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Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments Read chapter Read first chapter

2019 | OriginalPaper | Chapter

AFSI: Adaptive Restart for Fast Semi-Iterative Schemes for Convex Optimisation

Authors : Jón Arnar Tómasson, Peter Ochs, Joachim Weickert

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Smooth optimisation problems arise in many fields including image processing, and having fast methods for solving them has clear benefits. Widely and successfully used strategies to solve them are accelerated gradient methods. They accelerate standard gradient-based schemes by means of extrapolation. Unfortunately, most acceleration strategies are generic, in the sense, that they ignore specific information about the objective function. In this paper, we implement an adaptive restarting into a recently proposed efficient acceleration strategy that was coined Fast Semi-Iterative (FSI) scheme. Our analysis shows clear advantages of the adaptive restarting in terms of a theoretical convergence rate guarantee and state-of-the-art performance on a challenging image processing task.

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previous chapter Information-Theoretic Active Learning for Content-Based Image Retrieval
next chapter Invexity Preserving Transformations for Projection Free Optimization with Sparsity Inducing Non-convex Constraints
Appendix
Available only for authorised users
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Metadata
Title
AFSI: Adaptive Restart for Fast Semi-Iterative Schemes for Convex Optimisation
Authors
Jón Arnar Tómasson
Peter Ochs
Joachim Weickert
Copyright Year
2019
Book
Pattern Recognition

Print ISBN: 978-3-030-12938-5

Electronic ISBN: 978-3-030-12939-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-12939-2_46

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