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CurrentModule = Coluna

Column generation

Coluna provides an interface and generic functions to implement a multi-stage column generation algorithm together with a default implementation of this algorithm.

In this section, we are first going to present the generic functions, the implementation with some theory backgrounds and then give the references of the interface.

You can find the generic functions and the interface in the ColGen submodule and the default implementation in the Algorithm submodule at src/Algorithm/colgen.

Context

The ColGen submodule provides an interface and generic functions to implement a column generation algorithm. The implementation depends on an object called context.

Coluna.ColGen.AbstractColGenContext

Coluna provides two types of context:

Coluna.Algorithm.ColGenContext
Coluna.Algorithm.ColGenPrinterContext

Generic functions

Generic functions are the core of the column generation algorithm. There are three generic functions:

Coluna.ColGen.run!

See the main loop section for more details.

Coluna.ColGen.run_colgen_phase!

See the phase loop section for more details.

Coluna.ColGen.run_colgen_iteration!

See the column generation iteration section for more details.

They are independent of any other submodule of Coluna. You can use them to implement your own column generation algorithm.

Reformulation

The default implementation works with a reformulated problem contained in MathProg.Reformulation where master and subproblems are MathProg.Formulation objects.

The master has the following form:

$$\begin{aligned} \min \quad& \sum_{k \in K} c^k \lambda^k+\bar{c} y & \\\ \text{s.t.} \quad& \sum_{k \in K} A^k \lambda^k+\bar{A} y \geq a & (1)\\\ & l_k \leq \mathbf{1} \lambda^k \leq u_k & (2) \\\ & \bar{l} \leq y \leq \bar{u} & (3) \end{aligned}$$

where $\lambda$ are the master columns, $y$ are the pure master variables, constraints (1) are the linking constraints, constraints (2) are the convexity constraints that depend on $l_k$ and $u_k$ (e.g. the lower and upper multiplicity of the subproblem $k$ respectively), and constraints (3) are the bounds on the pure master variables.

The subproblems have the following form:

$$\begin{aligned} \min \quad& cx + 0z \\\ \text{s.t.} \quad& Bx \geq b \\\ & 1 \leq z \leq 1 \end{aligned}$$

where $x$ are the subproblem variables, $z$ is a setup variable that always takes the value one in a solution to the subproblem.

The coefficients of the columns in constraints (1) and (2) of the master are computed using representative variables of the subproblems. You can read this section (TODO Natacha) to understand how we map the subproblem solutions into master columns.

References:

Coluna.ColGen.get_reform
Coluna.ColGen.get_master
Coluna.ColGen.get_pricing_subprobs
Coluna.ColGen.is_minimization

Main loop

This is a description of how the Coluna.ColGen.run! generic function behaves in the default implementation.

The main loop stops when the Coluna.ColGen.stop_colgen method returns true. This is the case when one of the following conditions holds:

  • the master or a pricing subproblem is infeasible
  • the time limit is reached
  • the maximum number of iterations is reached

Otherwise, the main loop runs until there is no more phase or stage to execute.

The method returns:

Coluna.Algorithm.ColGenOutput

References:

Coluna.ColGen.stop_colgen
Coluna.ColGen.setup_reformulation!
Coluna.ColGen.setup_context!
Coluna.ColGen.AbstractColGenOutput
Coluna.ColGen.colgen_output_type
Coluna.ColGen.new_output

Phase loop

This is a description of how the Coluna.ColGen.run_colgen_phase! generic function behaves in the default implementation.

This function is responsible for maintaining the incumbent dual bound and the incumbent master IP primal solution.

