It can be useful to modify models after they have been created and solved, for example when we are solving many similar models in succession or generating the model dynamically (e.g. column generation). Additionally it is sometimes desirable for the solver to re-start from the last solution to reduce running times for successive solves ("hot-start"). Where available, JuMP exposes this functionality.
Some solvers do not expose the ability to modify a model after creation - the model must be constructed from scratch each time. JuMP will use the ability to modify problems exposed by the solver if possible, and will still work even if the solver does not support this functionality by passing the complete problem to the solver every time.
As before, variables can be added using the @variable macro. To remove a variable,
one can set the bounds on that variable to zero, e.g.:
setlowerbound(x, 0.0) setupperbound(x, 0.0)
While bound updates are applied immediately in JuMP, variable bound changes are not
transmitted to the solver until solve is called again.
To add variables that appear in existing constraints, e.g. in column generation,
there is an alternative form of the defVar macro:
@variable(m, x, objective = objcoef, inconstraints = constrrefs, coefficients = values) @variable(m, x >= lb, objective = objcoef, inconstraints = constrrefs, coefficients = values) @variable(m, x <= ub, objective = objcoef, inconstraints = constrrefs, coefficients = values) @variable(m, lb <= x <= ub, objective = objcoef, inconstraints = constrrefs, coefficients = values) @variable(m, lb <= x <= ub, Int, objective = objcoef, inconstraints = constrrefs, coefficients = values) # Types are supported
where objcoef is the coefficient of the variable in the new problem,
constrrefs is a vector of ConstraintRef, and values is a vector
of numbers. To give an example, consider the following code snippet:
m = Model() @variable(m, 0 <= x <= 1) @variable(m, 0 <= y <= 1) @objective(m, Max, 5x + 1y) @constraint(m, con, x + y <= 1) solve(m) # x = 1, y = 0 @variable(m, 0 <= z <= 1, objective = 10.0, inconstraints = [con], coefficients = [1.0]) # The constraint is now x + y + z <= 1 # The objective is now 5x + 1y + 10z solve(m) # z = 1
In some situations you may be adding all variables in this way. To do so, first define a set of empty constraints, e.g.
m = Model() @constraint(m, con, 0 <= 1) @objective(m, Max, 0) @variable(m, 0 <= x <= 1, objective = 5, inconstraints = [con], coefficients = [1.0]) @variable(m, 0 <= y <= 1, objective = 1, inconstraints = [con], coefficients = [1.0]) @variable(m, 0 <= z <= 1, objective = 10, inconstraints = [con], coefficients = [1.0]) solve(m)
JuMP does not currently support changing constraint coefficients. For less-than and greater-than constraints, the right-hand-side can be changed, e.g.:
@constraint(m, mycon, x + y <= 4) solve(m) JuMP.setRHS(mycon, 3) # Now x + y <= 3 solve(m) # Hot-start for LPs
To change the objective, simply call @objective again - the previous objective
function and sense will be replaced.