This document covers the topic of finding combinations or permutations that meet a specific set of criteria. For example, retrieving all combinations of a vector that have a product between two bounds.

There are 5 compiled constraint functions that can be utilized efficiently to test a given result.

- sum
- prod
- mean
- max
- min

They are passed as strings to the `constraintFun`

parameter. When these are employed without any other parameters being
set, an additional column is added that represents the result of
applying the given function to that combination/permutation. You can
also set `keepResults = TRUE`

(more on this later).

```
library(RcppAlgos)
options(width = 90)
packageVersion("RcppAlgos")
#> [1] '2.8.3'
cat(paste(capture.output(sessionInfo())[1:3], collapse = "\n"))
#> R version 4.3.1 (2023-06-16)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.4.1
## base R using combn and FUN
combnSum = combn(20, 10, sum)
algosSum = comboGeneral(20, 10, constraintFun = "sum")
## Notice the additional column (i.e. the 11th column)
head(algosSum)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
#> [1,] 1 2 3 4 5 6 7 8 9 10 55
#> [2,] 1 2 3 4 5 6 7 8 9 11 56
#> [3,] 1 2 3 4 5 6 7 8 9 12 57
#> [4,] 1 2 3 4 5 6 7 8 9 13 58
#> [5,] 1 2 3 4 5 6 7 8 9 14 59
#> [6,] 1 2 3 4 5 6 7 8 9 15 60
identical(as.integer(combnSum), algosSum[,11])
#> [1] TRUE
## Using parallel
paralSum = comboGeneral(20, 10, constraintFun = "sum", Parallel = TRUE)
identical(paralSum, algosSum)
#> [1] TRUE
library(microbenchmark)
microbenchmark(serial = comboGeneral(20, 10, constraintFun = "sum"),
parallel = comboGeneral(20, 10, constraintFun = "sum", Parallel = TRUE),
combnSum = combn(20, 10, sum), unit = "relative")
#> Warning in microbenchmark(serial = comboGeneral(20, 10, constraintFun = "sum"), : less
#> accurate nanosecond times to avoid potential integer overflows
#> Unit: relative
#> expr min lq mean median uq max neval cld
#> serial 3.448004 3.024937 2.742634 2.984405 2.930366 2.958093 100 a
#> parallel 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 100 b
#> combnSum 164.987777 143.760767 124.381205 139.771622 132.586909 65.974864 100 c
```

`rowSums`

and `rowMeans`

Finding row sums or row means is even faster than simply applying the
highly efficient `rowSums`

/`rowMeans`

*after* the combinations have already been generated:

```
## Pre-generate combinations
combs = comboGeneral(25, 10)
## Testing rowSums alone against generating combinations as well as summing
microbenchmark(serial = comboGeneral(25, 10, constraintFun = "sum"),
parallel = comboGeneral(25, 10, constraintFun = "sum", Parallel = TRUE),
rowsums = rowSums(combs), unit = "relative")
#> Unit: relative
#> expr min lq mean median uq max neval cld
#> serial 3.549230 3.276643 2.958342 2.977668 2.869537 1.4453889 100 a
#> parallel 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000 100 b
#> rowsums 2.094864 1.955225 1.731866 1.712414 1.658254 0.4516231 100 c
all.equal(rowSums(combs),
comboGeneral(25, 10,
constraintFun = "sum",
Parallel = TRUE)[,11])
#> [1] TRUE
## Testing rowMeans alone against generating combinations as well as obtain row means
microbenchmark(serial = comboGeneral(25, 10, constraintFun = "mean"),
parallel = comboGeneral(25, 10, constraintFun = "mean", Parallel = TRUE),
rowmeans = rowMeans(combs), unit = "relative")
#> Unit: relative
#> expr min lq mean median uq max neval cld
#> serial 2.361449 2.413497 2.370115 2.386648 2.353974 1.4051019 100 a
#> parallel 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000 100 b
#> rowmeans 1.332882 1.343652 1.287887 1.325946 1.305233 0.3777631 100 c
all.equal(rowMeans(combs),
comboGeneral(25, 10,
constraintFun = "mean",
Parallel = TRUE)[,11])
#> [1] TRUE
```

In both cases above, `RcppAlgos`

is doing double the work
nearly twice as fast!!!

