R Error: function returns NA at 3.07653253930756e+181 distance from x
R Error: function returns NA at 3.07653253930756e+181 distance from x
我正在使用 R 编程语言。
基于以下网站 https://www.stat.cmu.edu/~ryantibs/statcomp-F15/lectures/optimization.pdf 的代码（注意：由于某些原因，此网站无法在 Google Chrome 中打开 - 请尝试 Microsoft Edge Explorer），我正在尝试使用“梯度下降优化算法”优化（即找到最小值）函数：f(x) = x^3 - 2x - 5
我首先定义了我要优化的函数：
#define function to be optimized
func2 <- function(x) {
return( x[1]^3 - 2* x[1] - 5)
}
接下来，我定义了梯度下降优化算法的函数：
#load library
library(numDeriv)
#define gradient descent function
grad.descent = function(f, x0, max.iter=200, step.size=0.05,
stopping.deriv=0.01, ...) {
n = length(x0)
xmat = matrix(0,nrow=n,ncol=max.iter)
xmat[,1] = x0
for (k in 2:max.iter) {
# Calculate the gradient
grad.cur = grad(f,xmat[,k-1],...)
# Should we stop?
if (all(abs(grad.cur) < stopping.deriv)) {
k = k-1; break
}
# Move in the opposite direction of the grad
xmat[,k] = xmat[,k-1] - step.size * grad.cur
}
xmat = xmat[,1:k] # Trim
return(list(x=xmat[,k], xmat=xmat, k=k))
}
最后，我尝试优化了功能：
# I think this serves as an initialization value
x0 = c(-1.9)
#run gradient descent algorithm
gd = grad.descent(func2,x0,step.size=1/3)
问题：但是这个returns出现如下错误：
Error in grad.default(f, xmat[, k - 1], ...) :
function returns NA at 3.07653253930756e+181 distance from x.
有人可以告诉我我做错了什么吗？
谢谢！
如果你想强加边界，你可以这样做：
#load library
library(numDeriv)
#define gradient descent function
grad.descent = function(f, x0, max.iter=200, step.size=0.05,
stopping.deriv=0.01, boundaries = NULL, verbose = TRUE, ...) {
n = length(x0)
xmat = matrix(0,nrow=n,ncol=max.iter)
xmat[,1] = x0
for (k in 2:max.iter) {
if (verbose) message(paste(xmat[, k-1], collapse = ", "))
# Calculate the gradient
grad.cur = grad(f,xmat[,k-1],...)
# Should we stop?
if (all(abs(grad.cur) < stopping.deriv)) {
k = k-1; break
}
# Move in the opposite direction of the grad
xmat[,k] = xmat[,k-1] - step.size * grad.cur
if (!is.null(boundaries)) {
xmat[,k] <- ifelse(xmat[,k] < boundaries[1], boundaries[1], xmat[,k])
xmat[,k] <- ifelse(xmat[,k] > boundaries[2], boundaries[2], xmat[,k])
if (all(xmat[, k] == xmat[, k-1] | abs(grad.cur) < stopping.deriv))) break #stop if boundaries
}
}
xmat = xmat[,1:k, drop = FALSE] # Trim
return(list(x=xmat[,k], xmat=xmat, k=k))
}
# starting values
x0 = c(-1.9, 1.9)
#use functions that are actually vectorized
#if you want to use multiple starting values
f1 <- \(x) x^3 - 2* x - 5
grad.descent(f1,x0,step.size=1/3, boundaries = c(-5, 5))
f2 <- \(x) x^2
grad.descent(f2,x0,step.size=1/3, boundaries = c(-5, 5))
我正在使用 R 编程语言。
基于以下网站 https://www.stat.cmu.edu/~ryantibs/statcomp-F15/lectures/optimization.pdf 的代码（注意：由于某些原因，此网站无法在 Google Chrome 中打开 - 请尝试 Microsoft Edge Explorer），我正在尝试使用“梯度下降优化算法”优化（即找到最小值）函数：f(x) = x^3 - 2x - 5
我首先定义了我要优化的函数：
#define function to be optimized
func2 <- function(x) {
return( x[1]^3 - 2* x[1] - 5)
}
接下来，我定义了梯度下降优化算法的函数：
#load library
library(numDeriv)
#define gradient descent function
grad.descent = function(f, x0, max.iter=200, step.size=0.05,
stopping.deriv=0.01, ...) {
n = length(x0)
xmat = matrix(0,nrow=n,ncol=max.iter)
xmat[,1] = x0
for (k in 2:max.iter) {
# Calculate the gradient
grad.cur = grad(f,xmat[,k-1],...)
# Should we stop?
if (all(abs(grad.cur) < stopping.deriv)) {
k = k-1; break
}
# Move in the opposite direction of the grad
xmat[,k] = xmat[,k-1] - step.size * grad.cur
}
xmat = xmat[,1:k] # Trim
return(list(x=xmat[,k], xmat=xmat, k=k))
}
最后，我尝试优化了功能：
# I think this serves as an initialization value
x0 = c(-1.9)
#run gradient descent algorithm
gd = grad.descent(func2,x0,step.size=1/3)
问题：但是这个returns出现如下错误：
Error in grad.default(f, xmat[, k - 1], ...) :
function returns NA at 3.07653253930756e+181 distance from x.
有人可以告诉我我做错了什么吗？
谢谢！
如果你想强加边界，你可以这样做：
#load library
library(numDeriv)
#define gradient descent function
grad.descent = function(f, x0, max.iter=200, step.size=0.05,
stopping.deriv=0.01, boundaries = NULL, verbose = TRUE, ...) {
n = length(x0)
xmat = matrix(0,nrow=n,ncol=max.iter)
xmat[,1] = x0
for (k in 2:max.iter) {
if (verbose) message(paste(xmat[, k-1], collapse = ", "))
# Calculate the gradient
grad.cur = grad(f,xmat[,k-1],...)
# Should we stop?
if (all(abs(grad.cur) < stopping.deriv)) {
k = k-1; break
}
# Move in the opposite direction of the grad
xmat[,k] = xmat[,k-1] - step.size * grad.cur
if (!is.null(boundaries)) {
xmat[,k] <- ifelse(xmat[,k] < boundaries[1], boundaries[1], xmat[,k])
xmat[,k] <- ifelse(xmat[,k] > boundaries[2], boundaries[2], xmat[,k])
if (all(xmat[, k] == xmat[, k-1] | abs(grad.cur) < stopping.deriv))) break #stop if boundaries
}
}
xmat = xmat[,1:k, drop = FALSE] # Trim
return(list(x=xmat[,k], xmat=xmat, k=k))
}
# starting values
x0 = c(-1.9, 1.9)
#use functions that are actually vectorized
#if you want to use multiple starting values
f1 <- \(x) x^3 - 2* x - 5
grad.descent(f1,x0,step.size=1/3, boundaries = c(-5, 5))
f2 <- \(x) x^2
grad.descent(f2,x0,step.size=1/3, boundaries = c(-5, 5))