| Title: | Discrete and Global Optimization Routines |
|---|---|
| Description: | The R package 'adagio' will provide methods and algorithms for (discrete) optimization, e.g. knapsack and subset sum procedures, derivative-free Nelder-Mead and Hooke-Jeeves minimization, and some (evolutionary) global optimization functions. |
| Authors: | Hans W. Borchers [aut, cre] |
| Maintainer: | Hans W. Borchers <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.9.2 |
| Built: | 2024-10-31 20:49:41 UTC |
| Source: | CRAN |
Linear (sum) assignment problem, or LSAP.
assignment(cmat, dir = "min")assignment(cmat, dir = "min")
cmat |
quadratic (numeric) matrix, the cost matrix. |
dir |
direction, can be "min" or "max". |
Solves the linear (sum) assignment problem for quadratic matrices.
Uses the lp.assign function from the lpSolve package,
that is it solves LSAP as a mixed integer linear programming problem.
List with components perm, the permutation that defines the
minimum solution, min, the minimum value, and err is
always 0, i.e. not used at the moment.
Slower than the Hungarian algorithm in package clue.
Burkard, R., M. Dell'Amico, and S. Martello (2009). Assignment Problems. Society for Industrial and Applied Mathematics (SIAM).
Martello, S., and P. Toth (1990). Knapsack Problems: Algorithms and Computer Implementations. John Wiley & Sons, Ltd.
clue::solve_LSAP
## Example similar to clue::solve_LSAP set.seed(8237) x <- matrix(sample(1:100), nrow = 10) y <- assignment(x) # show permutation and check minimum sum y$perm # 7 6 10 5 8 2 1 4 9 3 y$min # 173 z <- cbind(1:10, y$perm) x[z] # 16 9 49 6 17 14 1 44 10 7 y$min == sum(x[z]) # TRUE ## Not run: ## Example: minimize sum of distances of complex points n <- 100 x <- rt(n, df=3) + 1i * rt(n, df=3) y <- runif(n) + 1i * runif(n) cmat <- round(outer(x, y, FUN = function(x,y) Mod(x - y)), 2) system.time(T1 <- assignment(cmat)) # elapsed: 0.003 T1$min / 100 # 145.75 ## Hungarian algorithm in package 'clue' library("clue") system.time(T2 <- solve_LSAP(cmat)) # elapsed: 0.014 sum(cmat[cbind(1:n, T2)]) # 145.75 ## End(Not run)## Example similar to clue::solve_LSAP set.seed(8237) x <- matrix(sample(1:100), nrow = 10) y <- assignment(x) # show permutation and check minimum sum y$perm # 7 6 10 5 8 2 1 4 9 3 y$min # 173 z <- cbind(1:10, y$perm) x[z] # 16 9 49 6 17 14 1 44 10 7 y$min == sum(x[z]) # TRUE ## Not run: ## Example: minimize sum of distances of complex points n <- 100 x <- rt(n, df=3) + 1i * rt(n, df=3) y <- runif(n) + 1i * runif(n) cmat <- round(outer(x, y, FUN = function(x,y) Mod(x - y)), 2) system.time(T1 <- assignment(cmat)) # elapsed: 0.003 T1$min / 100 # 145.75 ## Hungarian algorithm in package 'clue' library("clue") system.time(T2 <- solve_LSAP(cmat)) # elapsed: 0.014 sum(cmat[cbind(1:n, T2)]) # 145.75 ## End(Not run)
Solves the Bin Packing problem approximately.
bpp_approx(S, cap, method = c("firstfit", "bestfit", "worstfit"))bpp_approx(S, cap, method = c("firstfit", "bestfit", "worstfit"))
S |
vector of weights (or sizes) of items. |
cap |
same capacity for all the bins. |
method |
which approximate method to use. |
Solves approximately the Bin Packing problem for numeric weights and bins, all having the same volume.
Possible methods are "firstfit", "bestfit", and "worstfit". "firstfit" tries to place each item as early as possible, "bestfit" such that the remaining space in the bin is as small as possible, and "worstfit" such that the remaining space is as big as possible.
Best results are achieved with the "bestfit" method. "firstfit" may be a reasonable alternative. For smaller and medium-sized data the approximate results will come quite close to the exact solution, see the examples.
In general, the results are much better if the items in S are sorted
decreasingly. If they are not, an immediate warning is issued.
A list of the following components:
nbins |
minimum number of bins. |
xbins |
index of the bin each item is assigned to. |
sbins |
sum of item sizes in each bin. |
filled |
total volume filled in the bins (as percentage). |
The Bin Packing problem can be solved as a Linear Program. The formulation is a bit tricky, and it turned out 'lpSolve' does not solve medium-sized problems in acceptable time. (Tests with 'Rglpk' will follow.)
Hans W. Borchers
Silvano Martello. "Bin packing problems". In: 23rd Belgian Mathematical Optimization Workshop, La-Roche-en-Ardennes 2019.
Function binpacking in package 'knapsack' (on R-Forge).
## (1) S <- c(50, 3, 48, 53, 53, 4, 3, 41, 23, 20, 52, 49) cap <- 100 bpp_approx(S, cap, method = "bestfit") ## exact -- $nbins 4, filled 99.75 % ## firstfit -- $nbins 6, filled 66.5 % ## bestfit -- $nbins 5, filled 79.8 % ## ! when decreasingly sorted, 'bestfit' with nbins = 4 ## (2) S <- c(100,99,89,88,87,75,67,65,65,57,57,49,47,31,27,18,13,9,8,1) cap <- 100 bpp_approx(S, cap, method = "firstfit") # firstfit: 12 bins; exact: 12 bins ## Not run: ## (3) S <- c(99,99,96,96,92,92,91,88,87,86, 85,76,74,72,69,67,67,62,61,56, 52,51,49,46,44,42,40,40,33,33, 30,30,29,28,28,27,25,24,23,22, 21,20,17,14,13,11,10, 7, 7, 3) cap <- 100 bpp_approx(S, cap) # exact: 25; firstfit: 25; bestfit: 25 nbins ## (4) # 20 no.s in 1..100, capacity 100 set.seed(7013) S <- sample(1:100, 20, replace = TRUE) cap <- 100 bpp_approx(sort(S, decreasing = TRUE), cap, method = "bestfit") # exact: 12 bins; firstfit and bestfit: 13; worstfit: 14 bins ## End(Not run)## (1) S <- c(50, 3, 48, 53, 53, 4, 3, 41, 23, 20, 52, 49) cap <- 100 bpp_approx(S, cap, method = "bestfit") ## exact -- $nbins 4, filled 99.75 % ## firstfit -- $nbins 6, filled 66.5 % ## bestfit -- $nbins 5, filled 79.8 % ## ! when decreasingly sorted, 'bestfit' with nbins = 4 ## (2) S <- c(100,99,89,88,87,75,67,65,65,57,57,49,47,31,27,18,13,9,8,1) cap <- 100 bpp_approx(S, cap, method = "firstfit") # firstfit: 12 bins; exact: 12 bins ## Not run: ## (3) S <- c(99,99,96,96,92,92,91,88,87,86, 85,76,74,72,69,67,67,62,61,56, 52,51,49,46,44,42,40,40,33,33, 30,30,29,28,28,27,25,24,23,22, 21,20,17,14,13,11,10, 7, 7, 3) cap <- 100 bpp_approx(S, cap) # exact: 25; firstfit: 25; bestfit: 25 nbins ## (4) # 20 no.s in 1..100, capacity 100 set.seed(7013) S <- sample(1:100, 20, replace = TRUE) cap <- 100 bpp_approx(sort(S, decreasing = TRUE), cap, method = "bestfit") # exact: 12 bins; firstfit and bestfit: 13; worstfit: 14 bins ## End(Not run)
Solves the Change Making problem as an integer linear program.
change_making(items, value)change_making(items, value)
items |
vector of integer numbers greater than zero. |
value |
integer number |
The Change Making problem attempts to find a minimal combination of items that sum up to a given value. If the items are distinct positive integers, the solution is unique.
If the problem is infeasible, i.e. there is no such combination, the
returned count is 0.
The problem is treated as an Integer Linear Program (ILP) and solved
with the lp solver in lpSolve.
Returns a list with components count, the number of items used to
sum up to the value, and solution, the number of items used
per item.
See the Wikipedia article on the "change making problem".
items = c(2, 5, 10, 50, 100) value = 999 change_making(items, value) ## Not run: solutions <- numeric(20) for (m in 1:20) { sol <- change_making(items, m) solutions[m] <- sol$count } solutions #> [1] 0 1 0 2 1 3 2 4 3 1 4 2 5 3 2 4 3 5 4 2 ## End(Not run)items = c(2, 5, 10, 50, 100) value = 999 change_making(items, value) ## Not run: solutions <- numeric(20) for (m in 1:20) { sol <- change_making(items, m) solutions[m] <- sol$count } solutions #> [1] 0 1 0 2 1 3 2 4 3 1 4 2 5 3 2 4 3 5 4 2 ## End(Not run)
The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is an evolutionary algorithm for difficult non-linear non-convex optimization problems in continuous domain. The CMA-ES is typically applied to unconstrained or bounded constraint optimization problems, and search space dimensions between three and fifty.
pureCMAES(par, fun, lower = NULL, upper = NULL, sigma = 0.5, stopfitness = -Inf, stopeval = 1000*length(par)^2, ...)pureCMAES(par, fun, lower = NULL, upper = NULL, sigma = 0.5, stopfitness = -Inf, stopeval = 1000*length(par)^2, ...)
par |
objective variables initial point. |
fun |
objective/target/fitness function. |
lower, upper
|
lower and upper bounds for the parameters. |
sigma |
coordinate wise standard deviation (step size). |
stopfitness |
stop if fitness < stopfitness (minimization). |
stopeval |
stop after stopeval number of function evaluations |
... |
additional parameters to be passed to the function. |
The CMA-ES implements a stochastic variable-metric method. In the very particular case of a convex-quadratic objective function the covariance matrix adapts to the inverse of the Hessian matrix, up to a scalar factor and small random fluctuations. The update equations for mean and covariance matrix maximize a likelihood while resembling an expectation-maximization algorithm.
