| Title: | Generate Feasible Samples of a Knapsack Problem |
| Version: | 0.1.1 |
| Date: | 2024-01-31 |
| Author: | Chin Soon Lim [aut] |
| Maintainer: | Chin Soon Lim <chinsoon12@hotmail.com> |
| Description: | The sampl.mcmc function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints. |
| Depends: | R (≥ 3.3.0) |
| Imports: | lpSolve, stats |
| License: | GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE] |
| RoxygenNote: | 7.2.3 |
| URL: | https://github.com/chinsoon12/KnapsackSampling |
| BugReports: | https://github.com/chinsoon12/KnapsackSampling |
| NeedsCompilation: | no |
| Packaged: | 2024-01-31 01:44:15 UTC; chinsoon |
| Repository: | CRAN |
| Date/Publication: | 2024-01-31 08:30:08 UTC |
Flip a 1 and a 0 simultaneously
Description
Flip a 1 and a 0 simultaneously
Usage
flip01(x)
Arguments
x |
an integer or logical vector |
Value
x an integer vector
Generate an initial feasible solution by solving a linear programming with binary variables
Description
Generate an initial feasible solution by solving a linear programming with binary variables
Usage
initState(numVar, objVec = runif(numVar), constraints = NULL)
Arguments
numVar |
- number of variables |
objVec |
- objective function as a numeric vector |
constraints |
- a list of list of constraints with constr.mat, constr.dir, constr.rhs in each sublist |
Value
a binary vector containing a feasible solution
Examples
#see documentation for sampl.mcmc
Generate feasible solutions to a knapsack problem using Markov Chain Monte Carlo
Description
Generate feasible solutions to a knapsack problem using Markov Chain Monte Carlo
Usage
sampl.mcmc(init, numSampl, maxIter = 2 * numSampl, constraints = NULL)
Arguments
init |
- an initial feasible solution |
numSampl |
- number of samples to be generated |
maxIter |
- maximum number of iterations to be run to prevent infinite loop |
constraints |
- a list of list of constraints with constr.mat, constr.dir, constr.rhs in each sublist. Please see example for an example of constraints. |
Value
a matrix of {0, 1} with each row representing a sample
Examples
#number of variables
N <- 100
#number of variables in each group
grpLen <- 10
#equality matrix
A <- matrix(c(rep(1, N)), ncol=N, byrow=TRUE)
#inequality matrix
G <- matrix(c(rep(1, grpLen), rep(0, N - grpLen),
rep(c(0,1), each=grpLen), rep(0, N - 2*grpLen)), ncol=N, byrow=TRUE)
#construct a list of list of constraints
constraints <- list(
list(constr.mat=A, constr.dir=rep("==", nrow(A)), constr.rhs=c(20)),
list(constr.mat=G, constr.dir=rep("<=", nrow(G)), constr.rhs=c(5, 5)),
list(constr.mat=G, constr.dir=rep(">=", nrow(G)), constr.rhs=c(1, 2))
)
#generate an initial feasible solution
init <- initState(N, constraints=constraints)
#create feasible solutions to knapsack problems subject to constraints
samples <- sampl.mcmc(init, 50, constraints=constraints)