Article ID: | iaor20084113 |
Country: | Brazil |
Volume: | 22 |
Issue: | 1 |
Start Page Number: | 37 |
End Page Number: | 59 |
Publication Date: | Jan 2002 |
Journal: | Pesquisa Operacional |
Authors: | Mattos R.S., Veiga A. |
Keywords: | entropy |
The entropy optimization principles MaxEnt of Jaynes and MinxEnt of Kullback can be applied in a variety of scientific fields. Both involve the constrained optimization of entropy measures, which are intrinsically nonlinear functions of probabilities. Since each is a non-linear programming problem, their solution depends on iterative search algorithms, and, in addition, the constraints that probabilities are non-negative and sum up to one restrict in a particular way the solution space. The paper presents in detail (with the aid of two flowcharts) a computer efficient implementation of those two principles in the linearly constrained case that makes a prior check for the existence of solution to the optimization problems. The authors also make available easy-to-use MatLab codes.