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m demopt1.m

function demopt1(xinit) %DEMOPT1 Demonstrate different optimisers on Rosenbrock's function. % % Description % The four general optimisers (quasi-Newton, conjugate gradients, % scaled conjugate gradien
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htm cannyedgedetector.htm

Canny Edge Detection Tutorial
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m demopt1.m

function demopt1(xinit) %DEMOPT1 Demonstrate different optimisers on Rosenbrock's function. % % Description % The four general optimisers (quasi-Newton, conjugate gradients, % scaled conjugate gradien
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txt readme.txt

Orthant-Wise Limited-memory Quasi-Newton algorithm minimizes functions of the form f(w) = loss(w) + C |w|_1 where loss is an arbitrary differentiable convex loss function, and |w|_1 is the L
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texi multimin.texi

@cindex minimization, multidimensional This chapter describes routines for finding minima of arbitrary multidimensional functions. The library provides low level components for a variety of iterativ
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m bp_stochastical.m

function [a1, a2, t1, t2] = BP_Stochastical(TrainingData_File, TestingData_File, nHiddenNeuron, lr, mc, Type) % Input arguments: % P, T, TV --- Training and Testing data set % nHiddenNeuron
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c header.c

/************************************************************* 3-D Reconstruction of Medical Images Three Dimensional Reconstruction Of Medical Images from Serial Slices - CT, MRI, Ultraso