The phase loop stops when the Coluna.ColGen.stop_colgen_phase method returns true. This is the case when one of the following conditions holds:

  • the maximum number of iterations is reached
  • the time limit is reached
  • the master is infeasible
  • the master is unbounded
  • a pricing subproblem is infeasible
  • a pricing subproblem is unbounded
  • there is no new column generated at the last iteration
  • there is a new constraint or valid inequality in the master
  • the incumbent dual bound and the primal master LP solution value converged

The method returns:

Coluna.Algorithm.ColGenPhaseOutput

References:

Coluna.ColGen.stop_colgen_phase
Coluna.ColGen.before_colgen_iteration
Coluna.ColGen.after_colgen_iteration
Coluna.ColGen.is_better_dual_bound

Phase iterator

In the first iterations, the restricted master LP contains a few columns and may be infeasible. To prevent this, we introduced artificial variables $v$ and we activate/deactivate these variables depending on whether we want to prove the infeasibility of the master LP or find the optimal LP solution. The default implementation provides three phases:

Coluna.Algorithm.ColGenPhase0
Coluna.Algorithm.ColGenPhase1
Coluna.Algorithm.ColGenPhase2

Column generation always starts with Phase 0.

The default implementation of the phase iterator belongs to the following type:

Coluna.Algorithm.ColunaColGenPhaseIterator

Transitions between the phases depend on four conditions:

  • (A) the presence of artificial variables in the master LP solution
  • (B) the generation of new essential constraints (may happen when a new master IP solution is found)
  • (C) the current stage is exact
  • (D) column generation converged

Transitions are the following:

flowchart TB;
    id1(Phase 0)
    id2(Phase 1)
    id3(Phase 2)
    id4(end)
    id5(error)
    id1 --A & !B & C--> id2
    id1 --!A & !B & C & D--> id4
    id1 -- otherwise --> id1
    id2 --!A & !B--> id3
    id2 --A & C & D--> id4
    id2 -- otherwise --> id2
    id3 -- !B & C & D --> id4
    id3 -- otherwise --> id3
    id3 -- B --> id2
    id3 -- A --> id5
    style id5 stroke:#f66
Loading

References:

Coluna.ColGen.AbstractColGenPhase
Coluna.ColGen.AbstractColGenPhaseIterator
Coluna.ColGen.new_phase_iterator
Coluna.ColGen.initial_phase
Coluna.ColGen.decrease_stage
Coluna.ColGen.next_phase

Phase output

Coluna.ColGen.AbstractColGenPhaseOutput
Coluna.ColGen.colgen_phase_output_type
Coluna.ColGen.new_phase_output

Stages

A stage is a set of consecutive iterations in which we use a given pricing solver. The aim is to speed up the resolution of the pricing problem by first using an approximate but fast pricing algorithm and then switching to increasingly less heuristic algorithms until the last stage where an exact solver is used. and an exact solver at the last stage. Given a pricing solver, when the column generation does not progress anymore or the pricing solver does not return any new column, the default implementation switch to a more exact pricing solver. Stages are created using the stages_pricing_solver_ids of the ColumnGenerationAlgorithm parameter object. The default implementation implements the interface around the following object:

Coluna.Algorithm.ColGenStageIterator

References:

Coluna.ColGen.AbstractColGenStage
Coluna.ColGen.AbstractColGenStageIterator
Coluna.ColGen.new_stage_iterator
Coluna.ColGen.initial_stage
Coluna.ColGen.next_stage
Coluna.ColGen.get_pricing_subprob_optimizer
Coluna.ColGen.stage_id
Coluna.ColGen.is_exact_stage

Column generation iteration

This is a description of how the Coluna.ColGen.run_colgen_iteration! generic function behaves in the default implementation.

These are the main steps of a column generation iteration without stabilization. Click on the step to go to the relevant section.