`limitConstraints`

The standard 5 comparison operators (i.e. `"<"`

,
`">"`

, `"<="`

, `">="`

, &
`"=="`

) can be used in a variety of ways. In order for them
to have any effect, they must be used in conjunction with
`constraintFun`

as well as `limitConstraints`

. The
latter is the value(s) that will be used for comparison. It can be
passed as a single value or a vector of two numerical values. This is
useful when one wants to find results that are between (or outside) of a
given range.

First we will look at cases with only one comparison and one value
for the `limitConstraint`

.

```
## Generate some random data. N.B. Using R >= 4.0.0
set.seed(101)
myNums = sample(500, 20)
myNums
#> [1] 329 313 430 95 209 442 351 317 444 315 246 355 128 131 288 9 352 489 354 244
## Find all 5-tuples combinations without repetition of myNums
## (defined above) such that the sum is equal to 1176.
p1 = comboGeneral(v = myNums, m = 5,
constraintFun = "sum",
comparisonFun = "==",
limitConstraints = 1176)
tail(p1)
#> [,1] [,2] [,3] [,4] [,5]
#> [10,] 95 128 246 352 355
#> [11,] 95 128 288 313 352
#> [12,] 95 131 244 351 355
#> [13,] 95 131 244 352 354
#> [14,] 95 209 244 313 315
#> [15,] 128 131 246 317 354
## Authenticate with brute force
allCombs = comboGeneral(sort(myNums), 5)
identical(p1, allCombs[which(rowSums(allCombs) == 1176), ])
#> [1] TRUE
## How about finding combinations with repetition
## whose mean is less than or equal to 150.
p2 = comboGeneral(v = myNums, m = 5, TRUE,
constraintFun = "mean",
comparisonFun = "<=",
limitConstraints = 150)
## Again, we authenticate with brute force
allCombs = comboGeneral(sort(myNums), 5, TRUE)
identical(p2, allCombs[which(rowMeans(allCombs) <= 150), ])
#> [1] FALSE ### <-- What? They should be the same
## N.B.
class(p2[1, ])
#> [1] "numeric"
class(allCombs[1, ])
#> [1] "integer"
## When mean is employed or it can be determined that integral
## values will not suffice for the comparison, we fall back to
## numeric types, thus all.equal should return TRUE
all.equal(p2, allCombs[which(rowMeans(allCombs) <= 150), ])
#> [1] TRUE
```

Sometimes, we need to generate combinations/permutations such that
when we apply a constraint function, the results are between (or
outside) a given range. There is a natural two step process when finding
results outside a range, however for finding results between a range,
this two step approach could become computationally demanding. The
underlying algorithms in `RcppAlgos`

are optimized for both
cases and avoids adding results that will eventually be removed.

Using two comparisons is easy. The first comparison operator is applied to the first limit and the second operator is applied to the second limit.

Note that in the examples below, we have
`keepResults = TRUE`

. This means an additional column will be
added to the output that is the result of applying
`constraintFun`

