In aursiber/fitdistrplus: Help to Fit of a Parametric Distribution to Non-Censored or Censored Data

knitr::opts_chunk$set(echo = TRUE)

The NNLS problem

Consider the non-negative least square problem with a sum constraint. $$ \min_{x} ||A x - b||^2, \text{ such that } =c, \forall i, x_i\geq0, $$ where $A\in\mathbb R^{m\times n}$, $b\in\mathbb R^{m}$ and $c\in\mathbb R^{n}$.

Reformulation by integrating the sum constraint

Let define the $$ \theta = \left(\begin{matrix} x_1\ \vdots\ x_{n-1} \end{matrix}\right) \Rightarrow x_\theta= \left(\begin{matrix} x_1\ \vdots\ x_{n-1} \ c-<1,\theta> \end{matrix}\right) =\left(\begin{matrix} \theta \ c-<1,\theta> \end{matrix}\right). $$ The product matrix at that point is $$ A x_\theta = \left(\begin{matrix} A_{1,1} & \dots & A_{1,n-1} & A_{1,n} \ &\vdots \ A_{m,1} & \dots & A_{m,n-1} & A_{m,n} \end{matrix}\right) \left(\begin{matrix} x_1\ \vdots\ x_{n-1} \ c-<1,\theta> \end{matrix}\right) = \left(\begin{matrix} A_{1,1}x_1 + \dots + A_{1,n-1}x_{n-1} + A_{1,n}(c-<1,\theta>) \ \vdots \ A_{m,1}x_1 + \dots + A_{m,n-1} + A_{m,n}(c-<1,\theta>) \end{matrix}\right) $$ $$ = A_{,\leq n-1}\theta + A_{,n} (c-<1,\theta>) $$ where $A=(A_{,\leq n-1}, A_{,n})$.

So we define the objective function $$ F_c(\theta) = ||A x_\theta - b||^2 = (A_{,\leq n-1}\theta + A_{,n} (c-<1,\theta>) - b)^T(A_{,\leq n-1}\theta + A_{,n} (c-<1,\theta>) - b) $$

Let us compute the derivative for $i\leq n-1$ $$ \frac{\partial (A x_\theta - b)}{\partial \theta_i} = \frac{\partial (A_{,\leq n-1}\theta ))}{\partial \theta_i} + \frac{\partial (A_{,n} (c-<1,\theta>))}{\partial \theta_i} = A_{,i} - A_{,n}. $$ The derivative for $F_c$ is $i\leq n-1$ $$ \frac{\partial F_c(\theta)}{\partial \theta_i} =2(A_{,i} - A_{,n})^T (A_{,\leq n-1}\theta + A_{,n} (c-<1,\theta>) - b). $$ So the gradient for $F_c$ is $$ \nabla F_c(\theta) =2(A_{,\leq n-1} - A_{,n})^T (A_{,\leq n-1}\theta + A_{,n} (c-<1,\theta>) - b). $$

Numerical check

Quick check on R

Mc <- function(theta, a, b, sumtotal)
{
  x <- c(theta, sumtotal-sum(theta))
  a %*% x - b
}
gradMc <- function(i, a, b, sumtotal)
{
  a[, i] - a[, NCOL(a)]
}

Fc <- function(theta, a, b, sumtotal)
{
  x <- c(theta, sumtotal-sum(theta))
  sum((a %*% x - b)^2)
}
gradFc <- function(theta, i, a, b, sumtotal)
{
  x <- c(theta, sumtotal-sum(theta))
  diffa <- a[, i] - a[, NCOL(a)]
  y <- a %*% x - b
  2*crossprod(diffa, y)
}

fullgradFc <- function(theta, a, b, sumtotal)
{
  x <- c(theta, sumtotal-sum(theta))
  sub_a <- a[, 1:(NCOL(a)-1)]
  diffa <- sub_a - a[, NCOL(a)]
  y <- a %*% x - b
  2*crossprod(diffa, y)
}


a <- matrix(1:12, 4, 3)
b <- 1:4/4

theta1 <- 1:2
theta2 <- theta1+c(0,1e-3)
(Mc(theta1, a, b, 1) - Mc(theta2, a, b, 1))/-1e-3

gradMc(2, a, b, 1)

gradFc(theta1, 2, a, b, 1)
(Fc(theta1, a, b, 1) - Fc(theta2, a, b, 1))/-1e-3

fullgradFc(theta1, a, b, 1)
sapply(1:2, function(i) gradFc(theta1, i, a, b, 1))

aursiber/fitdistrplus documentation built on June 15, 2025, 6:24 a.m.