Returns a list with components xmin and fmin.
Be patient; for difficult problems or high dimensions the function may run for several minutes; avoid problem dimensions of 30 and more!
There are other implementations of Hansen's CMAES in package ‘cmaes’ (simplified form) and in package ‘parma’ as cmaes() (extended form).
Copyright (c) 2003-2010 Nikolas Hansen for Matlab code PURECMAES; converted to R by Hans W Borchers. (Hansen's homepage: www.cmap.polytechnique.fr/~nikolaus.hansen/)
Hansen, N. (2011). The CMA Evolution Strategy: A Tutorial.
https://arxiv.org/abs/1604.00772
Hansen, N., D.V. Arnold, and A. Auger (2013). Evolution Strategies. J. Kacprzyk and W. Pedrycz (Eds.). Handbook of Computational Intelligence, Springer-Verlag, 2015.
cmaes::cmaes, parma::cmaes
## Not run: ## Polynomial minimax approximation of data points ## (see the Remez algorithm) n <- 10; m <- 101 # polynomial of degree 10; no. of data points xi <- seq(-1, 1, length = m) yi <- 1 / (1 + (5*xi)^2) # Runge's function pval <- function(p, x) # Horner scheme outer(x, (length(p) - 1):0, "^") %*% p pfit <- function(x, y, n) # polynomial fitting of degree n qr.solve(outer(x, seq(n, 0), "^"), y) fn1 <- function(p) # objective function max(abs(pval(p, xi) - yi)) pf <- pfit(xi, yi, 10) # start with a least-squares fitting sol1 <- pureCMAES(pf, fn1, rep(-200, 11), rep(200, 11)) zapsmall(sol1$xmin) # [1] -50.24826 0.00000 135.85352 0.00000 -134.20107 0.00000 # [7] 59.19315 0.00000 -11.55888 0.00000 0.93453 print(sol1$fmin, digits = 10) # [1] 0.06546780411 ## Polynomial fitting in the L1 norm ## (or use LP or IRLS approaches) fn2 <- function(p) sum(abs(pval(p, xi) - yi)) sol2 <- pureCMAES(pf, fn2, rep(-100, 11), rep(100, 11)) zapsmall(sol2$xmin) # [1] -21.93238 0.00000 62.91083 0.00000 -67.84847 0.00000 # [7] 34.14398 0.00000 -8.11899 0.00000 0.84533 print(sol2$fmin, digits = 10) # [1] 3.061810639 ## End(Not run)## Not run: ## Polynomial minimax approximation of data points ## (see the Remez algorithm) n <- 10; m <- 101 # polynomial of degree 10; no. of data points xi <- seq(-1, 1, length = m) yi <- 1 / (1 + (5*xi)^2) # Runge's function pval <- function(p, x) # Horner scheme outer(x, (length(p) - 1):0, "^") %*% p pfit <- function(x, y, n) # polynomial fitting of degree n qr.solve(outer(x, seq(n, 0), "^"), y) fn1 <- function(p) # objective function max(abs(pval(p, xi) - yi)) pf <- pfit(xi, yi, 10) # start with a least-squares fitting sol1 <- pureCMAES(pf, fn1, rep(-200, 11), rep(200, 11)) zapsmall(sol1$xmin) # [1] -50.24826 0.00000 135.85352 0.00000 -134.20107 0.00000 # [7] 59.19315 0.00000 -11.55888 0.00000 0.93453 print(sol1$fmin, digits = 10) # [1] 0.06546780411 ## Polynomial fitting in the L1 norm ## (or use LP or IRLS approaches) fn2 <- function(p) sum(abs(pval(p, xi) - yi)) sol2 <- pureCMAES(pf, fn2, rep(-100, 11), rep(100, 11)) zapsmall(sol2$xmin) # [1] -21.93238 0.00000 62.91083 0.00000 -67.84847 0.00000 # [7] 34.14398 0.00000 -8.11899 0.00000 0.84533 print(sol2$fmin, digits = 10) # [1] 3.061810639 ## End(Not run)
Visualizes multivariate functions around a point or along a line between
two points in R^n.
fminviz(fn, x0, nlines = 2*length(x0), npoints = 51, scaled = 1.0) flineviz(fn, x1, x2, npoints = 51, scaled = 0.1)fminviz(fn, x0, nlines = 2*length(x0), npoints = 51, scaled = 1.0) flineviz(fn, x1, x2, npoints = 51, scaled = 0.1)
fn |
multivariate function to be visualized. |
x0, x1, x2
|
points in n-dimensional space. |
nlines |
number of lines to plot. |
npoints |
number of points used to plot a line. |
scaled |
scale factor to extend the line(s). |
fminviz vizualizes the behavior of a multivariate function fn
around a point x0. It randomly selects nlines lines through
x0 in R^n and draws the curves of the function along these
lines in one graph.
Curves that have at least one point below fn(x0) are drawn in red,
all others in blue. The scale on the x-axis is the Euclidean distance in
R^n. The scale factor can change it.
flineviz vizualizes the behavior of a multivariate function fn
along the straight line between the points x1 and x2. Points
x1 and x2 are also plotted.
Plots a line graph and returns NULL (invisibly).
## Not run: f1 <- function(x) x[1]^2 - x[2]^2 fminviz(f1, c(0, 0), nlines = 10) f2 <- function(x) (1 - x[1])^2 + 100*(x[2] - x[1]^2)^2 flineviz(f2, c(0, 0), c(1, 1)) ## End(Not run)## Not run: f1 <- function(x) x[1]^2 - x[2]^2 fminviz(f1, c(0, 0), nlines = 10) f2 <- function(x) (1 - x[1])^2 + 100*(x[2] - x[1]^2)^2 flineviz(f2, c(0, 0), c(1, 1)) ## End(Not run)
A Hamiltionian path or cycle (a.k.a. Hamiltonian circuit) is a path through a graph that visits each vertex exactly once, resp. a closed path through the graph.
hamiltonian(edges, start = 1, cycle = TRUE)hamiltonian(edges, start = 1, cycle = TRUE)
edges |
an edge list describing an undirected graph. |
start |
vertex number to start the path or cycle. |
cycle |
Boolean, should a path or a full cycle be found. |
hamiltonian() applies a backtracking algorithm that is relatively
efficient for graphs of up to 30–40 vertices. The edge list is first
transformed to a list where the i-th component contains the list
of all vertices connected to vertex i.
The edge list must be of the form c(v1, v2, v3, v2, ...) meaning
that there are edges v1 --> v2, v3 --> v4, etc., connecting these
vertices. Therefore, an edge list has an even number of entries.
If the function returns NULL, there is no Hamiltonian path or
cycle. The function does not check if the graph is connected or not. And
if cycle = TRUE is used, then there also exists an edge from the
last to the first entry in the resulting path.
Ifa Hamiltonian cycle exists in the graph it will be found whatever the starting vertex was. For a Hamiltonian path this is different and a successful search may very well depend on the start.
Returns a vector containing vertex number of a valid path or cycle,
or NULL if no path or cycle has been found (i.e., does not exist);
If a cycle was requested, there exists an edge from the last to the first
vertex in this list of edges.
See the igraph package for more information about handling graphs and
defining them through edge lists or other constructs.
Hans W. Borchers
Papadimitriou, Ch. H., and K. Steiglitz (1998). Optimization Problems: Algorithms and Complexity. Prentice-Hall/Dover Publications.