flowchart TB;
    id1(Optimize master LP)
    id2{{Solution to master LP is integer?}}
    id3(Update incumbent primal solution if better than current one)
    id4(Compute reduced cost of subproblem variables)
    id5{{Subproblem iterator}}
    id6(Optimize pricing subproblem)
    id7(Push subproblem solution into set)
    id8(Compute dual bound)
    id9(Insert columns)
    id10(Iteration output)
    id1 --> id2
    id2 --yes--> id3
    id2 --no--> id4
    id3 --> id4
    id4 --> id5
    id5 --subproblem--> id6
    id6 --> id7
    id7 --> id5
    id5 --end--> id8
    id8 --> id9
    id9 --> id10
    click id1 href "#Optimize-master-LP" "Link to doc"
    click id2 href "#Check-integrality-of-the-master-LP-solution" "Link to doc"
    click id3 href "#Update-incumbent-primal-solution" "Link to doc"
    click id4 href "#Reduced-costs-calculation" "Link to doc"
    click id5 href "#Pricing-subproblem-iterator" "Link to doc"
    click id6 href "#Pricing-subproblem-optimization" "Link to doc"
    click id7 href "#Set-of-generated-columns" "Link to doc"
    click id8 href "#Dual-bound-calculation" "Link to doc"
    click id9 href "#Columns-insertion" "Link to doc"
    click id10 href "#Iteration-output" "Link to doc"
Loading

Optimize master LP

At each iteration, the algorithm requires a dual solution to the master LP to compute the reduced cost of subproblem variables.

The default implementation optimizes the master with an LP solver through MathOptInterface. It returns a primal and a dual solution.

In the default implementation, the master LP output is in the following data structure:

Coluna.Algorithm.ColGenMasterResult

References:

Coluna.ColGen.optimize_master_lp_problem!

You can see the additional methods to implement in the result data structures section.

Go back to the column generation iteration overview.

Check the integrality of the master LP solution

The algorithm checks the integrality of the primal solution to the master LP to improve the global primal bound of the branch-cut-price algorithm.

By default, the integrality check is done using the MathProg.proj_cols_is_integer method. It implements the mapping procedure from the paper "F. Vanderbeck, Branching in branch-and-price: a generic scheme, Math.Prog. (2011)". Basically, it sorts the column used in the master LP primal solution in lexicographic order. It assigns a weight to each column equal to the value of the column in the master LP solution. It then forms columns of weight one by accumulating the columns of the fractional solution. If columns are integral, the solution is integral. This is a heuristic procedure so it can miss some integer solutions.

In the case the pricing subproblems are solved by a callback, and some subproblem integer variables are "hidden" from Coluna (values of these variables are usually stored in CustomData associated with the pricing problem solution), the mapping procedure may not be valid. In this case, the integrality should be checked in the "strict" way, i.e., by explicitly verifying that all columns are integer.

Integrality check procedure is set using parameter strict_integrality_check (false by default) of the ColumnGenerationAlgorithm.

If the solution is integral, the essential cut callback is called to make sure it is feasible.

References:

Coluna.ColGen.check_primal_ip_feasibility!
Coluna.ColGen.is_better_primal_sol

Go back to the column generation iteration overview.

Update incumbent primal solution

If the solution to master LP is integral and better than the current best one, we need to update the incumbent. This solution is then used by the tree-search algorithm in the bounding mechanism that prunes the nodes.

References:

Coluna.ColGen.update_inc_primal_sol!

Go back to the column generation iteration overview.

Reduced costs calculation

Reduced costs calculation is written as a math operation in the run_colgen_iteration! generic function. As a consequence, the dual solution to the master LP and the implementation of the two following methods must return data structures that support math operations.

To speed up this operation, we cache data in the following data structure:

Coluna.Algorithm.ReducedCostsCalculationHelper

Reduced costs calculation also requires the implementation of the two following methods:

Coluna.ColGen.update_master_constrs_dual_vals!
Coluna.ColGen.update_reduced_costs!
Coluna.ColGen.get_subprob_var_orig_costs
Coluna.ColGen.get_subprob_var_coef_matrix
Coluna.ColGen.update_sp_vars_red_costs!

Go back to the column generation iteration overview.

Pricing subproblem iterator

The pricing strategy is basically an iterator used to iterate over the pricing subproblems to optimize at each iteration of the column generation. The context can serve as a memory of the pricing strategy to change the way we iterate over subproblems between each column generation iteration.

The default implementation iterates over all subproblems.

Here are the references for the interface:

Coluna.ColGen.AbstractPricingStrategy
Coluna.ColGen.get_pricing_strategy
Coluna.ColGen.pricing_strategy_iterate

Go back to the column generation iteration overview.

