in the paper, Best Subset Selection via a Modern Optimization Lens, it mentioned that adding additional bounds on the coefficients leads to improved performance of the MIO in Equation (9).
In the code file (master/bestsubset/R/bs.R) it shows the bound on the beta is calculated by this:
bigm = 2*max(abs(best.beta))
where best.beta is from the projected gradient method. But I felt that this is not exactly the same to the description in the Section 2.3.1 in the paper,
in the paper, Best Subset Selection via a Modern Optimization Lens, it mentioned that adding additional bounds on the coefficients leads to improved performance of the MIO in Equation (9).
In the code file (master/bestsubset/R/bs.R) it shows the bound on the beta is calculated by this:
where best.beta is from the projected gradient method. But I felt that this is not exactly the same to the description in the Section 2.3.1 in the paper,