to that particular combination.

```
## Get combinations such that the product is
## strictly between 3600 and 4000
comboGeneral(5, 7, TRUE, constraintFun = "prod",
comparisonFun = c(">","<"), ## Find results > 3600 and < 4000
limitConstraints = c(3600, 4000),
keepResults = TRUE)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 1 2 3 5 5 5 5 3750
#> [2,] 1 3 4 4 4 4 5 3840
#> [3,] 2 2 3 4 4 4 5 3840
#> [4,] 3 3 3 3 3 3 5 3645
#> [5,] 3 3 3 3 3 4 4 3888
# ## The above is the same as doing the following:
# comboGeneral(5, 7, TRUE, constraintFun = "prod",
# comparisonFun = c("<",">"), ## Note that the comparison vector
# limitConstraints = c(4000, 3600), ## and the limits have flipped
# keepResults = TRUE)
## What about finding combinations outside a range
outside = comboGeneral(5, 7, TRUE, constraintFun = "prod",
comparisonFun = c("<=",">="),
limitConstraints = c(3600, 4000),
keepResults = TRUE)
all(apply(outside[, -8], 1, prod) <= 3600
| apply(outside[, -8], 1, prod) >= 4000)
#> [1] TRUE
dim(outside)
#> [1] 325 8
## Note that we obtained 5 results when searching "between"
## 3600 and 4000. Thus we have: 325 + 5 = 330
comboCount(5, 7, T)
#> [1] 330
```

`tolerance`

When the underlying type is `numeric`

, round-off
errors can occur. As stated in floating-point
error mitigation:

“By definition, floating-point error cannot be eliminated, and, at best, can only be managed.”

Here is a great stackoverflow post that further illuminates this tricky topic:

For these reasons, the argument `tolerance`

can be
utilized to refine a given constraint. It is added to the upper limit
and subtracted from the lower limit. The default value is
`sqrt(.Machine$double.eps) ~= 0.00000001490116`

.

This default value is good and bad.

For the good side:

```
dim(comboGeneral(seq(0, 0.5, 0.05), 6, TRUE,
constraintFun = "sum",
comparisonFun = "==",
limitConstraints = 1))
#> [1] 199 6
## Confirm with integers and brute force
allCbs = comboGeneral(seq(0L, 50L, 5L), 6, TRUE, constraintFun = "sum")
sum(allCbs[, 7] == 100L)
#> [1] 199
```

If we had a tolerance of zero, we would have obtained an incorrect result:

```
## We miss 31 combinations that add up to 1
dim(comboGeneral(seq(0, 0.5, 0.05), 6, TRUE,
constraintFun = "sum",
comparisonFun = "==",
limitConstraints = 1, tolerance = 0))
#> [1] 168 6
```

And now for a less desirable result. The example below appears to give incorrect results. That is, we shouldn’t return any combination with a mean of 4.1 or 5.1.

```
comboGeneral(c(2.1, 3.1, 5.1, 7.1), 3, T,
constraintFun = "mean", comparisonFun = c("<", ">"),
limitConstraints = c(5.1, 4.1), keepResults = TRUE)
#> [,1] [,2] [,3] [,4]
#> [1,] 2.1 3.1 7.1 4.100000
#> [2,] 2.1 5.1 5.1 4.100000
#> [3,] 2.1 5.1 7.1 4.766667
#> [4,] 3.1 3.1 7.1 4.433333
#> [5,] 3.1 5.1 5.1 4.433333
#> [6,] 3.1 5.1 7.1 5.100000
#> [7,] 5.1 5.1 5.1 5.100000
```

In the above example, the range that is actually tested against is
`c(4.0999999950329462, 5.1000000049670531)`

.

If you want to be absolutely sure you are getting the correct results, one must rely on integers as simple changes in arithmetic can throw off precision in floating point operations.

`permuteGeneral`

Typically, when we call `permuteGeneral`

, the output is in
lexicographical order, however when we apply a constraint, the
underlying algorithm checks against combinations only, as this is more
efficient. If a particular combination meets a constraint, then all
permutations of that vector also meet that constraint, so there is no
need to check them. For this reason, the output isn’t in order.
Observe:

```
permuteGeneral(c(2, 3, 5, 7), 3, freqs = rep(2, 4),
constraintFun = "mean", comparisonFun = c(">", "<"),
limitConstraints = c(4, 5), keepResults = TRUE, tolerance = 0)
#> [,1] [,2] [,3] [,4]
#> [1,] 2 5 7 4.666667 ### <-- First combination that meets the criteria
#> [2,] 2 7 5 4.666667
#> [3,] 5 2 7 4.666667
#> [4,] 5 7 2 4.666667
#> [5,] 7 2 5 4.666667
#> [6,] 7 5 2 4.666667
#> [7,] 3 3 7 4.333333 ### <-- Second combination that meets the criteria
#> [8,] 3 7 3 4.333333
#> [9,] 7 3 3 4.333333
#> [10,] 3 5 5 4.333333 ### <-- Third combination that meets the criteria
#> [11,] 5 3 5 4.333333
#> [12,] 5 5 3 4.333333
```

