An Introduction to Compressed Sensing
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Published By Society For Industrial And Applied Mathematics

9781611976113, 9781611976120

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Chapter 5: Deterministic Construction of Measurement Matrices

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch5 ◽

2019 ◽

pp. 137-162

Keyword(s):

Deterministic Construction

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Chapter 4: Concepts from Graph Theory

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch4 ◽

2019 ◽

pp. 109-135

Keyword(s):

Graph Theory

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Chapter 1: Mathematical Preliminaries

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch1 ◽

2019 ◽

pp. 1-43

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Chapter 8: Some Recent Approaches to Vector Recovery

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch8 ◽

2019 ◽

pp. 243-286

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Chapter 9: Optimization Algorithms

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch9 ◽

2019 ◽

pp. 287-313

Keyword(s):

Optimization Algorithms

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Back Matter

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.bm ◽

2019 ◽

pp. 323-341

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Chapter 3: Null Space–Based Conditions

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch3 ◽

2019 ◽

pp. 69-107

Keyword(s):

Null Space

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Chapter 7: Matrix Recovery and Completion

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch7 ◽

2019 ◽

pp. 201-242

Keyword(s):

Matrix Recovery

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Chapter 10: Case Studies

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch10 ◽

2019 ◽

pp. 315-321

Keyword(s):

Case Studies

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Chapter 6: Probabilistic Construction of Measurement Matrices

An Introduction to Compressed Sensing ◽

10.1137/1.9781611976120.ch6 ◽

2019 ◽

pp. 163-199

Keyword(s):

Probabilistic Construction

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