The algorithms described in this section use only the value of the function at each evaluation point.
These methods use the Simplex algorithm of Nelder and Mead. Starting from the initial vector x = p_0, the algorithm constructs an additional n vectors p_i using the step size vector s = step_size as follows:
p_0 = (x_0, x_1, ... , x_n) p_1 = (x_0 + s_0, x_1, ... , x_n) p_2 = (x_0, x_1 + s_1, ... , x_n) ... = ... p_n = (x_0, x_1, ... , x_n + s_n)
These vectors form the n+1 vertices of a simplex in n dimensions. On each iteration the algorithm uses simple geometrical transformations to update the vector corresponding to the highest function value. The geometric transformations are reflection, reflection followed by expansion, contraction and multiple contraction. Using these transformations the simplex moves through the space towards the minimum, where it contracts itself.
After each iteration, the best vertex is returned. Note, that due to the nature of the algorithm not every step improves the current best parameter vector. Usually several iterations are required.
The minimizer-specific characteristic size is calculated as the
average distance from the geometrical center of the simplex to all its
vertices. This size can be used as a stopping criteria, as the
simplex contracts itself near the minimum. The size is returned by the
nmsimplex2 version of this minimiser is a new O(N) operations
implementation of the earlier O(N^2) operations
minimiser. It uses the same underlying algorithm, but the simplex
updates are computed more efficiently for high-dimensional problems.
In addition, the size of simplex is calculated as the RMS
distance of each vertex from the center rather than the mean distance,
allowing a linear update of this quantity on each step. The memory usage is
O(N^2) for both algorithms.
This method is a variant of
nmsimplex2 which initialises the
simplex around the starting point x using a randomly-oriented
set of basis vectors instead of the fixed coordinate axes. The
final dimensions of the simplex are scaled along the coordinate axes by the
vector step_size. The randomisation uses a simple deterministic
generator so that repeated calls to
a given solver object will vary the orientation in a well-defined way.