Package de.labathome.trustconstr.interfaces

Interface HessianUpdateStrategy

All Superinterfaces:
BiFunction<Matrix,Object,Matrix>
All Known Implementing Classes:
BFGS, FullHessianUpdateStrategy, SR1
public interface HessianUpdateStrategy extends BiFunction<Matrix,Object,Matrix>
Interface for implementing Hessian update strategies. Many optimization methods make use of Hessian (or inverse Hessian) approximations, such as the quasi-Newton methods BFGS, SR1, L-BFGS. Some of these approximations, however, do not actually need to store the entire matrix or can compute the internal matrix product with a given vector in a very efficiently manner. This class serves as an abstract interface between the optimization algorithm and the quasi-Newton update strategies, giving freedom of implementation to store and update the internal matrix as efficiently as possible. Different choices of initialization and update procedure will result in different quasi-Newton strategies. Four methods should be implemented in derived classes: ``initialize``, ``update``, ``dot`` and ``get_matrix``. Any instance of a class that implements this interface, can be accepted by the method ``minimize`` and used by the compatible solvers to approximate the Hessian (or inverse Hessian) used by the optimization algorithms.

  • Method Details

    • initialize

      void initialize(long n, HessianApproximationType approxType)
      Initialize internal matrix.

      Allocate internal memory for storing and updating the Hessian or its inverse.

      Parameters:
      n - Problem dimension.
      approxType - Selects either the Hessian or the inverse Hessian. When set to 'hess' the Hessian will be stored and updated. When set to 'inv_hess' its inverse will be used instead.

    • update

      void update(Matrix deltaX, Matrix deltaG)
      Update internal matrix.

      Update Hessian matrix or its inverse (depending on how 'approx_type' is defined) using information about the last evaluated points.

      Parameters:
      deltaX - The difference between two points the gradient function have been evaluated at: ``delta_x = x2 - x1``.
      deltaG - The difference between the gradients: ``delta_grad = grad(x2) - grad(x1)``.

    • getMatrix

      Matrix getMatrix()
      Return current internal matrix.
      Returns:
      Dense matrix containing either the Hessian or its inverse (depending on how 'approx_type' is defined).