latin {magic}

Random latin squares

Description

Various functionality for generating random latin squares

Usage

incidence(a)
is.incidence(a, include.improper)
is.incidence.improper(a)
unincidence(a)
inc_to_inc(a)
another_latin(a)
another_incidence(i)
rlatin(n,size=NULL,start=NULL,burnin=NULL)

Arguments

a

A latin square

i

An incidence array

n, include.improper, size, start, burnin

Various control arguments; see details section

Details

• Function incidence() takes an integer array (specifically, a latin square) and returns the incidence array as per Jacobson and Matthew 1996

• Function is.incidence() tests for an array being an incidence array; if argument include.improper is TRUE, admit an improper array

• Function is.incidence.improper() tests for an array being an improper array

• Function unincidence() converts an incidence array to a latin square

• Function another_latin() takes a latin square and returns a different latin square

• Function another_incidence() takes an incidence array and returns a different incidence array

• Function rlatin() generates a (Markov) sequence of random latin squres, arranged in a 3D array. Argument n specifies how many to generate; argument size gives the size of latin squares generated; argument start gives the start latin square (it must be latin and is checked with is.latin()); argument burnin gives the burn-in value (number of Markov steps to discard).

  Default value of NULL for argument size means to take the size of argument start; default value of NULL for argument start means to use circulant(size)

  As a special case, if argument size and start both take the default value of NULL, then argument n is interpreted as the size of a single random latin square to be returned; the other arguments take their default values. This ensures that “rlatin(n)” returns a single random n\times n latin square.

From Jacobson and Matthew 1996, an n\times n latin square LS is equivalent to an n\times n\times n array A with entries 0 or 1; the dimensions of A are identified with the rows, columns and symbols of LS; a 1 appears in cell (r,c,s) of A iffi the symbol s appears in row r, column s of LS. Jacobson and Matthew call this an incidence cube.

The notation is readily generalized to latin hypercubes and incidence() is dimensionally vectorized.

An improper incidence cube is an incidence cube that includes a single -1 entry; all other entries must be 0 or 1; and all line sums must equal 1.

Author(s)

Robin K. S. Hankin

References

M. T. Jacobson and P. Matthews 1996. “Generating uniformly distributed random latin squares”. Journal of Combinatorial Designs, volume 4, No. 6, pp405–437

See Also

is.magic

Examples

rlatin(5)
rlatin(n=2, size=4, burnin=10)

# An example that allows one to optimize an objective function
# [here f()] over latin squares:
gr <- function(x){ another_latin(matrix(x,7,7)) }
set.seed(0)
index <- sample(49,20)
f <- function(x){ sum(x[index])}
jj <- optim(par=as.vector(latin(7)), fn=f, gr=gr, method="SANN", control=list(maxit=10))
best_latin <- matrix(jj$par,7,7)
print(best_latin)
print(f(best_latin))

#compare starting value:
f(circulant(7))