MATLABoptimization
2016-08-23
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MATLAB optimization program includes unconstrained one-dimensional extreme value problem forward and backward method golden section method Fibonacci method Newton method basic Newton method global Newton method secant method parabola method cubic interpolation method acceptable search method goidstein method Wolfe.Powell Method simplex search method Powell method steepest descent method conjugate gradient method Newton method modified Newton method quasi Newton method trust region method Explicit steepest descent method, Rosen gradient projection method, penalty function method, exterior penalty function method, interior penalty function method, mixed penalty function method, multiplier method, G-N method, modified G-N method, L-M method, linear programming simplex method, modified simplex method, large M method, variable bounded simplex method, integer programming cutting plane method, branch and bound method, 0-1 programming quadratic programming Lagrange method, action set algorithm Path tracking particle swarm optimization basic particle swarm optimization particle swarm optimization with compression factor weight improved particle swarm optimization linear decreasing weight adaptive weight random weight variable learning factor particle swarm optimization synchronous learning factor asynchronous learning factor second order particle swarm optimization second order oscillatory particle swarm optimization MATLAB optimization process includes Non-binding one-dimensional extremum problems Advance and retreat method Golden Section Fibonacci method of basic Newton s method Newton s method Newton s Law of the global secant method parabola method acceptable to the three interpolation search method Goidstein France Wolfe.Powell France Simplex search method Powell steepest descent method Conjugate gradient method Newton s method Newton s method to amend Quasi-Newton Method trust regio
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MATLABoptimization
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