Optimization

Concept

A core part of the nabs library is support for the optimization of various process variables using the bluesky scanning framework. The physics behind every use case may be wildly different, but the main idea is to have a set of plans that scan a “motor” until a specific criteria is observed on a separate “detector”. nabs contains three complete plans for different situations; maximize, minimize, and walk_to_target. Hopefully the intention behind each is self-evident based on the name.

There are numerous algorithms to locate extrema within a univariate function, each with their own strengths and weaknesses. To deal with this nabs takes inspiration from the scipy library allowing the user to select their chosen methodology as the function (or in our case create the plan). A brief description of available methods are described below.

Available Methods

Golden Section Search

method="golden"

Golden Section Search approaches finding extrema by reducing a range known to contain the extrema step by step. Every iteration selects a “probe” point within our known range and uses the result to reduce the search-space in one direction or the other. By cleverly choosing these search points based on the “golden ratio”, the region is reduced by the same factor regardless of the luck involved with choosing probe points.

Strengths	The algorithm has the benefit of converging in a known number of steps based on the size of the given search-space and the desired resolution. Also, because the methodolgy involves no fitting or gradient calculations it may be more robust against noise.
Weaknesses	The method assumes that the underlying functionality is unimodal. If there are any local extrema this method may not converge to the expected point. Finally, the motor is required to move bi-directionally, so if there are issues with backlash this method should not be used.