Initial columns

The initial columns callback let you provide initial columns associated to each problem ahead the optimization. This callback is useful when you have an efficient heuristic that finds feasible solutions to the problem. You can then extract columns from the solutions and give them to Coluna through the callback. You have to make sure the columns you provide are feasible because Coluna won't check their feasibility. The cost of the columns will be computed using the perennial cost of subproblem variables.

Let us see an example with the following generalized assignment problem :

M = 1:3;
J = 1:5;
c = [1 1 1 1 1; 1.2 1.2 1.1 1.1 1; 1.3 1.3 1.1 1.2 1.4];
Q = [3, 2, 3];

with the following Coluna configuration

using JuMP, GLPK, BlockDecomposition, Coluna;

coluna = optimizer_with_attributes(
    Coluna.Optimizer,
    "params" => Coluna.Params(
        solver = Coluna.Algorithm.TreeSearchAlgorithm() # default branch-cut-and-price
    ),
    "default_optimizer" => GLPK.Optimizer # GLPK for the master & the subproblems
);

for which the JuMP model takes the form:

@axis(M_axis, M);
model = BlockModel(coluna);

@variable(model, x[m in M_axis, j in J], Bin);
@constraint(model, cov[j in J], sum(x[m, j] for m in M_axis) >= 1);
@constraint(model, knp[m in M_axis], sum(x[m, j] for j in J) <= Q[m]);
@objective(model, Min, sum(c[m, j] * x[m, j] for m in M_axis, j in J));

@dantzig_wolfe_decomposition(model, decomposition, M_axis)

subproblems = getsubproblems(decomposition)
specify!.(subproblems, lower_multiplicity = 0, upper_multiplicity = 1)

3-element Vector{Nothing}:
 nothing
 nothing
 nothing

Let's consider that the following assignment patterns are good candidates:

machine1 = [[1,2,4], [1,3,4], [2,3,4], [2,3,5]];
machine2 = [[1,2], [1,5], [2,5], [3,4]];
machine3 = [[1,2,3], [1,3,4], [1,3,5], [2,3,4]];

initial_columns = [machine1, machine2, machine3];

We can write the initial columns callback:

function initial_columns_callback(cbdata)
    # Retrieve the index of the subproblem (it will be one of the values in M_axis)
    spid = BlockDecomposition.callback_spid(cbdata, model)
    println("initial columns callback $spid")

    # Retrieve assignment patterns of a given machine
    for col in initial_columns[spid]
        # Create the column in the good representation
        vars = [x[spid, j] for j in col]
        vals = [1.0 for _ in col]

        # Submit the column
        MOI.submit(model, BlockDecomposition.InitialColumn(cbdata), vars, vals)
    end
end

initial_columns_callback (generic function with 1 method)

The initial columns callback is a function. It takes as argument cbdata which is a data structure that allows the user to interact with Coluna within the callback.

We provide the initial columns callback to Coluna through the following method:

MOI.set(model, BlockDecomposition.InitialColumnsCallback(), initial_columns_callback)

You can then optimize:

optimize!(model)

Coluna
Version 0.8.1 | https://github.com/atoptima/Coluna.jl
initial columns callback 3
initial columns callback 2
initial columns callback 1
***************************************************************************************
**** B&B tree root node
**** Local DB = -Inf, global bounds: [ -Inf , Inf ], time = 0.36 sec.
***************************************************************************************
  <st= 1> <it=  1> <et= 0.36> <mst= 0.00> <sp= 0.00> <cols= 3> <al= 0.00> <DB=   -5.3000> <mlp=    5.2000> <PB=Inf>
  <st= 1> <it=  2> <et= 0.36> <mst= 0.00> <sp= 0.00> <cols= 3> <al= 0.00> <DB=    4.9500> <mlp=    5.2000> <PB=Inf>
[ Info: Improving primal solution with value 5.1 is found during column generation
  <st= 1> <it=  3> <et= 0.36> <mst= 0.00> <sp= 0.00> <cols= 0> <al= 0.00> <DB=    5.1000> <mlp=    5.1000> <PB=5.1000>
 ──────────────────────────────────────────────────────────────────────────
                                  Time                    Allocations
                         ───────────────────────   ────────────────────────
    Tot / % measured:         1.52s /  23.8%            150MiB /  31.0%

 Section         ncalls     time    %tot     avg     alloc    %tot      avg
 ──────────────────────────────────────────────────────────────────────────
 Coluna               1    361ms  100.0%   361ms   46.6MiB  100.0%  46.6MiB
   SolveLpForm        3    470μs    0.1%   157μs    148KiB    0.3%  49.5KiB
 ──────────────────────────────────────────────────────────────────────────
[ Info: Terminated
[ Info: Primal bound: 5.1
[ Info: Dual bound: 5.1

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