Sigmoid problems are a class of optimization problems with the objective of maximizing the sum of multiple sigmoid functions. They are defined by their limits at negative and positive infinity. Similar to the unit step function the function approaches 1 as it approaches infinity and approaches -1 as it approaches negative infinity. Sigmoid functions are shaped like an “S”, having both a convex and concave portion. The concave attributes will make solving the problems require a non-linear optimization, increasing computational burden. Though sigmoid problems are harder to solve than ordinary convex programs, they have many useful real-world applications which have encouraged their development. Sigmoid problems have become especially useful in the creation of artificial neural networks that simulate learning. The functionality of sigmoid problems allow in statistical models and other artificial learning.

Last modified

• 11/30/2018

Creator

• Matthew Hantzmon

DOI

• https://doi.org/10.21985/N2444N

Keyword

• Signomial

Rights statement

• In Copyright