As you can see, the *2 ^{nd}* through the

Specialized algorithms are employed when it can be determined that we are looking for integer partitions.

As of version `2.5.0`

, we now have added
`partitionsGeneral`

which is similar to
`comboGeneral`

with `constraintFun = "sum"`

and
`comparisonFun = "=="`

. Instead of using the very general
`limitConstraints`

parameter, we use `target`

with
a default of `max(v)`

as it seems more fitting for
partitions.

We need `v = 0:N`

, `repetition = TRUE`

. When we
leave `m = NULL`

, `m`

is internally set to the
length of the longest non-zero combination (this is true for all cases
below).

```
partitionsGeneral(0:5, repetition = TRUE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 0 0 0 5
#> [2,] 0 0 0 1 4
#> [3,] 0 0 0 2 3
#> [4,] 0 0 1 1 3
#> [5,] 0 0 1 2 2
#> [6,] 0 1 1 1 2
#> [7,] 1 1 1 1 1
## Note that we could also use comboGeneral:
## comboGeneral(0:5, repetition = TRUE,
## constraintFun = "sum",
## comparisonFun = "==", limitConstraints = 5)
##
## The same goes for any of the examples below
```

Everything is the same as above except for explicitly setting the desired length and deciding whether to include zero or not.

Same as `Case 1 & 2`

except now we have
`repetition = FALSE`

.

```
partitionsGeneral(0:10)
#> [,1] [,2] [,3] [,4]
#> [1,] 0 1 2 7
#> [2,] 0 1 3 6
#> [3,] 0 1 4 5
#> [4,] 0 2 3 5
#> [5,] 1 2 3 4
## Zero not included and restrict the length
partitionsGeneral(10, 3)
#> [,1] [,2] [,3]
#> [1,] 1 2 7
#> [2,] 1 3 6
#> [3,] 1 4 5
#> [4,] 2 3 5
## Include zero and restrict the length
partitionsGeneral(0:10, 3)
#> [,1] [,2] [,3]
#> [1,] 0 1 9
#> [2,] 0 2 8
#> [3,] 0 3 7
#> [4,] 0 4 6
#> [5,] 1 2 7
#> [6,] 1 3 6
#> [7,] 1 4 5
#> [8,] 2 3 5
## partitions of 10 into distinct parts of every length
lapply(1:4, function(x) {
partitionsGeneral(10, x)
})
#> [[1]]
#> [,1]
#> [1,] 10
#>
#> [[2]]
#> [,1] [,2]
#> [1,] 1 9
#> [2,] 2 8
#> [3,] 3 7
#> [4,] 4 6
#>
#> [[3]]
#> [,1] [,2] [,3]
#> [1,] 1 2 7
#> [2,] 1 3 6
#> [3,] 1 4 5
#> [4,] 2 3 5
#>
#> [[4]]
#> [,1] [,2] [,3] [,4]
#> [1,] 1 2 3 4
```

`freqs`

to Refine LengthWe can utilize the `freqs`

argument to obtain more
distinct partitions by allowing for repeated zeros. The super optimized
algorithm will only be carried out if zero is included and the number of
repetitions for every number except zero is one.

For example, given `v = 0:N`

and `J >= 1`

,
if `freqs = c(J, rep(1, N))`

, then the super optimized
algorithm will be used, however if
`freqs = c(J, 2, rep(1, N - 1))`

, the general algorithm will
be used. It should be noted that the general algorithms are still highly
optimized so one should not fear using it.