Pricing subproblem optimization

At each iteration, the algorithm requires primal solutions to the pricing subproblems. The generic function supports multi-column generation so you can return any number of solutions.

The default implementation supports optimization of the pricing subproblems using a MILP solver or a pricing callback. Non-robust valid inequalities are not supported by MILP solvers as they change the structure of the subproblems. When using a pricing callback, you must be aware of how Coluna calculates the reduced cost of a column:

The reduced cost of a column is split into three contributions:

  • the contribution of the subproblem variables that is the primal solution cost given the reduced cost of subproblem variables
  • the contribution of the non-robust constraints (i.e. master constraints that cannot be expressed using subproblem variables except the convexity constraint) that is not supported by MILP solver but that you must take into account in the pricing callback
  • the contribution of the master convexity constraint that is automatically taken into account by Coluna once the primal solution is returned.

Therefore, when you use a pricing callback, you must not discard some columns based only on the primal solution cost because you don't know the contribution of the convexity constraint.

Coluna.Algorithm.GeneratedColumn    
Coluna.Algorithm.ColGenPricingResult

References:

Coluna.ColGen.optimize_pricing_problem!

You can see the additional methods to implement in the result data structures section.

Go back to the column generation iteration overview.

Set of generated columns

You can define your data structure to manage the columns generated at a given iteration. Columns are inserted after the optimization of all pricing subproblems to allow the parallelization of the latter.

In the default implementation, we use the following data structure:

Coluna.Algorithm.ColumnsSet
Coluna.Algorithm.SubprobPrimalSolsSet

In the default implementation, push_in_set! is responsible for checking if the column has improving reduced cost. Only columns with improving reduced cost are inserted in the set. The push_in_set! is also responsible to insert he best primal solution to each pricing problem into the SubprobPrimalSolsSet object.

References:

Coluna.ColGen.set_of_columns
Coluna.ColGen.push_in_set!

Go back to the column generation iteration overview.

Dual bound calculation

In the default implementation, given a vector $\pi \geq 0$ of dual values to the master constraints (1), the Lagrangian dual function is given by:

$$L(\pi) = \pi a + \sum_{k \in K} \max_{l_k \leq \mathbf{1} \lambda^k \leq u^k} (c^k - \pi A^k)\lambda^k + \max_{ \bar{l} \leq y \leq \bar{u}} (\bar{c} - \pi \bar{A})y$$

Let:

  • element $z_k(\pi) \leq \min_i (c^k_i - \pi A^k_i)$ be a lower bound on the solution value of the pricing problem
  • element $\bar{z}_j(\pi) = \bar{c} - \pi \bar{A}$ be the reduced cost of pure master variable $y_j$

Then, the Lagrangian dual function can be lower bounded by:

$$L(\pi) \geq \pi a + \sum_{k \in K} \max\{ z_k(\pi) \cdot l_k, z_k(\pi) \cdot u_k \} + \sum_{j \in J} \max\{ \bar{z}_j(\pi) \cdot \bar{l}_j, \bar{z}_j(\pi) \cdot \bar{u}_j\}$$

More precisely:

  • the first term is the contribution of the master obtained by removing the contribution of the convexity constraints (computed by ColGen.Algorithm._convexity_contrib), and the pure master variables (but you should see the third term) from the master LP solution value
  • the second term is the contribution of the subproblem variables which is the sum of the best solution value of each pricing subproblem multiplied by the lower and upper multiplicity of the subproblem depending on whether the reduced cost is negative or positive (this is computed by ColGen.Algorithm._subprob_contrib)
  • the third term is the contribution of the pure master variables which is taken into account by master LP value.

Therefore, we can compute the Lagrangian dual bound as follows:

master_lp_obj_val - convexity_contrib + sp_contrib

However, if the smoothing stabilization is active, we compute the dual bound at the sep-point. As a consequence, we can't use the master LP value because it corresponds to the dual solution at the out-point. We therefore need to compute the lagrangian dual bound by strictly applying the above formula.

