STEP ("Select the Easiest Point") is a scalar optimization algorithm that minimizes a function by halving intervals over the bounded space iteratively, each time selecting the interval with smallest "difficulty". The difficulty measure is curvature of x^2 function crossing the interval boundary points and touching the supposed (so-far-estimated) optimum; this curvature will be small for intervals that have boundary points near the optimum. The "smoother" the function, the better this works.
- http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=349896
- http://www.applied-mathematics.net/optimization/Step.pdf
For now, see the top of the step.py module for usage instructions
and some simple examples.
Experimental support for multi-variate optimization of linearly
separable functions is provided by ndstep.py and ndstep-seq.py.
A testing tool for multi-variate optimization using STEP is available
as the test.py script.