View source: R/testfunctions.R
griewank | R Documentation |
Griewank function is defined by
f_{\rm griewank}(x_1, ..., x_d) = \sum_{k = 1}^{d} \frac{x_k^2}{4000} - \prod_{k = 1}^d \cos\left(\frac{x_k}{\sqrt{k}}\right) + 1
with x_k \in [-600, 600]
for k = 1, ..., d
.
griewank(x)
griewankGrad(x)
x |
a numeric |
The gradient of Griewank function is
\nabla f_{\rm griewank}(x_1, ..., x_d) = \begin{pmatrix} \frac{x_1}{2000} + \frac{1}{\sqrt{1}} \sin\left( \frac{x_1}{\sqrt{1}}\right) \prod_{k = 2}^d \cos\left(\frac{x_k}{\sqrt{k}}\right) \\ \vdots \\ \frac{x_d}{2000} + \frac{1}{\sqrt{d}} \sin\left( \frac{x_d}{\sqrt{d}}\right) \prod_{k = 1}^{d-1} \cos\left(\frac{x_k}{\sqrt{k}}\right) \end{pmatrix}.
Griewank function has one global minimum f_{\rm griewank}(x^{\star}) = 0
at x^{\star} = (0,\dots, 0)
.
griewank
returns the function value of Griewank function at x
.
griewankGrad
returns the gradient of Griewank function at x
.
Carmen van Meegen
Plevris, V. and Solorzano, G. (2022). A Collection of 30 Multidimensional Functions for Global Optimization Benchmarking. Data, 7(4):46. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/data7040046")}.
Surjanovic, S. and Bingham, D. (2013). Virtual Library of Simulation Experiments: Test Functions and Datasets. https://www.sfu.ca/~ssurjano/ (retrieved January 19, 2024).
tangents
for drawing tangent lines.
# 1-dimensional Griewank function with tangents
curve(griewank(x), from = -5, to = 5, n = 200)
x <- seq(-5, 5, length = 5)
y <- griewank(x)
dy <- griewankGrad(x)
tangents(x, y, dy, length = 2, lwd = 2, col = "red")
points(x, y, pch = 16)
# Contour plot of Griewank function
n.grid <- 50
x1 <- x2 <- seq(-5, 5, length.out = n.grid)
y <- outer(x1, x2, function(x1, x2) griewank(cbind(x1, x2)))
contour(x1, x2, y, xaxs = "i", yaxs = "i", nlevels = 25, xlab = "x1", ylab = "x2")
# Perspective plot of Griewank function
col.pal <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow",
"#FF7F00", "red", "#7F0000"))
colors <- col.pal(100)
y.facet.center <- (y[-1, -1] + y[-1, -n.grid] + y[-n.grid, -1] + y[-n.grid, -n.grid])/4
y.facet.range <- cut(y.facet.center, 100)
persp(x1, x2, y, phi = 30, theta = -315, expand = 0.75, ticktype = "detailed",
col = colors[y.facet.range])
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