An introduction to optimization with applications in machine learning and data analytics /
Wheeler, Jeffrey Paul,
An introduction to optimization with applications in machine learning and data analytics / Jeffrey Paul Wheeler. - 448 pages : illustrations ; 24 cm.
Includes bibliographical references and index.
I. Preliminary Matters -- 1. Preamble -- 2. The Language of Optimization -- 3. Computational Complexity -- 4. Algebra Review -- 5. Matrix Factorization -- II. Linear Programming -- 6. Linear Programming -- 7. Sensitivity Analysis -- 8. Integer Linear Programming -- III. Nonlinear (Geometric) Programming -- 9. Calculus Review -- 10. A Calculus Approach to Nonlinear Programming -- 11. Constrained Nonlinear Programming: Lagrange Multipliers and the KKT Conditions -- 12. Optimization Involving Quadratic Forms -- 13. Iterative Methods -- 14. Derivative-Free Methods -- 15. Search Algorithms -- IV. Convexity and the Fundamental Theorem of Linear Programming -- 16. Important Sets for Optimization -- 17. The Fundamental Theorem of Linear Programming -- 18. Convex Functions -- 19. Convex Optimization (Jourdain Lamperski) -- V. Combinatorial Optimization -- 20. An Introduction to Combinatorics -- 21. An Introduction to Graph Theory -- 22. Network Flows -- 23. Minimum-Weight Spanning Trees and Shortest Paths -- 24. Network Modeling and the Transshipment Problem -- 25. The Traveling Salesperson Problem -- VI. Optimization for Data Analytics and Machine Learning -- 26. Probability -- 27. Regression Analysis via Least Squares (John McKay and Suren Jayasuria) -- 28. Forecasting (Joseph “Nico” Gabriel) -- 29. Introduction to Machine Learning (Suren Jayasuria and John McKay) -- Appendices: A. Techniques of Proof -- B. Useful Tools from Analysis and Topology -- Bibliography -- Index -- Notation.
This textbook offers a practical and balanced approach to optimization, blending theory and applications across multiple disciplines such as machine learning, economics, and engineering. It includes comprehensive coverage of linear and nonlinear programming, combinatorics, convex analysis, and modern machine learning techniques. The book integrates hands-on computing via Excel, Python, MATLAB, and other tools, making it accessible for a broad audience of students and professionals.
9780367425500
Mathematical optimization.
Machine learning.
Data analytics.
Operations research.
Engineering mathematics.
519.6 / W.J.I
An introduction to optimization with applications in machine learning and data analytics / Jeffrey Paul Wheeler. - 448 pages : illustrations ; 24 cm.
Includes bibliographical references and index.
I. Preliminary Matters -- 1. Preamble -- 2. The Language of Optimization -- 3. Computational Complexity -- 4. Algebra Review -- 5. Matrix Factorization -- II. Linear Programming -- 6. Linear Programming -- 7. Sensitivity Analysis -- 8. Integer Linear Programming -- III. Nonlinear (Geometric) Programming -- 9. Calculus Review -- 10. A Calculus Approach to Nonlinear Programming -- 11. Constrained Nonlinear Programming: Lagrange Multipliers and the KKT Conditions -- 12. Optimization Involving Quadratic Forms -- 13. Iterative Methods -- 14. Derivative-Free Methods -- 15. Search Algorithms -- IV. Convexity and the Fundamental Theorem of Linear Programming -- 16. Important Sets for Optimization -- 17. The Fundamental Theorem of Linear Programming -- 18. Convex Functions -- 19. Convex Optimization (Jourdain Lamperski) -- V. Combinatorial Optimization -- 20. An Introduction to Combinatorics -- 21. An Introduction to Graph Theory -- 22. Network Flows -- 23. Minimum-Weight Spanning Trees and Shortest Paths -- 24. Network Modeling and the Transshipment Problem -- 25. The Traveling Salesperson Problem -- VI. Optimization for Data Analytics and Machine Learning -- 26. Probability -- 27. Regression Analysis via Least Squares (John McKay and Suren Jayasuria) -- 28. Forecasting (Joseph “Nico” Gabriel) -- 29. Introduction to Machine Learning (Suren Jayasuria and John McKay) -- Appendices: A. Techniques of Proof -- B. Useful Tools from Analysis and Topology -- Bibliography -- Index -- Notation.
This textbook offers a practical and balanced approach to optimization, blending theory and applications across multiple disciplines such as machine learning, economics, and engineering. It includes comprehensive coverage of linear and nonlinear programming, combinatorics, convex analysis, and modern machine learning techniques. The book integrates hands-on computing via Excel, Python, MATLAB, and other tools, making it accessible for a broad audience of students and professionals.
9780367425500
Mathematical optimization.
Machine learning.
Data analytics.
Operations research.
Engineering mathematics.
519.6 / W.J.I