A NodeJS module for createing and solving simple Linear Programs using lp_solve.
Example:
// solve http://people.brunel.ac.uk/~mastjjb/jeb/or/morelp.html
var lpsolve = require('lp_solve');
var Row = lpsolve.Row;
var lp = new lpsolve.LinearProgram();
var x = lp.addColumn('x'); // lp.addColumn('x', true) for integer variable
var y = lp.addColumn('y'); // lp.addColumn('y', false, true) for binary variable
var objective = new Row().Add(x, 1).Add(y, 1);
lp.setObjective(objective);
var machineatime = new Row().Add(x, 50).Add(y, 24);
lp.addConstraint(machineatime, 'LE', 2400, 'machine a time')
var machinebtime = new Row().Add(x, 30).Add(y, 33);
lp.addConstraint(machinebtime, 'LE', 2100, 'machine b time')
lp.addConstraint(new Row().Add(x, 1), 'GE', 75 - 30, 'meet demand of x')
lp.addConstraint(new Row().Add(y, 1), 'GE', 95 - 90, 'meet demand of y')
console.log(lp.dumpProgram());
console.log(lp.solve());
console.log('objective =', lp.getObjectiveValue())
console.log('x =', lp.get(x));
console.log('y =', lp.get(y));
console.log('machineatime =', lp.calculate(machineatime));
console.log('machinebtime =', lp.calculate(machinebtime));Output
minimize: +1 x +1 y
subject to
machine a time: +50 x +24 y <= 2400
machine b time: +30 x +33 y <= 2100
meet demand of x: +1 x >= 45
meet demand of y: +1 y >= 5
Model name: '' - run #1
Objective: Minimize(R0)
SUBMITTED
Model size: 4 constraints, 2 variables, 6 non-zeros.
Sets: 0 GUB, 0 SOS.
Using DUAL simplex for phase 1 and PRIMAL simplex for phase 2.
The primal and dual simplex pricing strategy set to 'Devex'.
Optimal solution 50 after 2 iter.
Excellent numeric accuracy ||*|| = 0
MEMO: lp_solve version 5.5.2.0 for 64 bit OS, with 64 bit REAL variables.
In the total iteration count 2, 0 (0.0%) were bound flips.
There were 0 refactorizations, 0 triggered by time and 0 by density.
... on average 2.0 major pivots per refactorization.
The largest [LUSOL v2.2.1.0] fact(B) had 5 NZ entries, 1.0x largest basis.
The constraint matrix inf-norm is 50, with a dynamic range of 50.
Time to load data was 0.001 seconds, presolve used 0.004 seconds,
... 0.003 seconds in simplex solver, in total 0.008 seconds.
{ code: 0, description: 'OPTIMAL' }
objective = 50
x = 45
y = 5
machineatime = 2370
machinebtime = 1515