FLSSS (version 9.2.8)

decomposeArbFLSSS: arbFLSSS decomposition

Description

Decompose an arbFLSSS instance into sub-problems for distributed computing.

Usage

decomposeArbFLSSS(
  len,
  V,
  target,
  approxNinstance = 1000L,
  maxCore = 7L,
  ksumTable = NULL,
  ksumK = 4L,
  ksumTableSizeScaler = 30L,
  verbose = TRUE
  )

Value

A list of two:

$arbFLSSSobjects: a list. Each element is an arbFLSSS object that would be supplied to arbFLSSSobjRun().

$solutionsFound: a list. Solutions found during decomposition.

Arguments

len

See the same argument in arbFLSSS().

V

See the same argument in arbFLSSS().

target

See the same argument in arbFLSSS().

approxNinstance

See the same argument in arbFLSSS().

maxCore

See the same argument in arbFLSSS(). The decomposition is single-threaded, but building the k-sum accelerator is multithreaded.

ksumTable

Either NULL or the return value from ksumHash(). ksumTable is not necessary for the decomposition. The function merely store a reference to it in every arbFLSSS object.

ksumK

See the same argument in arbFLSSS(). If ksumK >= 3 and ksumTable == NULL, the function will build a k-sum accelerator and store a reference in every arbFLSSS object.

ksumTableSizeScaler

See the same argument in arbFLSSS().

verbose

See the same argument in arbFLSSS().

Details

The rationale follows decomposeMflsss(). The pair decomposeArbFLSSS() and arbFLSSSobjRun() makes up the distributed computing counterpart of arbFLSSS().

Examples

set.seed(42)
d = 5L # Set dimension.
N = 60L # Set size.
len = 10L # Subset size.
roundN = 2L # For rounding the numeric values before conversion to strings.


V = matrix(round(runif(N * d, -1e5, 1e5), roundN), nrow = N) # Make superset.
sol = sample(N, len) # Make a solution.
target = round(colSums(V[sol, ]), roundN) # Target subset sum.


optionSave = options()
options(scipen = 999) # Ensure numeric => string conversion does not
# produce strings like 2e-3.
Vstr = matrix(as.character(V), nrow = N) # String version of V.
targetStr = as.character(target)




# ==============================================================================
# Uncomment the block below since it takes a few seconds to run, which 
# does not pass CRAN's Valgrind test.
# ==============================================================================
# system.time({
#   theDecomposed = FLSSS::decomposeArbFLSSS(
#     len = len, V = Vstr, target = targetStr, approxNinstance = 1000,
#     maxCore = 2, ksumTable = NULL, ksumK = 4, verbose = TRUE)
# })
# 
# 
# # Check if any solution has been found during decomposition.
# str(theDecomposed$solutionsFound)
# 
# 
# # Check the first arbFLSSS object.
# str(theDecomposed$arbFLSSSobjects[[1]])
# 
# 
# options(optionSave)