Package igraph
## Dodekaeder graph D20_edges <- c( 1, 2, 1, 5, 1, 6, 2, 3, 2, 8, 3, 4, 3, 10, 4, 5, 4, 12, 5, 14, 6, 7, 6, 15, 7, 8, 7, 16, 8, 9, 9, 10, 9, 17, 10, 11, 11, 12, 11, 18, 12, 13, 13, 14, 13, 19, 14, 15, 15, 20, 16, 17, 16, 20, 17, 18, 18, 19, 19, 20) hamiltonian(D20_edges, cycle = TRUE) # [1] 1 2 3 4 5 14 13 12 11 10 9 8 7 16 17 18 19 20 15 6 hamiltonian(D20_edges, cycle = FALSE) # [1] 1 2 3 4 5 14 13 12 11 10 9 8 7 6 15 20 16 17 18 19 ## Herschel graph # The Herschel graph the smallest non-Hamiltonian polyhedral graph. H11_edges <- c( 1, 2, 1, 8, 1, 9, 1, 10, 2, 3, 2, 11, 3, 4, 3, 9, 4, 5, 4, 11, 5, 6, 5, 9, 5, 10, 6, 7, 6, 11, 7, 8, 7, 10, 8, 11) hamiltonian(H11_edges, cycle = FALSE) # NULL ## Not run: ## Example: Graph constructed from squares N <- 45 # 23, 32, 45 Q <- (2:trunc(sqrt(2*N-1)))^2 sq_edges <- c() for (i in 1:(N-1)) { for (j in (i+1):N) { if ((i+j) sq_edges <- c(sq_edges, i, j) } } require(igraph) sq_graph <- make_graph(sq_edges, directed=FALSE) plot(sq_graph) if (N == 23) { # does not find a path with start=1 ... hamiltonian(sq_edges, start=18, cycle=FALSE) # hamiltonian(sq_edges) # NULL } else if (N == 32) { # the first of these graphs that is Hamiltonian ... # hamiltonian(sq_edges, cycle=FALSE) hamiltonian(sq_edges) } else if (N == 45) { # takes much too long ... # hamiltonian(sq_edges, cycle=FALSE) hamiltonian(sq_edges) } ## End(Not run)## Dodekaeder graph D20_edges <- c( 1, 2, 1, 5, 1, 6, 2, 3, 2, 8, 3, 4, 3, 10, 4, 5, 4, 12, 5, 14, 6, 7, 6, 15, 7, 8, 7, 16, 8, 9, 9, 10, 9, 17, 10, 11, 11, 12, 11, 18, 12, 13, 13, 14, 13, 19, 14, 15, 15, 20, 16, 17, 16, 20, 17, 18, 18, 19, 19, 20) hamiltonian(D20_edges, cycle = TRUE) # [1] 1 2 3 4 5 14 13 12 11 10 9 8 7 16 17 18 19 20 15 6 hamiltonian(D20_edges, cycle = FALSE) # [1] 1 2 3 4 5 14 13 12 11 10 9 8 7 6 15 20 16 17 18 19 ## Herschel graph # The Herschel graph the smallest non-Hamiltonian polyhedral graph. H11_edges <- c( 1, 2, 1, 8, 1, 9, 1, 10, 2, 3, 2, 11, 3, 4, 3, 9, 4, 5, 4, 11, 5, 6, 5, 9, 5, 10, 6, 7, 6, 11, 7, 8, 7, 10, 8, 11) hamiltonian(H11_edges, cycle = FALSE) # NULL ## Not run: ## Example: Graph constructed from squares N <- 45 # 23, 32, 45 Q <- (2:trunc(sqrt(2*N-1)))^2 sq_edges <- c() for (i in 1:(N-1)) { for (j in (i+1):N) { if ((i+j) sq_edges <- c(sq_edges, i, j) } } require(igraph) sq_graph <- make_graph(sq_edges, directed=FALSE) plot(sq_graph) if (N == 23) { # does not find a path with start=1 ... hamiltonian(sq_edges, start=18, cycle=FALSE) # hamiltonian(sq_edges) # NULL } else if (N == 32) { # the first of these graphs that is Hamiltonian ... # hamiltonian(sq_edges, cycle=FALSE) hamiltonian(sq_edges) } else if (N == 45) { # takes much too long ... # hamiltonian(sq_edges, cycle=FALSE) hamiltonian(sq_edges) } ## End(Not run)
Provides storage for function calls.
Historize(fun, len = 0, ...)Historize(fun, len = 0, ...)
fun |
Function of one or several variables. |
len |
If > 0, input values will be stored, too. |
... |
Additional parameters to be handed to the function. |
Historize() adds storage to the function. If len=0 then only
function values will be stored in a vector, if len>0 then
variables will be stored in a vector, and function values will be stored
in a matrix, one line for every function call.
If Fn = Historize(...) is the 'historized' function, then the
storage can be retrieved with Fn(), and can be cleared with
Fn(NULL).
Filling the storage will take extra time and can slow down the
function calls. Especially also storing the variables used in the
call (with len>0) will make it considerably slower.
Functions can have multivariate output; the user is asked to take care of handling the output vector or matrix correctly. The function may even require additional parameters.
Returns a list, $input the input variables as matrix, $H2 the function values as vector, $nvars the number of input variables of the function, and $ncalls the number or recorded function calls.
Can also be applied to functions that output a vector (same length for every call).
Hans W. Borchers
trace
f <- function(x) sum(x^2) F <- Historize(f, len = 1) c( F(c(1,1)), F(c(1,2)), F(c(2,1)), F(c(2,2)) ) #> [1] 2 5 5 8 F() #> $input #> [,1] [,2] #> [1,] 1 1 #> [2,] 1 2 #> [3,] 2 1 #> [4,] 2 2 #> #> $values #> [1] 2 5 5 8 #> #> $nvars #> [1] 2 #> #> $ncalls #> [1] 4 F(NULL) # reset memory ## Rastrigin under Differential Evolution Fn <- Historize(fnRastrigin) dm <- 10 lb <- rep(-5.2, dm); ub <- -lb sol <- simpleDE(Fn, lower = lb, upper = ub) fvalues <- Fn()$values fvals <- cummin(fvalues) ## Not run: plot(fvalues, type = 'p', col = 7, pch = '.', cex = 2, main = "Simple DE optimization", xlab = '', ylab = '') lines(fvals, col = 2, lwd = 2) legend(length(fvalues), max(fvalues), c("Intermediate values", "cummulated min"), xjust = 1, col = c(7, 2), lwd = 2) grid() ## End(Not run)f <- function(x) sum(x^2) F <- Historize(f, len = 1) c( F(c(1,1)), F(c(1,2)), F(c(2,1)), F(c(2,2)) ) #> [1] 2 5 5 8 F() #> $input #> [,1] [,2] #> [1,] 1 1 #> [2,] 1 2 #> [3,] 2 1 #> [4,] 2 2 #> #> $values #> [1] 2 5 5 8 #> #> $nvars #> [1] 2 #> #> $ncalls #> [1] 4 F(NULL) # reset memory ## Rastrigin under Differential Evolution Fn <- Historize(fnRastrigin) dm <- 10 lb <- rep(-5.2, dm); ub <- -lb sol <- simpleDE(Fn, lower = lb, upper = ub) fvalues <- Fn()$values fvals <- cummin(fvalues) ## Not run: plot(fvalues, type = 'p', col = 7, pch = '.', cex = 2, main = "Simple DE optimization", xlab = '', ylab = '') lines(fvals, col = 2, lwd = 2) legend(length(fvalues), max(fvalues), c("Intermediate values", "cummulated min"), xjust = 1, col = c(7, 2), lwd = 2) grid() ## End(Not run)
An implementation of the Hooke-Jeeves algorithm for derivative-free optimization.
hookejeeves(x0, f, lb = NULL, ub = NULL, tol = 1e-08, target = Inf, maxfeval = Inf, info = FALSE, ...)hookejeeves(x0, f, lb = NULL, ub = NULL, tol = 1e-08, target = Inf, maxfeval = Inf, info = FALSE, ...)
x0 |
starting vector. |
f |
nonlinear function to be minimized. |
lb, ub
|
lower and upper bounds. |
tol |
relative tolerance, to be used as stopping rule. |
target |
iteration stops when this value is reached. |
maxfeval |
maximum number of allowed function evaluations. |
info |
logical, whether to print information during the main loop. |
... |
additional arguments to be passed to the function. |
This method computes a new point using the values of f at suitable
points along the orthogonal coordinate directions around the last point.
List with following components:
xmin |
minimum solution found so far. |
fmin |
value of |
fcalls |
number of function evaluations. |
niter |
number of iterations performed. |
Hooke-Jeeves is notorious for its number of function calls. Memoization is often suggested as a remedy.
For a similar implementation of Hooke-Jeeves see the ‘dfoptim’ package.
C.T. Kelley (1999), Iterative Methods for Optimization, SIAM.
Quarteroni, Sacco, and Saleri (2007), Numerical Mathematics, Springer-Verlag.
## Rosenbrock function rosenbrock <- function(x) { n <- length(x) x1 <- x[2:n] x2 <- x[1:(n-1)] sum(100*(x1-x2^2)^2 + (1-x2)^2) } hookejeeves(c(0,0,0,0), rosenbrock) # $xmin # [1] 1.000000 1.000001 1.000002 1.000004 # $fmin # [1] 4.774847e-12 # $fcalls # [1] 2499 # $niter #[1] 26 hookejeeves(rep(0,4), lb=rep(-1,4), ub=0.5, rosenbrock) # $xmin # [1] 0.50000000 0.26221320 0.07797602 0.00608027 # $fmin # [1] 1.667875 # $fcalls # [1] 571 # $niter # [1] 26## Rosenbrock function rosenbrock <- function(x) { n <- length(x) x1 <- x[2:n] x2 <- x[1:(n-1)] sum(100*(x1-x2^2)^2 + (1-x2)^2) } hookejeeves(c(0,0,0,0), rosenbrock) # $xmin # [1] 1.000000 1.000001 1.000002 1.000004 # $fmin # [1] 4.774847e-12 # $fcalls # [1] 2499 # $niter #[1] 26 hookejeeves(rep(0,4), lb=rep(-1,4), ub=0.5, rosenbrock) # $xmin # [1] 0.50000000 0.26221320 0.07797602 0.00608027 # $fmin # [1] 1.667875 # $fcalls # [1] 571 # $niter # [1] 26
Solves the 0-1 (binary) single knapsack problem.
knapsack(w, p, cap)knapsack(w, p, cap)
w |
integer vector of weights. |
p |
integer vector of profits. |
cap |
maximal capacity of the knapsack, integer too. |
knapsack solves the 0-1, or: binary, single knapsack problem by
using the dynamic programming approach. The problem can be formulated as:
Maximize sum(x*p) such that sum(x*w) <= cap, where x
is a vector with x[i] == 0 or 1.
