This is kind of part 15 of the series about: How to build a Constraint Programming solver? But it is also interesting for you if you don’t care about constraint programming.
You can learn how to use JuMP.jl as the modelling layer for your own solver.
Anyway these are the other posts so far in the ongoing series:
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Maybe I should have done that from the ground up but wasn’t sure how to and probably it was easier for the blog as well to start without a big code base. Therefore here it is now: Using JuMP.jl as the modelling layer for the constraint solver I’m creating for the past couple of months.
UserInterface change
Before I explain how it’s done I want to show how the user interface changes first:
How it was before
com = CS.init() # creating a constraint solver model
com_grid = Array{CS.Variable, 2}(undef, 9, 9)
for (ind,val) in enumerate(grid)
if val == 0
com_grid[ind] = add_var!(com, 1, 9)
else
com_grid[ind] = add_var!(com, 1, 9; fix=val)
end
end
for rc=1:9
#row
variables = com_grid[CartesianIndices((rc:rc,1:9))]
add_constraint!(com, CS.all_different([variables...]))
#col
variables = com_grid[CartesianIndices((1:9,rc:rc))]
add_constraint!(com, CS.all_different([variables...]))
end
which is a bit of the sudoku part. Now it looks like this:
m = Model(with_optimizer(CS.Optimizer))
@variable(m, 1 <= x[1:9,1:9] <= 9, Int)
# set variables
for r=1:9, c=1:9
if grid[r,c] != 0
@constraint(m, x[r,c] == grid[r,c])
end
end
for rc = 1:9
@constraint(m, x[rc,:] in CS.AllDifferentSet(9))
@constraint(m, x[:,rc] in CS.AllDifferentSet(9))
end
I think it looks a lot cleaner now but more importantly it is the standard interface for mathematical optimization solvers written in Julia.
This also means that if there will exist other constraint solvers in the future one can easily swap the…
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