Armijo:
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Use the Armijo rule to compute a stepsize, alpha:
alpha = s * beta ^ m (beta < 1)
where "s" is the initial stepsize (usually 1), and "m" is the first
nonnegative integer (0,1,2,...) such that:
f(x+alpha*d) - f(x) <= sigma * alpha * g(x)' * d
where f(x) is the cost function, g(x) is the gradient of f(x), x is the
current state, and d is the current direction of the optimization iteration.
Create a function handle like this:
f = @(x) x(1)^2 + 3*(x(2)-2)^3
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Form:
[alpha,xnew,m] = Armijo( x, s, beta, sigma, d, f, g )
See also: NewtonsMethod.m
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Inputs
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x Initial state
s Maximum stepsize
beta Reduction factor
sigma Scale factor
d Direction
f Function handle for cost function
g Function handle for gradient of cost function
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Outputs
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alpha Stepsize
xnew New state
m Number of iterations
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Children: