Optimization For Machine Learning eBook Free Download

 

Optimization For Machine Learning eBook Free Download

 

Optimization For Machine Learning eBook Free Download

Optimization For Machine Learning eBook Free Download

Intrduction:

The yearly Neural Information Processing Systems (NIPS) workshops bring together researchers with extensively changing foundations in insights, arithmetic, software engineering, material science, electrical designing, neuroscience, and intellectual science, bound together by a typical longing to create novel computational what’s more, factual techniques for data preparing and to get it the instruments for data preparing in the mind. Interestingly to gatherings, these workshops keep up an adaptable organization that both permits furthermore, empowers the presentation and exchange of work in advancement. They hence serve as a hatchery for the advancement of imperative new thoughts in this quickly developing field. The arrangement editors, in counsel with workshop coordinators and individuals from the NIPS Foundation Board, select particular workshop themes on the premise of exploratory incredibleness, scholarly broadness, furthermore, specialized effect. Accumulations of papers picked and altered by the coordinators of particular workshops are assembled around pedagogical basic parts, while research monographs give extensive depictions of workshop-related subjects, to make a progression of books that gives an auspicious, definitive record of the most recent improvements in the energizing field of neural calculation.

Contents:

1 Introduction: Optimization and Machine Learning
S. Sra, S. Nowozin, and S. J. Wright 1
1.1 Support Vector Machines . . . . . . . . . . . . . . . . . . . . 2
1.2 Regularized Optimization . . . . . . . . . . . . . . . . . . . . 7
1.3 Summary of the Chapters . . . . . . . . . . . . . . . . . . . . 11
1.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Convex Optimization with Sparsity-Inducing Norms
F. Bach, R. Jenatton, J. Mairal, and G. Obozinski 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 GenericMethods . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 ProximalMethods . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 (Block) Coordinate Descent Algorithms . . . . . . . . . . . . 32
2.5 Reweighted-2 Algorithms . . . . . . . . . . . . . . . . . . . . 34
2.6 Working-SetMethods . . . . . . . . . . . . . . . . . . . . . . 36
2.7 Quantitative Evaluation . . . . . . . . . . . . . . . . . . . . . 40
2.8 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3 Interior-Point Methods for Large-Scale Cone Programming
M. Andersen, J. Dahl, Z. Liu, and L. Vandenberghe 55
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.2 Primal-Dual Interior-PointMethods . . . . . . . . . . . . . . 60

4 Incremental Gradient, Subgradient, and Proximal Methods
for Convex Optimization: A Survey
D. P. Bertsekas 85
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.2 Incremental Subgradient-Proximal Methods . . . . . . . . . . 98
4.3 Convergence forMethods with Cyclic Order . . . . . . . . . . 102
4.4 Convergence forMethods with Randomized Order . . . . . . 108
4.5 Some Applications . . . . . . . . . . . . . . . . . . . . . . . . 111
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
5 First-Order Methods for Nonsmooth Convex Large-Scale
Optimization, I: General Purpose Methods
A. Juditsky and A. Nemirovski 121
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.2 Mirror Descent Algorithm: Minimizing over a Simple Set . . . 126
5.3 Problems with Functional Constraints . . . . . . . . . . . . . 130
5.4 Minimizing Strongly Convex Functions . . . . . . . . . . . . . 131
5.5 Mirror Descent Stochastic Approximation . . . . . . . . . . . 134
5.6 Mirror Descent for Convex-Concave Saddle-Point Problems . 135
5.7 Setting up a Mirror Descent Method . . . . . . . . . . . . . . 139
5.8 Notes and Remarks . . . . . . . . . . . . . . . . . . . . . . . . 145
5.9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6 First-Order Methods for Nonsmooth Convex Large-Scale
Optimization, II: Utilizing Problem’s Structure
A. Juditsky and A. Nemirovski 149
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6.2 Saddle-Point Reformulations of Convex Minimization Problems151

Optimization For Machine Learning eBook Free Download

 

Optimization For Machine Learning eBook Free Download

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