Gradient Descent Methods for Large-Scale Linear Inverse Problems Public
Cheng, Chen (2013)
Abstract
Iterative gradient descent methods are frequently used for ill-posed inverse problems because they are suitable for large models and they are cheap to work with. In this thesis, we explore three dierent types of gradient descent methods: the Landweber method, method of steepest descent, and the Barzilai-Borwein method. Specically, we also compare the eciency of these methods to the conjugate gradient method. The thesis begins with an introduction to the history and application of the gradient descent methods and to the methods tested, and follows with convergence analysis and numerical experience on real images. Ways to accelerate and smooth the Barzilai-Borwein method are also included.
Table of Contents
Contents 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Mathematical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Iterative Regulizarization . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Methods 7 2.1 Gradient Descent Methods . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 The Landweber Method . . . . . . . . . . . . . . . . . . . . . 10 2.1.2 Method of Steepest Descent . . . . . . . . . . . . . . . . . . . 11 2.1.3 The Barzilai-Borwein Method . . . . . . . . . . . . . . . . . . 14 2.2 The Conjugate Gradient Method . . . . . . . . . . . . . . . . . . . . 16 3 Convergence Analysis 18 3.1 Original Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Some Background . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2 Gradient Descent Methods . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Conjugate Gradient Method . . . . . . . . . . . . . . . . . . . 22 3.2 Preconditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4 Experiments 25 4.1 Satellite Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2 Grain Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3 Gaussian Blur Image . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5 Conclusion and Discussion 43
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