Algorithms for Sparsity-Constrained Optimization [electronic resource] / by Sohail Bahmani.

By:

Bahmani, Sohail [author.]

Contributor(s):

SpringerLink (Online service)

Series: Springer Theses, Recognizing Outstanding Ph.D. Research ; 261Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XXI, 107 p. 13 illus., 12 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319018812

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

621.382 23

LOC classification:

TK5102.9
TA1637-1638
TK7882.S65

Online resources:

Click here to access online

Contents:

Introduction -- Preliminaries -- Sparsity-Constrained Optimization -- Background -- 1-bit Compressed Sensing -- Estimation Under Model-Based Sparsity -- Projected Gradient Descent for `p-constrained Least Squares -- Conclusion and Future Work.

In: Springer eBooksSummary: 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.

Item type:

eBooks

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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.