Online learning via congregational gradient descent
The paper studies a version of the stepwise gradient descent algorithm consisting in the update of a population of solutions. The algorithm allows the determination of the global minimum of the expected cost function and is suitable for a broad class of learning and optimization problems. An averaging analysis of the algorithm allows to study its behaviour in terms of an associated ordinary differential equation. The speed of convergence and the effect of different population sizes are also studied. Simulation examples are finally provided.
scientific article; zbMATH DE number 1109544
scientific article