000			03380nam a22002657a 4500
003			IIITD
005			20240502151936.0
008			240410b xxu||||| |||| 00| 0 eng d
020			_a9783031267895
040			_aIIITD
082			_a519.3 _bGRI-I
100			_aGrippo, Luigi
245			_aIntroduction to methods for nonlinear optimization _cby Luigi Grippo and Marco Sciandrone.
260			_aCham : _bSpringer, _c©2023
300			_axv, 721 p. : _bill. ; _c24 cm.
490			_aUNITEXT-La Matematica per il 3+2 ; _v152
504			_aIncludes bibliographical references and index.
505			_t1 Introduction _t2 Fundamental definitions and basic existence results _t3 Optimality conditions for unconstrained problems in Rn _t4 Optimality conditions for problems with convex feasible set _t5 Optimality conditions for Nonlinear Programming _t6 Duality theory _t7 Optimality conditions based on theorems of the alternative _t8 Basic concepts on optimization algorithms _t9 Unconstrained optimization algorithms _t10 Line search methods _t11 Gradient method _t12 Conjugate direction methods _t13 Newton’s method _t14 Trust region methods _t15 Quasi-Newton Methods _t16 Methods for nonlinear equations _t17 Methods for least squares problems _t18 Methods for large-scale optimization _t19 Derivative-free methods for unconstrained optimization _t20 Methods for problems with convex feasible set _t21 Penalty and augmented Lagrangian methods _t22 SQP methods _t23 Introduction to interior point methods _t24 Nonmonotone methods _t25 Spectral gradient methods _t26 Decomposition methods
520			_aThis book has two main objectives: • to provide a concise introduction to nonlinear optimization methods, which can be used as a textbook at a graduate or upper undergraduate level; • to collect and organize selected important topics on optimization algorithms, not easily found in textbooks, which can provide material for advanced courses or can serve as a reference text for self-study and research. The basic material on unconstrained and constrained optimization is organized into two blocks of chapters: • basic theory and optimality conditions • unconstrained and constrained algorithms. These topics are treated in short chapters that contain the most important results in theory and algorithms, in a way that, in the authors’ experience, is suitable for introductory courses. A third block of chapters addresses methods that are of increasing interest for solving difficult optimization problems. Difficulty can be typically due to the high nonlinearity of the objective function, ill-conditioning of the Hessian matrix, lack of information on first-order derivatives, the need to solve large-scale problems. In the book various key subjects are addressed, including: exact penalty functions and exact augmented Lagrangian functions, non monotone methods, decomposition algorithms, derivative free methods for nonlinear equations and optimization problems. The appendices at the end of the book offer a review of the essential mathematical background, including an introduction to convex analysis that can make part of an introductory course.
650			_aContinuous Optimization
650			_aMathematical and Computational Engineering Applications.
650			_aMathematics of Computing.
700			_aSciandrone, Marco
942			_2ddc _cBK
999			_c172539 _d172539