Pytorch implementation of a Trust Region Newton Conjugate Gradient method.

To install the model please follow the next steps in the specified order:

1. Clone this repository and install it using the setup.py script:

git clone https://github.com/vchoutas/torch-trust-ncg.git

2. If you do not wish to modify the optimizer then run:

python setup.py install

1. If you want to be able to modify the optimizer then run:

python setup.py build develop

To create the optimizer simply run:

    optimizer = TrustRegionNewtonCG(parameter_list)

where paremeter_list is the list of parameters you wish to optimize. To perform one optimization step simply call the step function and pass a closure that computes the loss and the gradients. Note the the closure should have a boolean argument named backward, so that the optimizer avoids unnecessary backward passes.

For a simple example see the main.py function. To run it for the rosenbrock function execute the following command:

python -m torchtrustncg

For more details see chapter 7.2 of "Numerical Optimization, Nocedal and Wright":

@Book{NoceWrig06,
    Title                    = {Numerical Optimization},
    Author                   = {Jorge Nocedal and Stephen J. Wright},
    Publisher                = {Springer},
    Year                     = {2006},
    Address                  = {New York, NY, USA},
    Edition                  = {second}
}