Knapsack procedures can even solve subset sum problems, see the examples 3 and 3' below.
A list with components capacity, profit, and indices.
HwB email: <[email protected]>
Papadimitriou, C. H., and K. Steiglitz (1998). Combinatorial Optimization: Algorithms and Complexity. Dover Publications 1982, 1998.
Horowitz, E., and S. Sahni (1978). Fundamentals of Computer Algorithms. Computer Science Press, Rockville, ML.
knapsack::knapsack
# Example 1 p <- c(15, 100, 90, 60, 40, 15, 10, 1) w <- c( 2, 20, 20, 30, 40, 30, 60, 10) cap <- 102 (is <- knapsack(w, p, cap)) # [1] 1 2 3 4 6 , capacity 102 and total profit 280 ## Example 2 p <- c(70, 20, 39, 37, 7, 5, 10) w <- c(31, 10, 20, 19, 4, 3, 6) cap <- 50 (is <- knapsack(w, p, cap)) # [1] 1 4 , capacity 50 and total profit 107 ## Not run: ## Example 3: subset sum p <- seq(2, 44, by = 2)^2 w <- p is <- knapsack(w, p, 2012) p[is$indices] # 16 36 64 144 196 256 324 400 576 ## Example 3': maximize number of items # w <- seq(2, 44, by = 2)^2 # p <- numeric(22) + 1 # is <- knapsack(w, p, 2012) ## Example 4 from Rosetta Code: w = c( 9, 13, 153, 50, 15, 68, 27, 39, 23, 52, 11, 32, 24, 48, 73, 42, 43, 22, 7, 18, 4, 30) p = c(150, 35, 200, 160, 60, 45, 60, 40, 30, 10, 70, 30, 15, 10, 40, 70, 75, 80, 20, 12, 50, 10) cap = 400 system.time(is <- knapsack(w, p, cap)) # 0.001 sec ## End(Not run)# Example 1 p <- c(15, 100, 90, 60, 40, 15, 10, 1) w <- c( 2, 20, 20, 30, 40, 30, 60, 10) cap <- 102 (is <- knapsack(w, p, cap)) # [1] 1 2 3 4 6 , capacity 102 and total profit 280 ## Example 2 p <- c(70, 20, 39, 37, 7, 5, 10) w <- c(31, 10, 20, 19, 4, 3, 6) cap <- 50 (is <- knapsack(w, p, cap)) # [1] 1 4 , capacity 50 and total profit 107 ## Not run: ## Example 3: subset sum p <- seq(2, 44, by = 2)^2 w <- p is <- knapsack(w, p, 2012) p[is$indices] # 16 36 64 144 196 256 324 400 576 ## Example 3': maximize number of items # w <- seq(2, 44, by = 2)^2 # p <- numeric(22) + 1 # is <- knapsack(w, p, 2012) ## Example 4 from Rosetta Code: w = c( 9, 13, 153, 50, 15, 68, 27, 39, 23, 52, 11, 32, 24, 48, 73, 42, 43, 22, 7, 18, 4, 30) p = c(150, 35, 200, 160, 60, 45, 60, 40, 30, 10, 70, 30, 15, 10, 40, 70, 75, 80, 20, 12, 50, 10) cap = 400 system.time(is <- knapsack(w, p, cap)) # 0.001 sec ## End(Not run)
Find the largest/maximal empty rectangle, i.e. with largest area, not containing given points.
maxempty(x, y, ax = c(0, 1), ay = c(0, 1))maxempty(x, y, ax = c(0, 1), ay = c(0, 1))
x, y
|
coordinates of points to be avoided. |
ax, ay
|
left and right resp. lower and upper constraints. |
Find the largest or maximal empty two-dimensional rectangle in a rectangular area. The edges of this rectangle have to be parallel to the edges of the enclosing rectangle (and parallel to the coordinate axes). ‘Empty’ means that none of the points given are contained in the interior of the found rectangle.
List with area and rect the rectangle as a vector usable for
the rect graphics function.
The algorithm has a run-time of O(n^2) while there are run-times of
O(n*log(n)) reported in the literature, utilizing a more complex
data structure. I don't know of any comparable algorithms for the largest
empty circle problem.
HwB email: <[email protected]>
B. Chazelle, R. L. Drysdale, and D. T. Lee (1986). Computing the Largest Empty Rectangle. SIAM Journal of Computing, Vol. 15(1), pp. 300–315.
A. Naamad, D. T. Lee, and W.-L. Hsu (1984). On the Maximum Empty Rectangle Problem. Discrete Applied Mathematics, Vol. 8, pp. 267–277.
Hmisc::largest.empty with a Fortran implementation of this code.
N <- 100; set.seed(8237) x <- runif(N); y <- runif(N) R <- maxempty(x, y, c(0,1), c(0,1)) R # $area # [1] 0.08238793 # $rect # [1] 0.7023670 0.1797339 0.8175771 0.8948442 ## Not run: plot(x, y, pch="+", xlim=c(0,1), ylim=c(0,1), col="darkgray", main = "Maximally empty rectangle") rect(0, 0, 1, 1, border = "red", lwd = 1, lty = "dashed") do.call(rect, as.list(R$rect)) grid() ## End(Not run)N <- 100; set.seed(8237) x <- runif(N); y <- runif(N) R <- maxempty(x, y, c(0,1), c(0,1)) R # $area # [1] 0.08238793 # $rect # [1] 0.7023670 0.1797339 0.8175771 0.8948442 ## Not run: plot(x, y, pch="+", xlim=c(0,1), ylim=c(0,1), col="darkgray", main = "Maximally empty rectangle") rect(0, 0, 1, 1, border = "red", lwd = 1, lty = "dashed") do.call(rect, as.list(R$rect)) grid() ## End(Not run)
Lemarechal's MAXQUAD optimization test function.
maxquad(n, m)maxquad(n, m)
n |
number of variables of the generated test function. |
m |
number of functions to compete for the maximum. |
MAXQUAD actually is a family of minimax functions, parametrized by the
number n of variables and the number m of functions whose
maximum it is.
Returns a list with components fn the generated test function of
n variables, and gr the corresponding (analytical) gradient
function.
Kuntsevich, A., and F. Kappel (1997). SolvOpt – The Solver for Local Nonlinear Optimization Problems. Manual Version 1.1, Institute of Mathematics, University of Graz.
Lemarechal, C., and R. Mifflin, Eds. (1978). Nonsmooth Optimization. Pergamon Press, Oxford.
Shor, N. Z. (1985). Minimization Methods for Non-differentiable Functions. Series in Computational Mathematics, Springer-Verlag, Berlin.
# Test function of 5 variables, defined as maximum of 5 smooth functions maxq <- maxquad(5, 5) fnMaxquad <- maxq$fn grMaxquad <- maxq$gr # shor# Test function of 5 variables, defined as maximum of 5 smooth functions maxq <- maxquad(5, 5) fnMaxquad <- maxq$fn grMaxquad <- maxq$gr # shor
Find a subarray with maximal positive sum.
maxsub(x, inds = TRUE) maxsub2d(A)maxsub(x, inds = TRUE) maxsub2d(A)
x |
numeric vector. |
A |
numeric matrix |
inds |
logical; shall the indices be returned? |
maxsub finds a contiguous subarray whose sum is maximally positive.
This is sometimes called Kadane's algorithm.
maxsub will use a very fast version with a running time of
O(n) where n is the length of the input vector x.
maxsub2d finds a (contiguous) submatrix whose sum of elements is
maximally positive. The approach taken here is to apply the one-dimensional
routine to summed arrays between all rows of A. This has a run-time
of O(n^3), though a run-time of O(n^2 log n) seems possible
see the reference below.
maxsub2d can solve a 100-by-100 matrix in a few seconds –
but beware of bigger ones.
Either just a maximal sum, or a list this sum as component sum plus
the start and end indices as a vector inds.
In special cases, the matrix A may be sparse or (as in the example
section) only have one nonzero element in each row and column. Expectation
is that there may exists a more efficient (say O(n^2)) algorithm in
these special cases.
HwB <[email protected]>
Bentley, Jon (1986). “Programming Pearls”, Column 7. Addison-Wesley Publ. Co., Reading, MA.
T. Takaoka (2002). Efficient Algorithms for the Maximum Subarray Problem by Distance Matrix Multiplication. The Australasian Theory Symposion, CATS 2002.
