abstract "We further develop a new framework, called PDE Acceleration, by applying it to calculus of variations problems defined for general functions on $mathbb{R}^n$, obtaining efficient numerical algorithms to solve the resulting class of optimization problems based on simple discretizations of their corresponding accelerated PDE's. While the resulting family of PDE's and numerical schemes are quite general, we give special attention to their application for regularized inversion problems, with particular illustrative examples on some popular image processing applications. The method is a generalization of momentum, or accelerated, gradient descent to the PDE setting. For elliptic problems, the descent equations are a nonlinear damped wave equation, instead of a diffusion equation, and the acceleration is realized as an improvement in the CFL condition from $Delta tsim Delta x^{2}$ (for diffusion) to $Delta tsim Delta x$ (for wave equations). We work out several explicit as well as a semi-implicit numerical schemes, together with their necessary stability constraints, and include recursive update formulations which allow minimal-effort adaptation of existing gradient descent PDE codes into the accelerated PDE framework. We explore these schemes more carefully for a broad class of regularized inversion applications, with special attention to quadratic, Beltrami, and Total Variation regularization, where the accelerated PDE takes the form of a nonlinear wave equation. Experimental examples demonstrate the application of these schemes for image denoising, deblurring, and inpainting, including comparisons against Primal Dual, Split Bregman, and ADMM algorithms." @default.
• W2893454762 created "2018-10-05" @default.
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• W2893454762 date "2018-09-30" @default.
• W2893454762 modified "2023-10-15" @default.
• W2893454762 title "Accelerated PDE's for efficient solution of regularized inversion problems" @default.
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