fnnls_reg: Fast Non-Negative Least Squares Regression

In nnsolve: Fast Non-Negative Least Squares

Fast Non-Negative Least Squares Regression

Description

Solves the NNLS problem min ||y - Xb||^2 subject to b >= 0 using the Fast Non-Negative Least Squares algorithm of Bro & de Jong (1997).

Usage

fnnls_reg(
  y,
  X,
  tol = 1e-06,
  max_iter = 1000,
  sum_to_constant = FALSE,
  constant = 1,
  lower_bound = FALSE,
  lb = 0
)

Arguments

y	A numeric vector of length n.
X	A numeric matrix of dimensions n x k.
tol	The convergence tolerance, default is 1e-6.
max_iter	The maximum number of iterations, default is 1000.
sum_to_constant	If TRUE all entries sum to 'constant', Default is FALSE.
constant	If sum_to_constant is TRUE, all entries sum to this number. The default value is 1.
lower_bound	If TRUE all entries bounded below by 'lb', otherwise they are nonnegative. The default value is FALSE.
lb	If lower_bound is TRUE all entries are bounded below by 'lb'. The default value is 0.

Value

A list with two elements:

• b: A non-negative numeric vector of length k with the estimated coefficients.

• mse: The mean squared error of the fitted model.

References

Bro, Rasmus & Jong, Sijmen. (1997). A Fast Non-negativity-constrained Least Squares Algorithm. Journal of Chemometrics. 11. 393-401. 10.1002/(SICI)1099-128X(199709/10)11:53.0.CO;2-L.

Examples

n <- 100
k <- 10
X <- matrix(rnorm(n * k), nrow = n, ncol = k)
true_b <- abs(rnorm(k))
y <- X %*% true_b + rnorm(n, sd = 0.1)
result <- fnnls_reg(y, X)
result$b
result$mse

nnsolve documentation built on April 12, 2026, 5:06 p.m.