## Find a maximal sum subvector set.seed(8237) x <- rnorm(1e6) system.time(res <- maxsub(x, inds = TRUE)) res ## Standard example: Find a maximal sum submatrix A <- matrix(c(0,-2,-7,0, 9,2,-6,2, -4,1,-4,1, -1,8,0,2), nrow = 4, ncol = 4, byrow =TRUE) maxsub2d(A) # $sum: 15 # $inds: 2 4 1 2 , i.e., rows = 2..4, columns = 1..2 ## Not run: ## Application to points in the unit square: set.seed(723) N <- 50; w <- rnorm(N) x <- runif(N); y <- runif(N) clr <- ifelse (w >= 0, "blue", "red") plot(x, y, pch = 20, col = clr, xlim = c(0, 1), ylim = c(0, 1)) xs <- unique(sort(x)); ns <- length(xs) X <- c(0, ((xs[1:(ns-1)] + xs[2:ns])/2), 1) ys <- unique(sort(y)); ms <- length(ys) Y <- c(0, ((ys[1:(ns-1)] + ys[2:ns])/2), 1) abline(v = X, col = "gray") abline(h = Y, col = "gray") A <- matrix(0, N, N) xi <- findInterval(x, X); yi <- findInterval(y, Y) for (i in 1:N) A[yi[i], xi[i]] <- w[i] msr <- maxsub2d(A) rect(X[msr$inds[3]], Y[msr$inds[1]], X[msr$inds[4]+1], Y[msr$inds[2]+1]) ## End(Not run)## Find a maximal sum subvector set.seed(8237) x <- rnorm(1e6) system.time(res <- maxsub(x, inds = TRUE)) res ## Standard example: Find a maximal sum submatrix A <- matrix(c(0,-2,-7,0, 9,2,-6,2, -4,1,-4,1, -1,8,0,2), nrow = 4, ncol = 4, byrow =TRUE) maxsub2d(A) # $sum: 15 # $inds: 2 4 1 2 , i.e., rows = 2..4, columns = 1..2 ## Not run: ## Application to points in the unit square: set.seed(723) N <- 50; w <- rnorm(N) x <- runif(N); y <- runif(N) clr <- ifelse (w >= 0, "blue", "red") plot(x, y, pch = 20, col = clr, xlim = c(0, 1), ylim = c(0, 1)) xs <- unique(sort(x)); ns <- length(xs) X <- c(0, ((xs[1:(ns-1)] + xs[2:ns])/2), 1) ys <- unique(sort(y)); ms <- length(ys) Y <- c(0, ((ys[1:(ns-1)] + ys[2:ns])/2), 1) abline(v = X, col = "gray") abline(h = Y, col = "gray") A <- matrix(0, N, N) xi <- findInterval(x, X); yi <- findInterval(y, Y) for (i in 1:N) A[yi[i], xi[i]] <- w[i] msr <- maxsub2d(A) rect(X[msr$inds[3]], Y[msr$inds[1]], X[msr$inds[4]+1], Y[msr$inds[2]+1]) ## End(Not run)
Solves the 0-1 (binary) multiple knapsack problem.
mknapsack(w, p, cap)mknapsack(w, p, cap)
w |
vector of (positive) weights. |
p |
vector of (positive) profits. |
cap |
vector of capacities of different knapsacks. |
Solves the 0-1 multiple knapsack problem for a set of profits and weights.
A multiple 0-1 knapsack problem can be formulated as:
maximize vstar = p(1)*(x(1,1) + ... + x(m,1)) + ...
... + p(n)*(x(1,n) + ... + x(m,n))
subject to
w(1)*x(i,1) + ... + w(n)*x(i,n) <= cap(i) for i=1,...,m
x(1,j) + ... + x(m,j) <= 1 for j=1,...,n
x(i,j) = 0 or 1 for i=1,...,m , j=1,...,n ,
The multiple knapsack problem is reformulated as a linear program and
solved with the help of package lpSolve.
This function can be used for the single knapsack problem as well,
but the 'dynamic programming' version in the knapsack function
is faster (but: allows only integer values).
The solution found is most often not unique and may not be the most compact
one. In the future, we will attempt to 'compactify' through backtracking.
The number of backtracks will be returned in list element bs.
A list with components, ksack the knapsack numbers the items are
assigned to, value the total value/profit of the solution found, and
bs the number of backtracks used.
Contrary to earlier versions, the sequence of profits and weights has been interchanged: first the weights, then profits.
The compiled version was transferred to the knapsack package on
R-Forge (see project 'optimist').
Kellerer, H., U. Pferschy, and D. Pisinger (2004). Knapsack Problems. Springer-Verlag, Berlin Heidelberg.
Martello, S., and P. Toth (1990). Knapsack Problems: Algorithms and Computer Implementations. John Wiley & Sons, Ltd.
Other packages implementing knapsack routines.
## Example 1: single knapsack w <- c( 2, 20, 20, 30, 40, 30, 60, 10) p <- c(15, 100, 90, 60, 40, 15, 10, 1) cap <- 102 (is <- mknapsack(w, p, cap)) which(is$ksack == 1) # [1] 1 2 3 4 6 , capacity 102 and total profit 280 ## Example 2: multiple knapsack w <- c( 40, 60, 30, 40, 20, 5) p <- c(110, 150, 70, 80, 30, 5) cap <- c(85, 65) is <- mknapsack(w, p, cap) # kps 1: 1,4; kps 2: 2,6; value: 345 ## Example 3: multiple knapsack p <- c(78, 35, 89, 36, 94, 75, 74, 79, 80, 16) w <- c(18, 9, 23, 20, 59, 61, 70, 75, 76, 30) cap <- c(103, 156) is <- mknapsack(w, p, cap) # kps 1: 3,4,5; kps 2: 1,6,9; value: 452 ## Not run: # How to Cut Your Planks with R # R-bloggers, Rasmus Baath, 2016-06-12 # # This is application of multiple knapsacks to cutting planks into pieces. planks_we_have <- c(120, 137, 220, 420, 480) planks_we_want <- c(19, 19, 19, 19, 79, 79, 79, 103, 103, 103, 135, 135, 135, 135, 160) s <- mknapsack(planks_we_want, planks_we_want + 1, planks_we_have) s$ksack ## [1] 5 5 5 5 3 5 5 4 1 5 4 5 3 2 4 # Solution w/o backtracking # bin 1 : 103 | Rest: 17 # bin 2 : 135 | Rest: 2 # bin 3 : 79 + 135 | Rest: 6 # bin 4 : 103 + 135 + 160 | Rest: 22 # bin 5 : 4*19 + 2*79 + 103 + 135 | Rest: 8 # # Solution with reversing the bins (bigger ones first) # bin 1 : 103 | Rest: 4 # bin 2 : 2*19 + 79 | Rest: 20 # bin 3 : 79 + 135 | Rest: 6 # bin 4 : 2*19 + 79 + 135 + 160 | Rest: 8 # bin 5 : 2*103 + 2*135 | Rest: 17 # # Solution with backtracking (compactification) # sol = c(1, 4, 4, 1, 1, 3, 4, 5, 5, 5, 5, 4, 2, 3, 4) # bin 1 : 2*19 + 79 | Rest: 3 # bin 2 : 135 | Rest: 2 # bin 3 : 79 + 135 | Rest: 6 # bin 4 : 2*19 + 79 + 135 + 160 | Rest: 8 # bin 5 : 3*103 + 135 | Rest: 36 ## End(Not run)## Example 1: single knapsack w <- c( 2, 20, 20, 30, 40, 30, 60, 10) p <- c(15, 100, 90, 60, 40, 15, 10, 1) cap <- 102 (is <- mknapsack(w, p, cap)) which(is$ksack == 1) # [1] 1 2 3 4 6 , capacity 102 and total profit 280 ## Example 2: multiple knapsack w <- c( 40, 60, 30, 40, 20, 5) p <- c(110, 150, 70, 80, 30, 5) cap <- c(85, 65) is <- mknapsack(w, p, cap) # kps 1: 1,4; kps 2: 2,6; value: 345 ## Example 3: multiple knapsack p <- c(78, 35, 89, 36, 94, 75, 74, 79, 80, 16) w <- c(18, 9, 23, 20, 59, 61, 70, 75, 76, 30) cap <- c(103, 156) is <- mknapsack(w, p, cap) # kps 1: 3,4,5; kps 2: 1,6,9; value: 452 ## Not run: # How to Cut Your Planks with R # R-bloggers, Rasmus Baath, 2016-06-12 # # This is application of multiple knapsacks to cutting planks into pieces. planks_we_have <- c(120, 137, 220, 420, 480) planks_we_want <- c(19, 19, 19, 19, 79, 79, 79, 103, 103, 103, 135, 135, 135, 135, 160) s <- mknapsack(planks_we_want, planks_we_want + 1, planks_we_have) s$ksack ## [1] 5 5 5 5 3 5 5 4 1 5 4 5 3 2 4 # Solution w/o backtracking # bin 1 : 103 | Rest: 17 # bin 2 : 135 | Rest: 2 # bin 3 : 79 + 135 | Rest: 6 # bin 4 : 103 + 135 + 160 | Rest: 22 # bin 5 : 4*19 + 2*79 + 103 + 135 | Rest: 8 # # Solution with reversing the bins (bigger ones first) # bin 1 : 103 | Rest: 4 # bin 2 : 2*19 + 79 | Rest: 20 # bin 3 : 79 + 135 | Rest: 6 # bin 4 : 2*19 + 79 + 135 + 160 | Rest: 8 # bin 5 : 2*103 + 2*135 | Rest: 17 # # Solution with backtracking (compactification) # sol = c(1, 4, 4, 1, 1, 3, 4, 5, 5, 5, 5, 4, 2, 3, 4) # bin 1 : 2*19 + 79 | Rest: 3 # bin 2 : 135 | Rest: 2 # bin 3 : 79 + 135 | Rest: 6 # bin 4 : 2*19 + 79 + 135 + 160 | Rest: 8 # bin 5 : 3*103 + 135 | Rest: 36 ## End(Not run)
An implementation of the Nelder-Mead algorithm for derivative-free optimization / function minimization.
neldermead( fn, x0, ..., adapt = TRUE, tol = 1e-10, maxfeval = 10000, step = rep(1.0, length(x0))) neldermeadb(fn, x0, ..., lower, upper, adapt = TRUE, tol = 1e-10, maxfeval = 10000, step = rep(1, length(x0)))neldermead( fn, x0, ..., adapt = TRUE, tol = 1e-10, maxfeval = 10000, step = rep(1.0, length(x0))) neldermeadb(fn, x0, ..., lower, upper, adapt = TRUE, tol = 1e-10, maxfeval = 10000, step = rep(1, length(x0)))
fn |
nonlinear function to be minimized. |
x0 |
starting point for the iteration. |
adapt |
logical; adapt to parameter dimension. |
tol |
terminating limit for the variance of function values;
can be made *very* small, like |
maxfeval |
maximum number of function evaluations. |
step |
size and shape of initial simplex; relative magnitudes of its elements should reflect the units of the variables. |
... |
additional arguments to be passed to the function. |
lower, upper
|
lower and upper bounds. |
Also called a ‘simplex’ method for finding the local minimum of a function of several variables. The method is a pattern search that compares function values at the vertices of the simplex. The process generates a sequence of simplices with ever reducing sizes.