A pattern that is guaranteed to retrieve all distinct partitions of
*N* is to set `v = 0:N`

and
`freqs = c(N, rep(1, N))`

(the extra zeros will be left
off).

```
## Obtain all distinct partitions of 10
partitionsGeneral(0:10, freqs = c(10, rep(1, 10))) ## Same as c(3, rep(1, 10))
#> [,1] [,2] [,3] [,4]
#> [1,] 0 0 0 10
#> [2,] 0 0 1 9
#> [3,] 0 0 2 8
#> [4,] 0 0 3 7
#> [5,] 0 0 4 6
#> [6,] 0 1 2 7
#> [7,] 0 1 3 6
#> [8,] 0 1 4 5
#> [9,] 0 2 3 5
#> [10,] 1 2 3 4
```

`freqs`

As noted in `Case 1`

, if `m = NULL`

, the length
of the output will be determined by the longest non-zero combination
that sums to *N*.

```
## m is NOT NULL and output has at most 2 zeros
partitionsGeneral(0:10, 3, freqs = c(2, rep(1, 10)))
#> [,1] [,2] [,3]
#> [1,] 0 0 10
#> [2,] 0 1 9
#> [3,] 0 2 8
#> [4,] 0 3 7
#> [5,] 0 4 6
#> [6,] 1 2 7
#> [7,] 1 3 6
#> [8,] 1 4 5
#> [9,] 2 3 5
## m is NULL and output has at most 2 zeros
partitionsGeneral(0:10, freqs = c(2, rep(1, 10)))
#> [,1] [,2] [,3] [,4]
#> [1,] 0 0 1 9
#> [2,] 0 0 2 8
#> [3,] 0 0 3 7
#> [4,] 0 0 4 6
#> [5,] 0 1 2 7
#> [6,] 0 1 3 6
#> [7,] 0 1 4 5
#> [8,] 0 2 3 5
#> [9,] 1 2 3 4
```

```
## partitions of 12 into 4 parts where each part can
## be used a specific number of times (e.g. 2 or 3)
partitionsGeneral(12, 4, freqs = rep(2:3, 6))
#> [,1] [,2] [,3] [,4]
#> [1,] 1 1 2 8
#> [2,] 1 1 3 7
#> [3,] 1 1 4 6
#> [4,] 1 1 5 5
#> [5,] 1 2 2 7
#> [6,] 1 2 3 6
#> [7,] 1 2 4 5
#> [8,] 1 3 3 5
#> [9,] 1 3 4 4
#> [10,] 2 2 2 6
#> [11,] 2 2 3 5
#> [12,] 2 2 4 4
#> [13,] 2 3 3 4
```

Note, as of version `2.5.0`

, one can generate partitions
in parallel using the `nThreads`

argument.

```
## partitions of 60
partitionsCount(0:60, repetition = TRUE)
#> [1] 966467
## Single threaded
system.time(partitionsGeneral(0:60, repetition = TRUE))
#> user system elapsed
#> 0.025 0.012 0.038
## Using nThreads
system.time(partitionsGeneral(0:60, repetition = TRUE, nThreads=4))
#> user system elapsed
#> 0.032 0.022 0.015
## partitions of 120 into distinct parts
partitionsCount(0:120, freqs = c(120, rep(1, 120)))
#> [1] 2194432
system.time(partitionsGeneral(0:120, freqs = c(120, rep(1, 120))))
#> user system elapsed
#> 0.020 0.006 0.026
system.time(partitionsGeneral(0:120, freqs = c(120, rep(1, 120)), nThreads=4))
#> user system elapsed
#> 0.023 0.006 0.008
## partitions of 100 into parts of 15 with specific multiplicity
partitionsCount(100, 15, freqs = rep(4:8, 20))
#> [1] 6704215
## Over 6 million in just over a second!
system.time(partitionsGeneral(100, 15, freqs = rep(4:8, 20)))
#> user system elapsed
#> 0.341 0.092 0.433
```

Compositions
are related to integer partitions, however order matters. With
`RcppAlgos`

, we generate standard compositions with
`compositionsGeneral`

. Currently, the composition algorithms
are limited to a subset of cases of compositions with repetiion.

