Modeling and solving

Ideally, given a problem, one just want to model and solve. That is what LocalSearchSolvers is aiming for. Here we only provide JuMP syntax.

Model

using CBLS, JuMP

model = Model(CBLS.Optimizer) # CBLS is an exported alias of LocalSearchSolvers

# add variables (cf Variables section)
# add constraints (cf Constraints section)
# add objective (cf Objectives section)

Solver

# run the solver. If no objectives are provided, it will look for a satisfying solution and stop
optimize!(model)

# extract the values (assuming X, a (collection of) variable(s) is the target)
solution = value.(X)

Solver options

LocalSearchSolvers.Options — Type

Options()

Arguments:

dynamic::Bool: is the model dynamic?
iteration::Union{Int, Float64}: limit on the number of iterations
print_level::Symbol: verbosity to choose among :silent, :minimal, :partial, :verbose
solutions::Int: number of solutions to return
specialize::Bool: should the types of the model be specialized or not. Usually yes for static problems. For dynamic in depends if the user intend to introduce new types. The specialized model is about 10% faster.
tabu_time::Int: DESCRIPTION
tabu_local::Int: DESCRIPTION
tabu_delta::Float64: DESCRIPTION
threads::Int: Number of threads to use
time_limit::Float64: time limit in seconds
`function Options(; dynamic = false, iteration = 10000, printlevel = :minimal, solutions = 1, specialize = !dynamic, tabutime = 0, tabulocal = 0, tabudelta = 0.0, threads = typemax(0), time_limit = Inf)

# Setting options in JuMP syntax: print_level, time_limit, iteration
model = Model(CBLS.Optimizer)
set_optimizer_attribute(model, "iteration", 100)
set_optimizer_attribute(model, "print_level", :verbose)
set_time_limit_sec(model, 5.0)

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