The simplex function minimisation procedure due to Nelder and Mead (1965), as implemented by O'Neill (1971), with subsequent comments by Chambers and Ertel 1974, Benyon 1976, and Hill 1978. For another elaborate implementation of Nelder-Mead in R based on Matlab code by Kelley see package ‘dfoptim’.
eldermead can be used up to 20 dimensions (then ‘tol’ and ‘maxfeval’
need to be increased). With adapt=TRUE it applies adaptive
coefficients for the simplicial search, depending on the problem dimension
– see Fuchang and Lixing (2012). This approach especially reduces the
number of function calls.
With upper and/or lower bounds, neldermeadb applies transfinite
to define the function on all of R^n and to retransform the solution to the
bounded domain. Of course, if the optimum is near to the boundary, results
will not be as accurate as when the minimum is in the interior.
List with following components:
xmin |
minimum solution found. |
fmin |
value of |
fcount |
number of iterations performed. |
restarts |
number of restarts. |
errmess |
error message |
Original FORTRAN77 version by R O'Neill; MATLAB version by John Burkardt under LGPL license. Re-implemented in R by Hans W. Borchers.
Nelder, J., and R. Mead (1965). A simplex method for function minimization. Computer Journal, Volume 7, pp. 308-313.
O'Neill, R. (1971). Algorithm AS 47: Function Minimization Using a Simplex Procedure. Applied Statistics, Volume 20(3), pp. 338-345.
J. C. Lagarias et al. (1998). Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal for Optimization, Vol. 9, No. 1, pp 112-147.
Fuchang Gao and Lixing Han (2012). Implementing the Nelder-Mead simplex algorithm with adaptive parameters. Computational Optimization and Applications, Vol. 51, No. 1, pp. 259-277.
## Classical tests as in the article by Nelder and Mead # Rosenbrock's parabolic valley rpv <- function(x) 100*(x[2] - x[1]^2)^2 + (1 - x[1])^2 x0 <- c(-2, 1) neldermead(rpv, x0) # 1 1 # Fletcher and Powell's helic valley fphv <- function(x) 100*(x[3] - 10*atan2(x[2], x[1])/(2*pi))^2 + (sqrt(x[1]^2 + x[2]^2) - 1)^2 +x[3]^2 x0 <- c(-1, 0, 0) neldermead(fphv, x0) # 1 0 0 # Powell's Singular Function (PSF) psf <- function(x) (x[1] + 10*x[2])^2 + 5*(x[3] - x[4])^2 + (x[2] - 2*x[3])^4 + 10*(x[1] - x[4])^4 x0 <- c(3, -1, 0, 1) neldermead(psf, x0) # 0 0 0 0, needs maximum number of function calls # Bounded version of Nelder-Mead lower <- c(-Inf, 0, 0) upper <- c( Inf, 0.5, 1) x0 <- c(0, 0.1, 0.1) neldermeadb(fnRosenbrock, c(0, 0.1, 0.1), lower = lower, upper = upper) # $xmin = c(0.7085595, 0.5000000, 0.2500000) # $fmin = 0.3353605 ## Not run: # Can run Rosenbrock's function in 30 dimensions in one and a half minutes: neldermead(fnRosenbrock, rep(0, 30), tol=1e-20, maxfeval=10^7) # $xmin # [1] 0.9999998 1.0000004 1.0000000 1.0000001 1.0000000 1.0000001 # [7] 1.0000002 1.0000001 0.9999997 0.9999999 0.9999997 1.0000000 # [13] 0.9999999 0.9999994 0.9999998 0.9999999 0.9999999 0.9999999 # [19] 0.9999999 1.0000001 0.9999998 1.0000000 1.0000003 0.9999999 # [25] 1.0000000 0.9999996 0.9999995 0.9999990 0.9999973 0.9999947 # $fmin # [1] 5.617352e-10 # $fcount # [1] 1426085 # elapsed time is 96.008000 seconds ## End(Not run)## Classical tests as in the article by Nelder and Mead # Rosenbrock's parabolic valley rpv <- function(x) 100*(x[2] - x[1]^2)^2 + (1 - x[1])^2 x0 <- c(-2, 1) neldermead(rpv, x0) # 1 1 # Fletcher and Powell's helic valley fphv <- function(x) 100*(x[3] - 10*atan2(x[2], x[1])/(2*pi))^2 + (sqrt(x[1]^2 + x[2]^2) - 1)^2 +x[3]^2 x0 <- c(-1, 0, 0) neldermead(fphv, x0) # 1 0 0 # Powell's Singular Function (PSF) psf <- function(x) (x[1] + 10*x[2])^2 + 5*(x[3] - x[4])^2 + (x[2] - 2*x[3])^4 + 10*(x[1] - x[4])^4 x0 <- c(3, -1, 0, 1) neldermead(psf, x0) # 0 0 0 0, needs maximum number of function calls # Bounded version of Nelder-Mead lower <- c(-Inf, 0, 0) upper <- c( Inf, 0.5, 1) x0 <- c(0, 0.1, 0.1) neldermeadb(fnRosenbrock, c(0, 0.1, 0.1), lower = lower, upper = upper) # $xmin = c(0.7085595, 0.5000000, 0.2500000) # $fmin = 0.3353605 ## Not run: # Can run Rosenbrock's function in 30 dimensions in one and a half minutes: neldermead(fnRosenbrock, rep(0, 30), tol=1e-20, maxfeval=10^7) # $xmin # [1] 0.9999998 1.0000004 1.0000000 1.0000001 1.0000000 1.0000001 # [7] 1.0000002 1.0000001 0.9999997 0.9999999 0.9999997 1.0000000 # [13] 0.9999999 0.9999994 0.9999998 0.9999999 0.9999999 0.9999999 # [19] 0.9999999 1.0000001 0.9999998 1.0000000 1.0000003 0.9999999 # [25] 1.0000000 0.9999996 0.9999995 0.9999990 0.9999973 0.9999947 # $fmin # [1] 5.617352e-10 # $fcount # [1] 1426085 # elapsed time is 96.008000 seconds ## End(Not run)
Counts items, or finds subsequences of (integer) sequences.
count(x, sorted = TRUE) occurs(subseq, series)count(x, sorted = TRUE) occurs(subseq, series)
x |
array of items, i.e. numbers or characters. |
sorted |
logical; default is to sort items beforehand. |
subseq |
vector of integers. |
series |
vector of integers. |
count counts the items, similar to table, but as fast and
a more tractable output. If sorted then the total number per item
will be counted, else per repetition.
If m and n are the lengths of s and S resp.,
then occurs(s, S) determines all positions i such that
s == S[i, ..., i+m-1].
The code is vectorized and relatively fast. It is intended to complement this with an implementation of Rabin-Karp, and possibly Knuth-Morris-Pratt and Boyer-Moore algorithms.
count returns a list with components v the items and
e the number of times it apears in the array.
occurs returns a vector of indices, the positions where the
subsequence appears in the series.
## Examples patrn <- c(1,2,3,4) exmpl <- c(3,3,4,2,3,1,2,3,4,8,8,23,1,2,3,4,4,34,4,3,2,1,1,2,3,4) occurs(patrn, exmpl) ## [1] 6 13 23 ## Not run: set.seed(2437) p <- sample(1:20, 1000000, replace=TRUE) system.time(i <- occurs(c(1,2,3,4,5), p)) #=> [1] 799536 ## user system elapsed ## 0.017 0.000 0.017 [sec] system.time(c <- count(p)) ## user system elapsed ## 0.075 0.000 0.076 print(c) ## $v ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ## $e ## [1] 49904 50216 49913 50154 49967 50045 49747 49883 49851 49893 ## [11] 50193 50024 49946 49828 50319 50279 50019 49990 49839 49990 ## End(Not run)## Examples patrn <- c(1,2,3,4) exmpl <- c(3,3,4,2,3,1,2,3,4,8,8,23,1,2,3,4,4,34,4,3,2,1,1,2,3,4) occurs(patrn, exmpl) ## [1] 6 13 23 ## Not run: set.seed(2437) p <- sample(1:20, 1000000, replace=TRUE) system.time(i <- occurs(c(1,2,3,4,5), p)) #=> [1] 799536 ## user system elapsed ## 0.017 0.000 0.017 [sec] system.time(c <- count(p)) ## user system elapsed ## 0.075 0.000 0.076 print(c) ## $v ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ## $e ## [1] 49904 50216 49913 50154 49967 50045 49747 49883 49851 49893 ## [11] 50193 50024 49946 49828 50319 50279 50019 49990 49839 49990 ## End(Not run)
Solves the Set Cover problem as an integer linear program.
setcover(Sets, weights)setcover(Sets, weights)
Sets |
matrix of 0s and 1s, each line defining a subset. |
weights |
numerical weights for each subset. |
The Set Cover problems attempts to find in subsets (of a 'universe') a minimal set of subsets that still covers the whole set.