The output with `compositionGeneral`

will be in
lexicographical order. When we set `weak = TRUE`

, we will
obtain ** weak compositions**, which allow for
zeros to be a part of the sequence (E.g.

`c(0, 0, 5), c(0, 5, 0), c(5, 0, 0)`

are weak compositions of
5). As the Wikipedia article points out, we can increase the number of
zeros indefinitely when `weak = TRUE`

.For more general cases, we can make use of
`permuteGeneral`

, keeping in mind that the output will not be
in lexicographical order. Another consideration with
`permuteGeneral`

is that when we include zero, we will always
obtain weak compositions.

With that in mind, generating compositions with
`RcppAlgos`

is easy, flexible, and quite efficient.

```
## See Case 1
compositionsGeneral(0:3, repetition = TRUE)
#> [,1] [,2] [,3]
#> [1,] 0 0 3
#> [2,] 0 1 2
#> [3,] 0 2 1
#> [4,] 1 1 1
## Get weak compositions
compositionsGeneral(0:3, repetition = TRUE, weak = TRUE)
#> [,1] [,2] [,3]
#> [1,] 0 0 3
#> [2,] 0 1 2
#> [3,] 0 2 1
#> [4,] 0 3 0
#> [5,] 1 0 2
#> [6,] 1 1 1
#> [7,] 1 2 0
#> [8,] 2 0 1
#> [9,] 2 1 0
#> [10,] 3 0 0
## Get weak compositions with width > than target
tail(compositionsGeneral(0:3, 10, repetition = TRUE, weak = TRUE))
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [215,] 2 0 0 0 0 1 0 0 0 0
#> [216,] 2 0 0 0 1 0 0 0 0 0
#> [217,] 2 0 0 1 0 0 0 0 0 0
#> [218,] 2 0 1 0 0 0 0 0 0 0
#> [219,] 2 1 0 0 0 0 0 0 0 0
#> [220,] 3 0 0 0 0 0 0 0 0 0
## With permuteGeneral, we always get weak compositions, just
## not in lexicographical order
permuteGeneral(0:3, repetition = TRUE,
constraintFun = "sum",
comparisonFun = "==", limitConstraints = 3)
#> [,1] [,2] [,3]
#> [1,] 0 0 3
#> [2,] 0 3 0
#> [3,] 3 0 0
#> [4,] 0 1 2
#> [5,] 0 2 1
#> [6,] 1 0 2
#> [7,] 1 2 0
#> [8,] 2 0 1
#> [9,] 2 1 0
#> [10,] 1 1 1
tail(permuteGeneral(0:3, 10, repetition = TRUE,
constraintFun = "sum",
comparisonFun = "==", limitConstraints = 3))
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [215,] 1 1 0 0 0 0 0 1 0 0
#> [216,] 1 1 0 0 0 0 1 0 0 0
#> [217,] 1 1 0 0 0 1 0 0 0 0
#> [218,] 1 1 0 0 1 0 0 0 0 0
#> [219,] 1 1 0 1 0 0 0 0 0 0
#> [220,] 1 1 1 0 0 0 0 0 0 0
```

We must use `permuteGeneral`

here.

```
compositionsGeneral(6, 3)
#> Error: Currently, there is no composition algorithm for this case.
#> Use permuteCount, permuteIter, permuteGeneral, permuteSample, or
#> permuteRank instead.
## See Case 3
permuteGeneral(6, 3,
constraintFun = "sum",
comparisonFun = "==", limitConstraints = 6)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 1 3 2
#> [3,] 2 1 3
#> [4,] 2 3 1
#> [5,] 3 1 2
#> [6,] 3 2 1
```

