000			01163nam a22002537a 4500
005			20250328095117.0
008			250319b |||||||| |||| 00| 0 eng d
020			_a9783319775852
040			_aMAIN
082			_a519.6 _bSNY-18
100			_aSnyman, Jan A
245			_aPractical Mathematical Optimization : _bBasic Optimization Theory and Gradient-Based Algorithms / _cJan A Snyman, Daniel N Wilke.
250			_a2nd ed
260			_aCham : _bSpringer, _c2018
300			_axxvi, 372p.
440			_aSpringer Optimization and Its Applications, 1931-6828 ; 133
500			_a1.Introduction -- 2.Line search descent methods for unconstrained minimization.-3. Standard methods for constrained optimization.-4. Basic Example Problems -- 5. Some Basic Optimization Theorems -- 6. New gradient-based trajectory and approximation methods -- 7. Surrogate Models -- 8. Gradient-only solution strategies -- 9. Practical computational optimization using Python -- Appendix -- Index.
650			_aOptimization.
650			_aAlgorithms.
650			_aMathematical Software.
650			_a Numerical Analysis.
700			_aWilke, Daniel N,
942			_cBK
999			_c22224 _d22224