Each line of the matrix Sets defines a characteristic function of
a subset. It is required that each element of the universe is contained
in at least one of these subsets.
The problem is treated as an Integer Linear Program (ILP) and solved
with the lp solver in lpSolve.
Returns a list with components sets, giving the indices of subsets,
and objective, the sum of weights of subsets present in the solution.
See the Wikipedia article on the "set cover problem".
# Define 12 subsets of universe {1, ..., 10}. set.seed(7*11*13) A <- matrix(sample(c(0,1), prob = c(0.8,0.2), size = 120, replace =TRUE), nrow = 12, ncol = 10) sol <- setcover(Sets = A, weights = rep(1, 12)) sol ## $sets ## [1] 1 2 9 12 ## $no.sets ##[1] 4 # all universe elements are covered: colSums(A[sol$sets, ]) ## [1] 1 1 2 1 1 1 2 1 1 2# Define 12 subsets of universe {1, ..., 10}. set.seed(7*11*13) A <- matrix(sample(c(0,1), prob = c(0.8,0.2), size = 120, replace =TRUE), nrow = 12, ncol = 10) sol <- setcover(Sets = A, weights = rep(1, 12)) sol ## $sets ## [1] 1 2 9 12 ## $no.sets ##[1] 4 # all universe elements are covered: colSums(A[sol$sets, ]) ## [1] 1 1 2 1 1 1 2 1 1 2
Test functions for global optimization posed for the SIAM 100-digit challenge in 2002 by Nick Trefethen, Oxford University, UK.
fnTrefethen(p2) fnWagon(p3)fnTrefethen(p2) fnWagon(p3)
p2 |
Numerical vector of length 2. |
p3 |
Numerical vector of length 3. |
These are highly nonlinear and oscillating functions in two and three dimensions with thousands of local mimima inside the unit square resp. cube (i.e., [-1, 1] x [-1, 1] or [-1, 1] x [-1, 1] x [-1, 1]).
Function value is a single real number.
HwB <[email protected]>
F. Bornemann, D. Laurie, S. Wagon, and J. Waldvogel (2004). The SIAM 100-Digit Challenge: A Study in High-Accuracy Numerical Computing. Society for Industrial and Applied Mathematics.
x <- 2*runif(5) - 1 fnTrefethen(x) fnWagon(x) ## Not run: T <- matrix(NA, nrow=1001, ncol=1001) for (i in 1:1001) { for (j in 1:1001) { T[i, j] <- fnTrefethen(c(x[i], y[j])) } } image(x, y, T) contour(x, y, T, add=TRUE) ## End(Not run)x <- 2*runif(5) - 1 fnTrefethen(x) fnWagon(x) ## Not run: T <- matrix(NA, nrow=1001, ncol=1001) for (i in 1:1001) { for (j in 1:1001) { T[i, j] <- fnTrefethen(c(x[i], y[j])) } } image(x, y, T) contour(x, y, T, add=TRUE) ## End(Not run)
Simple Differential Evolution for Minimization.
simpleDE(fun, lower, upper, N = 64, nmax = 256, r = 0.4, confined = TRUE, log = FALSE)simpleDE(fun, lower, upper, N = 64, nmax = 256, r = 0.4, confined = TRUE, log = FALSE)
fun |
the objective function to be minimized. |
lower |
vector of lower bounds for all coordinates. |
upper |
vector of upper bounds for all coordinates. |
N |
population size. |
nmax |
bound on the number of generations. |
r |
amplification factor. |
confined |
logical; stay confined within bounds. |
log |
logical; shall a trace be printed. |
Evolutionary search to minimize a function: For points in the current generation, children are formed by taking a linear combination of parents, i.e., each member of the next generation has the form
where the are members of the current generation and is an
amplification factor.
List with the following components:
fmin |
function value at the minimum found. |
xmin |
numeric vector representing the minimum. |
nfeval |
number of function calls. |
Original Mathematica version by Dirk Laurie in the SIAM textbook. Translated to R by Hans W Borchers.
HwB <[email protected]>
Dirk Laurie. “A Complex Optimization". Chapter 5 In: F. Bornemann, D. Laurie, S. Wagon, and J. Waldvogel (Eds.). The SIAM 100-Digit Challenge. Society of Industrial and Applied Mathematics, 2004.
simpleEA, DEoptim in the ‘DEoptim’ package.
simpleDE(fnTrefethen, lower = c(-1,-1), upper = c(1,1)) # $fmin # [1] -3.306869 # $xmin # [1] -0.02440308 0.21061243 # this is the true global optimum!simpleDE(fnTrefethen, lower = c(-1,-1), upper = c(1,1)) # $fmin # [1] -3.306869 # $xmin # [1] -0.02440308 0.21061243 # this is the true global optimum!
Simple Evolutionary Algorithm for Minimization.
simpleEA(fn, lower, upper, N = 100, ..., con = 0.1, new = 0.05, tol = 1e-10, eps = 1e-07, scl = 1/2, confined = FALSE, log = FALSE)simpleEA(fn, lower, upper, N = 100, ..., con = 0.1, new = 0.05, tol = 1e-10, eps = 1e-07, scl = 1/2, confined = FALSE, log = FALSE)
fn |
the objective function to be minimized. |
lower |
vector of lower bounds for all coordinates. |
upper |
vector of upper bounds for all coordinates. |
N |
number of children per parent. |
... |
additional parameters to be passed to the function. |
con |
percentage of individuals concentrating to the best parents. |
new |
percentage of new individuals not focussed on existing parents. |
tol |
tolerance; if in the last three loops no better individuals were found up to this tolerance, stop. |
eps |
grid size bound to be reached. |
scl |
scaling factor for shrinking the grid. |
confined |
logical; shall the set of individuals be strictly respect the boundary? Default: FALSE. |
log |
logical, should best solution found be printed per step. |
Evolutionary search to minimize a function: For each point in the current generation, n random points are introduced and the n best results of each generation (and its parents) are used to form the next generation.
The scale shrinks the generation of new points as the algorithm proceeds. It is possible for some children to lie outside the given rectangle, and therefore the final result may lie outside the unit rectangle well. (TO DO: Make this an option.)
List with the following components:
par |
numeric vector representing the minimum found. |
val |
function value at the minimum found. |
fun.calls |
number of function calls made. |
rel.scl |
last scaling factor indicating grid size in last step. |
rel.tol |
relative tolerance within the last three minima found. |
Original Mathematica Version by Stan Wagon in the SIAM textbook. Translated to R by Hans W Borchers.
HwB <[email protected]>
Stan Wagon. “Think Globally, Act Locally". Chapter 4 In: F. Bornemann, D. Laurie, S. Wagon, and J. Waldvogel (Eds.). The SIAM 100-Digit Challenge. Society of Industrial and Applied Mathematics, 2004.
DEoptim in the ‘DEoptim’ package.
simpleEA(fnTrefethen, lower=c(-1,-1), upper=c(1,1), log=FALSE) # $par # [1] -0.02440310 0.21061243 # this is the true global optimum! # $val # [1] -3.306869simpleEA(fnTrefethen, lower=c(-1,-1), upper=c(1,1), log=FALSE) # $par # [1] -0.02440310 0.21061243 # this is the true global optimum! # $val # [1] -3.306869
Subset sum routine for positive integers.
subsetsum(S, t, method = "greedy") sss_test(S, t)subsetsum(S, t, method = "greedy") sss_test(S, t)
S |
vector of positive integers. |
t |
target value, bigger than all items in |
method |
can be “greedy” or “dynamic”, where “dynamic” stands for the dynamic programming approach. |
subsetsum is searching for a set of elements in S that
sum up to t by continuously adding more elements of S.
It is not required that S is decreasingly sorted. But for reasons
of efficiency and smaller execution times it is urgently recommended to
sort the item set in decreasing order. See the examples to find out how
to handle your data.
The first components will be preferred, i.e., if S is decreasing,
the sum with larger elements will be found, if increasing, the sum with
smaller elements. Because of timing considerations, the default is to
sort decreasingly before processing.
The dynamic method may be faster for large sets, but will also require much more memory if the target value is large.
sss_test will find the biggest number below or equal to t
that can be expressed as a sum of items in S. It will not return
any indices. It can be quite fast, though it preprocesses the set S
to be sorted decreasingly, too.
List with the target value, if reached, and vector of indices of elements
in S that sum up to t.
If no solution is found, the dynamic method will return indices for the largest value below the target, the greedy method witll return NULL.
sss_test will simply return maximum sum value found.
A compiled version – and much faster, in Fortran – can be found in
package 'knapsack' (R-Forge, project 'optimist') as subsetsum.