With `compositionGeneral`

we are able to take advantage of
parallel computation. With `permuteGeneral`

, the parallel
options have no effect when generating compositions.

```
## compositions of 25
system.time(compositionsGeneral(0:25, repetition = TRUE))
#> user system elapsed
#> 1.711 0.098 1.809
compositionsCount(0:25, repetition=TRUE)
#> [1] 16777216
## Use multiple threads for greater efficiency. Generate
## over 16 million compositions in under a second!
system.time(compositionsGeneral(0:25, repetition = TRUE, nThreads = 4))
#> user system elapsed
#> 1.871 0.103 0.547
## weak compositions of 12 usnig nThreads = 4
system.time(weakComp12 <- compositionsGeneral(0:12, repetition = TRUE,
weak = TRUE, nThreads = 4))
#> user system elapsed
#> 0.012 0.007 0.005
## And using permuteGeneral
system.time(weakPerm12 <- permuteGeneral(0:12, 12, repetition = TRUE,
constraintFun = "sum",
comparisonFun = "==",
limitConstraints = 12))
#> user system elapsed
#> 0.011 0.003 0.015
dim(weakPerm12)
#> [1] 1352078 12
identical(weakPerm12[do.call(order, as.data.frame(weakPerm12)), ],
weakComp12)
#> [1] TRUE
## General compositions with varying multiplicities
system.time(comp25_gen <- permuteGeneral(25, 10, freqs = rep(4:8, 5),
constraintFun = "sum",
comparisonFun = "==",
limitConstraints = 25))
#> user system elapsed
#> 0.025 0.006 0.031
dim(comp25_gen)
#> [1] 946092 10
```

`cpp11::check_user_interrupt`

Some of these operations can take some time, especially when you are
in the exploratory phase and you don’t have that much information about
what type of solution you will obtain. For this reason, we have added
the ability to interrupt execution. Under the hood, we call
`cpp11::check_user_interrupt()`

once every second to check if
the user has requested for the process to be interrupted. Note that we
only check for user interruptions when we cannot determine the number of
results up front.

This means that if we initiate a process that will take a long time
or exhaust all of the available memory (e.g. we forget to put an upper
limit on the number of results, relax the tolerance, etc.), we can
simply hit `Ctrl + c`

, or `esc`

if using
`RStudio`

, to stop execution.

```
set.seed(123)
s = rnorm(1000)
## Oops!! We forgot to limit the output/put a loose tolerance
## There are as.numeric(comboCount(s, 20, T)) ~= 4.964324e+41
## This will either take a long long time, or all of your
## memory will be consumed!!!
##
## No problem... simply hit Ctrl + c or if in RStudio, hit esc
## or hit the "Stop" button
##
## system.time(testInterrupt <- partitionsGeneral(s, 20, TRUE, target = 0))
## Timing stopped at: 1.029 0.011 1.04
##
```

Generally, we encourage user to use iterators (See Combinatorial Iterators in RcppAlgos) as they offer greater flexibility. For example, with iterators it is easy to avoid resource consuming calls by only fetching a few results at a time.

Here is an example of how to investigate difficult problems due to combinatorial explosion without fear of having to restart R.

```
## We use "s" defined above
iter = partitionsIter(s, 20, TRUE, target = 0)
## Test one iteration to see if we need to relax the tolerance
system.time(iter@nextIter())
#> user system elapsed
#> 5.163 0.018 5.182
## 8 seconds per iteration is a bit much... Let's loosen things
## a little by increasing the tolerance from sqrt(.Machine$double.eps)
## ~= 1.49e-8 to 1e-5.
relaxedIter = partitionsIter(s, 20, TRUE, target = 0, tolerance = 1e-5)
system.time(relaxedIter@nextIter())
#> user system elapsed
#> 0.001 0.000 0.001
```