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Algorithms for Sparsity-Constrained Optimization

ST: ALGORITHMS FOR SPARSITY CONSTRAINED OPTIMIZATION

Mathematics

Mathematics & Science

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B2B & B2C

This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a 'greedy' algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.

POST SECONDARY FOUR YEAR

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Duration

Duration Unit Of Measure

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Front Cover.
Other Frontmatter.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Supervisor&#x0027;s Foreword.
Acknowledgements.
Parts of This Thesis Have Been Published in the Following Articles.
Contents.
List of Algorithms.
List of Figures.
List of Tables.
Notations.
1: Introduction.
2: Preliminaries.
3: Sparsity-Constrained Optimization.
4: 1-Bit Compressed Sensing.
5: Estimation Under Model-Based Sparsity.
6: Projected Gradient Descent for &#x2113;p-Constrained Least Squares.
7: Conclusion and Future Work.
Proofs of Chap. 3.
Proofs of Chap. 4.
Proofs of Chap. 5.
Proofs of Chap. 6.

Algorithms for Sparsity-Constrained Optimization.css-1p8rvzy{position:absolute;padding-left:8px;}

Algorithms for Sparsity-Constrained Optimization