A recursive version, returning *all* solutions, is much too slow in R,
but is possible in Julia and can be asked from the author.
HwB email: <[email protected]>
Horowitz, E., and S. Sahni (1978). Fundamentals of Computer Algorithms. Computer Science Press, Rockville, ML.
t <- 5842 S <- c(267, 493, 869, 961, 1000, 1153, 1246, 1598, 1766, 1922) # S is not decreasingly sorted, so ... o <- order(S, decreasing = TRUE) So <- S[o] # So is decreasingly sorted sol <- subsetsum(So, t) # $inds: 2 4 6 7 8 w.r.t. So is <- o[sol$inds] # is: 9 7 5 4 3 w.r.t. S sum(S[is]) # 5842 ## Not run: amount <- 4748652 products <- c(30500,30500,30500,30500,42000,42000,42000,42000, 42000,42000,42000,42000,42000,42000,71040,90900, 76950,35100,71190,53730,456000,70740,70740,533600, 83800,59500,27465,28000,28000,28000,28000,28000, 26140,49600,77000,123289,27000,27000,27000,27000, 27000,27000,80000,33000,33000,55000,77382,48048, 51186,40000,35000,21716,63051,15025,15025,15025, 15025,800000,1110000,59700,25908,829350,1198000,1031655) # prepare set prods <- products[products <= amount] # no elements > amount prods <- sort(prods, decreasing=TRUE) # decreasing order # now find one solution system.time(is <- subsetsum(prods, amount)) # user system elapsed # 0.030 0.000 0.029 prods[is] # [1] 70740 70740 71190 76950 77382 80000 83800 # [8] 90900 456000 533600 829350 1110000 1198000 sum(prods[is]) == amount # [1] TRUE # Timings: # unsorted decr.sorted # "greedy" 22.930 0.030 (therefore the default settings) # "dynamic" 2.515 0.860 (overhead for smaller sets) # sss_test 8.450 0.040 (no indices returned) ## End(Not run)t <- 5842 S <- c(267, 493, 869, 961, 1000, 1153, 1246, 1598, 1766, 1922) # S is not decreasingly sorted, so ... o <- order(S, decreasing = TRUE) So <- S[o] # So is decreasingly sorted sol <- subsetsum(So, t) # $inds: 2 4 6 7 8 w.r.t. So is <- o[sol$inds] # is: 9 7 5 4 3 w.r.t. S sum(S[is]) # 5842 ## Not run: amount <- 4748652 products <- c(30500,30500,30500,30500,42000,42000,42000,42000, 42000,42000,42000,42000,42000,42000,71040,90900, 76950,35100,71190,53730,456000,70740,70740,533600, 83800,59500,27465,28000,28000,28000,28000,28000, 26140,49600,77000,123289,27000,27000,27000,27000, 27000,27000,80000,33000,33000,55000,77382,48048, 51186,40000,35000,21716,63051,15025,15025,15025, 15025,800000,1110000,59700,25908,829350,1198000,1031655) # prepare set prods <- products[products <= amount] # no elements > amount prods <- sort(prods, decreasing=TRUE) # decreasing order # now find one solution system.time(is <- subsetsum(prods, amount)) # user system elapsed # 0.030 0.000 0.029 prods[is] # [1] 70740 70740 71190 76950 77382 80000 83800 # [8] 90900 456000 533600 829350 1110000 1198000 sum(prods[is]) == amount # [1] TRUE # Timings: # unsorted decr.sorted # "greedy" 22.930 0.030 (therefore the default settings) # "dynamic" 2.515 0.860 (overhead for smaller sets) # sss_test 8.450 0.040 (no indices returned) ## End(Not run)
Simple and often used test function defined in higher dimensions and with
analytical gradients, especially suited for performance tests. Analytical
gradients, where existing, are provided with the gr prefix.
The dimension is determined by the length of the input vector.
fnRosenbrock(x) grRosenbrock(x) fnRastrigin(x) grRastrigin(x) fnNesterov(x) grNesterov(x) fnNesterov1(x) fnNesterov2(x) fnHald(x) grHald(x) fnShor(x) grShor(x)fnRosenbrock(x) grRosenbrock(x) fnRastrigin(x) grRastrigin(x) fnNesterov(x) grNesterov(x) fnNesterov1(x) fnNesterov2(x) fnHald(x) grHald(x) fnShor(x) grShor(x)
x |
numeric vector of a certain length. |
Rosenbrock – Rosenbrock's famous valley function from 1960. It can also be regarded as a least-squares problem:
| No. of Vars.: | n >= 2 |
| Bounds: | -5.12 <= xi <= 5.12 |
| Local minima: | at f(-1, 1, ..., 1) for n >= 4 |
| Minimum: | 0.0 |
| Solution: | xi = 1, i = 1:n |
Nesterov – Nesterov's smooth adaptation of Rosenbrock, based on the idea of Chebyshev polynomials. This function is even more difficult to optimize than Rosenbrock's:
Two nonsmooth Nesterov functions are available: Nesterov2 and Nesterov1, defined as
| No. of Vars.: | n >= 2 |
| Bounds: | -5.12 <= xi <= 5.12 |
| Local minima: ? | |
| Minimum: | 0.0 |
| Solution: | xi = 1, i = 1:n |
Nesterov1 and Nesterov2 – Simlar to Nesterov, except the terms added are taken with absolute value, which makes this function nonsmooth and painful for gradient-based optimization routines; no gradient provided.
(Nesterov2 uses absolute instead of quadratic terms.)
Rastrigin – Rastrigin's function is a famous, non-convex example from 1989 for global optimization. It is a typical example of a multimodal function with many local minima:
| No. of Vars.: | n >= 2 |
| Bounds: | -5.12 <= xi <= 5.12 |
| Local minima: | many |
| Minimum: | 0.0 |
| Solution: | xi = 0, i = 1:n |
Hald – Hald's function is a typical example of a non-smooth test function, from Hald and Madsen in 1981.
where and for .
| No. of Vars.: | n =5 |
| Bounds: | -1 <= xi <= 1 |
| Local minima: | ? |
| Minimum: | 0.0001223713 |
| Solution: | (0.99987763, 0.25358844, -0.74660757, 0.24520150, -0.03749029) |
Shor – Shor's function is another typical example of a non-smooth test function, a benchmark for Shor's R-algorithm.
Returns the values of the test function resp. its gradient at that point. If an analytical gradient is not available, a function computing the gradient numerically will be provided.
Search the Internet.
x <- runif(5) fnHald(x); grHald(x) # Compare analytical and numerical gradient shor_gr <- function(x) adagio:::ns.grad(fnShor, x) # internal gradient grShor(x); shor_gr(x)x <- runif(5) fnHald(x); grHald(x) # Compare analytical and numerical gradient shor_gr <- function(x) adagio:::ns.grad(fnShor, x) # internal gradient grShor(x); shor_gr(x)
Transformation of a box/bound constrained region to an unconstrained one.
transfinite(lower, upper, n = length(lower))transfinite(lower, upper, n = length(lower))
lower, upper
|
lower and upper box/bound constraints. |
n |
length of upper, lower if both are scalars, to which they get repeated. |
Transforms a constraint region in n-dimensional space bijectively to the
unconstrained space, applying a atanh resp. exp
transformation to each single variable that is bound constraint.
It provides two functions, h: B = []x...x[] --> R^n and its inverse
hinv. These functions can, for example, be used to add box/bound
constraints to a constrained optimization problem that is to be solved with
a (nonlinear) solver not allowing constraints.
Returns to functions as components h and hinv of a list.
Based on an idea of Ravi Varadhan, intrinsically used in his implementation of Nelder-Mead in the ‘dfoptim’ package.
For positivity constraints, x>=0, this approach is considered to be
numerically more stable than x-->exp(x) or x-->x^2.
lower <- c(-Inf, 0, 0) upper <- c( Inf, 0.5, 1) Tf <- transfinite(lower, upper) h <- Tf$h; hinv <- Tf$hinv ## Not run: ## Solve Rosenbrock with one variable restricted rosen <- function(x) { n <- length(x) x1 <- x[2:n]; x2 <- x[1:(n-1)] sum(100*(x1-x2^2)^2 + (1-x2)^2) } f <- function(x) rosen(hinv(x)) # f must be defined on all of R^n x0 <- c(0.1, 0.1, 0.1) # starting point not on the boundary! nm <- nelder_mead(h(x0), f) # unconstraint Nelder-Mead hinv(nm$xmin); nm$fmin # box/bound constraint solution # [1] 0.7085596 0.5000000 0.2500004 # [1] 0.3353605 ## End(Not run)lower <- c(-Inf, 0, 0) upper <- c( Inf, 0.5, 1) Tf <- transfinite(lower, upper) h <- Tf$h; hinv <- Tf$hinv ## Not run: ## Solve Rosenbrock with one variable restricted rosen <- function(x) { n <- length(x) x1 <- x[2:n]; x2 <- x[1:(n-1)] sum(100*(x1-x2^2)^2 + (1-x2)^2) } f <- function(x) rosen(hinv(x)) # f must be defined on all of R^n x0 <- c(0.1, 0.1, 0.1) # starting point not on the boundary! nm <- nelder_mead(h(x0), f) # unconstraint Nelder-Mead hinv(nm$xmin); nm$fmin # box/bound constraint solution # [1] 0.7085596 0.5000000 0.2500004 # [1] 0.3353605 ## End(Not run)