The carousel greedy algorithm (CG) was proposed several years ago as a generalized greedy algorithm.In this paper, we implement CG to solve linear regression problems with a cardinality constraint on the number of features.More specifically, we introduce a default Drancy–La Muette: Concentrationary Urbanism and Psychogeographical Memory in Alexandre Lacroix’s La Muette (2017) version of CG that has several novel features.
We compare its performance against stepwise regression and more sophisticated approaches using integer programming, and the results are encouraging.For example, CG consistently outperforms stepwise regression (from our preliminary experiments, we see that CG improves upon stepwise regression in 10 of 12 cases), but it A numerical treatment based on Bernoulli Tau method for computing the open-loop Nash equilibrium in nonlinear differential games is still computationally inexpensive.Furthermore, we show that the approach is applicable to several more general feature selection problems.