The entropy optimization principles MaxEnt of Jaynes (1957a,b) and MinxEnt of Kullback (1959) can be applied in a variety of scientific fields. Both involve the constrained optimization of entropy measures, which are intrinsically non-linear functions of probabilities. Since each is a non-linear programming problem, their solution depend 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<FONT FACE=Symbol>â</FONT> codes.
entropy optimization; Shannon's measure; Kullback's measure
