Carte MATLAB Optimization Techniques Cesar Lopez

MATLAB Optimization Techniques

Autor: Cesar Lopez
Limbă: engleză
Legare: Carte broșată
Editura: APress
Disponibilitate: În depozitul extern
Expediem în 5-8 zile
406.83 lei
MATLAB is a high-level language and environment for numerical computation, visualization, and progra...

Informații despre carte

Autor
Limbă
engleză
Legare
Carte - Carte broșată
Publicat
2014
Pagini
292
EAN
9781484202937
ISBN
1484202937
Enbook ID
05339916
Editura
Greutate
572
Dimensiuni
192 x 237 x 20

Descriere completă

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.§§MATLAB Optimization Techniques introduces you to MATLAB and its Optimization Toolbox, which contains the current state of the art in optimization algorithms. The main algorithms for minimizing non-limited are the BFGS method quasi-Newton and direct research Nelder-Mead with linear research method. Quadratic programming (SQP) sequence variations are used for minimization with boundaries, achievement of objectives, and semi-infinite optimizations. Non-linear least-squares problems are solved using the methods of Guass-Newton or Levenberg-Marquardt.§§With practical, hands-on examples, you'll learn about highly-optimized algorithms to solve linear programming problems, non-linear least square with limits, non-linear unconstrained minimization, non-linear minimization constrained limits, non-linear minimization with linear equalities, non-linear system solving equations, quadratic minimization with limits restrictions, quadratic minimization with linear equalities, and limits-constrained linear least squares.§§You'll also find optimized methods for formulations of quadratic programming and non-linear objectives, with linear equality constraints or limits restrictions. These methods are trusted and optimized algorithms developed by Thomas F. Coleman, with reflective and projection Newtonian methods used to manage restrictions. Finally, you'll discover routines to solve linear and quadratic program problems, using a method of active series combined with imaging techniques. The routines presented, provide a range of algorithms and linear research strategies, which are protected methods of interpolation and quadratic and cubic extrapolation.§

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