000			03049nam a22003017i 4500
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008			250728s2024 flua fr 001 0 eng d
020			_a9780367425500
040			_aEG-GaU‬‬ _cEG-GaU‬‬ _dEG-GaU‬‬ _erda
082	0	4	_223 _a519.6 _bW.J.I
100	1		_aWheeler, Jeffrey Paul, _eauthor. _962387
245	1	3	_aAn introduction to optimization with applications in machine learning and data analytics / _cJeffrey Paul Wheeler.
264		1	_aBoca Raton, FL : _bCRC Press, _c2024.
300			_a448 pages : _billustrations ; _c24 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references and index.
505			_aI. 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.
520			_aThis 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.
650		4	_aMathematical optimization. _91638
650		4	_aMachine learning. _915543
650		4	_aData analytics. _960497
650		4	_aOperations research. _925111
650		4	_aEngineering mathematics.
942			_2ddc _cBK
999			_c11793 _d11793