Optimization package

This package contains several optimization routines for Torch. Each optimization algorithm is based on the same interface:

x*, {f}, ... = optim.method(func, x, state)

where:

• func: a user-defined closure that respects this API: f, df/dx = func(x)
• x: the current parameter vector (a 1D torch.Tensor)
• state: a table of parameters, and state variables, dependent upon the algorithm
• x*: the new parameter vector that minimizes f, x* = argmin_x f(x)
• {f}: a table of all f values, in the order they've been evaluated (for some simple algorithms, like SGD, #f == 1)

Available algorithms

Please check this file for the full list of optimization algorithms available and examples. Get also into the test directory for straightforward examples using the Rosenbrock's function.

Important Note

The state table is used to hold the state of the algorithm. It's usually initialized once, by the user, and then passed to the optim function as a black box. Example:

state = {
   learningRate = 1e-3,
   momentum = 0.5
}

for i,sample in ipairs(training_samples) do
    local func = function(x)
       -- define eval function
       return f,df_dx
    end
    optim.sgd(func,x,state)
end