An active set approach to minimizing objective in Unlinked Monotone Regression
UMRactiveSet(
yy,
grad,
CC_SIR,
init,
counts = rep(1, length(init)),
stepsize,
MM,
tol_end = 1e-04,
tol_collapse,
printevery,
filename
)Y (response) observation vector (numeric)
a function(yy, mm) where mm is the previous iterate value (i.e., the estimate vector).
A curvature function object (denoted "C" in the paper). See CC_SIR_generic() and examples.
Initial value of estimate ('mm'). Vector, length may be different than length(yy). See 'counts' input.
Together 'init' and 'counts' serve as the initialization; the implied initial vector is rep.int(init, counts).
Gradient descent stepsize.
A number of iterations. May not use them all. MM is not exactly the total number of iterations used in the sense that within each of MM iterations, we will possibly run another algorithm which may take up to MM iterations (but usually takes many fewer).
Used as tolerance at various points . Generally algorithm (and some subalgorithms) end once sum(abs(mm-mmprev)) < tol, or you hit MM iterations.
Collapsing roughly equal mm values into each other.
integer value (generally << MM). Every 'printevery' iterations, a count will be printed and the output saved.
filename (path) to save output to.
param ww_y Weights (nonnegative, sum to 1) corresponding to yy. Same length as yy.
Uses first order (gradient) for optimization, and uses certain second derivative computations to leave saddle points. See Balabdaoui, Doss, and Durot (20xx). Note that yy and mm (i.e., number covariates) may have different length.