Significant speed improvements can sometimes be obtained using the LinearExpression object when there are long, dense, linear expressions. The arguments are
constant, linear_coeffs, linear_varswhere the second and third arguments are lists that must be of the same length. Here is a simple example that illustrates the syntax. This example creates two constraints that are the same; in this particular case the LinearExpression component would offer very little improvement because Pyomo would be able to detect that campe2 is a linear expression:
>>> import pyomo.environ as pyo >>> from pyomo.core.expr.numeric_expr import LinearExpression >>> model = pyo.ConcreteModel() >>> model.nVars = pyo.Param(initialize=4) >>> model.N = pyo.RangeSet(model.nVars) >>> model.x = pyo.Var(model.N, within=pyo.Binary) >>> >>> model.coefs = [1, 1, 3, 4] >>> >>> model.linexp = LinearExpression(constant=0, ... linear_coefs=model.coefs, ... linear_vars=[model.x[i] for i in model.N]) >>> def caprule(m): ... return m.linexp <= 6 >>> model.capme = pyo.Constraint(rule=caprule) >>> >>> def caprule2(m): ... return sum(model.coefs[i-1]*model.x[i] for i in model.N) <= 6 >>> model.capme2 = pyo.Constraint(rule=caprule2)
Warning
The lists that are passed to LinearExpression are not copied, so caution must be exercised if they are modified after the component is constructed.