solve2dlp: Graphical solution of a two-dimensional linear programming...

In intpoint: linear programming solver by the interior point method and graphically (two dimensions)

Description

The graphical method for solving linear programming problems in two variables is implemented. The lines corresponding to the constraints are drawn; next, the coordinates of the corner points of the feasible region are computed, and the objective function is evaluated to obtain the optimal value. Finally, the objective function is drawn over the optimal point. If the interior point algorithm is loaded, then the sequence of points obtained by this method are shown. Details: 1. Graph the feasible region. 2. Compute the coordinates of the corner points. 3. Substitute the coordinates of the corner points into the objective function to see which gives the optimal value. This point is the solution to the linear programming problem. 4. If the feasible region is not bounded, this method can be misleading: optimal solutions always exist when the feasible region is bounded, but may or may not exist when the feasible region is unbounded. If the feasible region is unbounded, we are minimizing the objective function, and its coefficients are non-negative, then a solution exists, so this method yields the solution.

Usage

solve2dlp(t = 1, c = NULL, bA = NULL, A = NULL, bm = NULL, m = NULL,
 bM = NULL, M = NULL, z = 1, ip = 1, e = 1e-04, a1 = 1, a2 = 0.97)

Arguments

t	Type of problem. Put 1 in the case of a maximization problem, and -1 if it is a minimization one.
c	The vector corresponding to the coefficients of the objective function
bA	The vector corresponding to the right hand side constants (with =)
A	The coefficients of the problem constraints matrix A (with =)
bm	The vector corresponding to the right hand side constants (with <=)
m	The coefficients of the problem constraints matrix A (with <=)
bM	The vector corresponding to the right hand side constants (with >=)
M	The coefficients of the problem constraints matrix A (with >=)
z	Put 0 if you only want to show the feasible region, not the solution. Default is 1
ip	Put 1 if you want to solve the problem by the interior point method, and 0 if not. The sequence of calculated points is displayed. Default is 1
e	The value of ε. Default is 1e-04
a1	The value of α_1. Default is 1
a2	The value of α_2. Default is 0.97

Value

The graphical solution to the linear programming problem. Lines corresponding to the constrain are coloured in green, the corner points in blue and the optimal point (if there exists) in magenta (with the objective function in blue). The sequence of interior points is drawn in red.

Author(s)

Alejandro Quintela del Rio [e-mail]

References

Murty, K.G. (1983) Linear Programming, Wiley.

Karmarkar, N. (1984) A new polynomial-time algorithm for linear programming. Combinatorica 4, pp. 373-395.

Vanderbei, R.J., Meketon, M.S. and Freedman, B.A. (1986) A modification of Karmarkar's linear programming algorithm. Algorithmica 1, pp. 395-407.

Examples

# Max Z  = x2
# Subject to  2x1 - x2  = -2
#	         x1 + 2x2  = 8
#       	   x1, x2   >= 0
c=c(3,2)
M1=c(2,-1)
bM1=-2
m1=c(1,2)
bm1=8
solve2dlp(t=1, m=m1, bm=bm1, M=M1, bM=bM1, c=c, z=0, ip=0)

# A unbounded problem
#Z = x1 + 2x2
# Subject to  x1 + x2 = 1
#		x2 = 4
#		x1, x2 =0

m1<-c(0,1)
bm1<-4
M1<-c(1,1)
bM1<-1
c<-c(1,2)
solve2dlp(t=1, m=m1, bm=bm1, M=M1, bM=bM1, c=c, z=1, ip=1)

# a problem with several constraints
m1<-rbind(c(1,0), c(1,2),c(0,1))
bm1<-c(5,10,4)
c=c(1,3)
solve2dlp(t=1, m=m1, bm=bm1, c=c, z=1, ip=1)

intpoint documentation built on May 29, 2017, 8:59 p.m.