References:

Coluna.ColGen.compute_sp_init_pb
Coluna.ColGen.compute_sp_init_db
Coluna.ColGen.compute_dual_bound

Go back to the column generation iteration overview.

Columns insertion

The default implementation inserts into the master all the columns stored in the ColumnsSet object.

Reference:

Coluna.ColGen.insert_columns!

Go back to the column generation iteration overview.

Iteration output

Coluna.Algorithm.ColGenIterationOutput

References:

Coluna.ColGen.AbstractColGenIterationOutput
Coluna.ColGen.colgen_iteration_output_type
Coluna.ColGen.new_iteration_output

Go back to the column generation iteration overview.

Getters for Result data structures

Method name Master Pricing
is_unbounded X X
is_infeasible X X
get_primal_sol X
get_primal_sols X
get_dual_sol X
get_obj_val X
get_primal_bound X
get_dual_bound X

References:

Coluna.ColGen.is_unbounded
Coluna.ColGen.is_infeasible
Coluna.ColGen.get_primal_sol
Coluna.ColGen.get_primal_sols
Coluna.ColGen.get_dual_sol
Coluna.ColGen.get_obj_val
Coluna.ColGen.get_primal_bound

Go back to the column generation iteration overview.

Getters for Output data structures

Method name ColGen Phase Iteration
get_nb_new_cols X
get_master_ip_primal_sol X X X
get_master_lp_primal_sol X
get_master_dual_sol X
get_dual_bound X X
get_master_lp_primal_bound X
is_infeasible X

References:

Coluna.ColGen.get_nb_new_cols
Coluna.ColGen.get_master_ip_primal_sol
Coluna.ColGen.get_master_lp_primal_sol
Coluna.ColGen.get_master_dual_sol
Coluna.ColGen.get_master_lp_primal_bound

Go back to the column generation iteration overview.

Stabilization

Coluna provides a default implementation of the smoothing stabilization with a self-adjusted $\alpha$ parameter, $0 \leq \alpha < 1$.

At each iteration of the column generation algorithm, instead of generating columns for the dual solution to the master LP, we generate columns for a perturbed dual solution defined as follows:

$$\pi^{\text{sep}} = \alpha \pi^{\text{in}} + (1-\alpha) \pi^{\text{out}}$$

where $\pi^{\text{in}}$ is the dual solution that gives the best Lagrangian dual bound so far (also called stabilization center) and $\pi^{\text{out}}$ is the dual solution to the master LP at the current iteration. This solution is returned by the default implementation of Coluna.ColGen.get_stab_dual_sol.

Some elements of the column generation change when using stabilization.

  • Columns are generated using the smoothed dual solution $\pi^{\text{sep}}$ but we still need to compute the reduced cost of the columns using the original dual solution $\pi^{\text{out}}$.
  • The dual bound is computed using the smoothed dual solution $\pi^{\text{sep}}$.
  • The pseudo bound is computed using the smoothed dual solution $\pi^{\text{sep}}$.
  • The smoothed dual bound can result in the generation of no improving columns. This is called a misprice. In that case, we need to move away from the stabilization center $\pi^{\text{in}}$ by decreasing $\alpha$.

When using self-adjusted stabilization, the smoothing coefficient $\alpha$ is adjusted to make the smoothed dual solution $\pi^{\text{sep}}$ closer to the best possible dual solution on the line between $\pi^{\text{in}}$ and $\pi^{\text{out}}$ (i.e. where the subgradient of the current primal solution is perpendicular to the latter line). To compute the subgradient, we use the following data structure:

Coluna.Algorithm.SubgradientCalculationHelper

References:

Coluna.ColGen.setup_stabilization!
Coluna.ColGen.update_stabilization_after_master_optim!
Coluna.ColGen.get_stab_dual_sol
Coluna.ColGen.check_misprice
Coluna.ColGen.update_stabilization_after_pricing_optim!
Coluna.ColGen.update_stabilization_after_misprice!
Coluna.ColGen.update_stabilization_after_iter!
Coluna.ColGen.get_output_str