Words of warning

This tutorial was generated using Literate.jl. Download the source as a .jl file. Download the source as a .ipynb file.

SDDP is a powerful solution technique for multistage stochastic programming. However, there are a number of subtle things to be aware of before creating your own models.

Relatively complete recourse

Models built in SDDP.jl need a property called relatively complete recourse.

One definition of relatively complete recourse is that all feasible decisions (not necessarily optimal) in a subproblem lead to feasible decisions in future subproblems.

For example, in the following problem, one feasible first stage decision is x.out = 0. But this causes an infeasibility in the second stage which requires x.in >= 1. This will throw an error about infeasibility if you try to solve.

using SDDP, HiGHS

model = SDDP.LinearPolicyGraph(
    stages = 2,
    lower_bound = 0,
    optimizer = HiGHS.Optimizer,
) do sp, t
    @variable(sp, x >= 0, SDDP.State, initial_value = 1)
    if t == 2
        @constraint(sp, x.in >= 1)
    end
    @stageobjective(sp, x.out)
end

    SDDP.train(model, iteration_limit = 1, print_level = 0)
[ Info: Writing cuts to the file `model.cuts.json`
Unable to retrieve solution from node 2.

  Termination status : INFEASIBLE
  Primal status      : NO_SOLUTION
  Dual status        : NO_SOLUTION.

The current subproblem was written to `subproblem_2.mof.json`.

There are two common causes of this error:
  1) you have a mistake in your formulation, or you violated
     the assumption of relatively complete recourse
  2) the solver encountered numerical issues

See https://odow.github.io/SDDP.jl/stable/tutorial/warnings/ for more information.
Warning

The actual constraints causing the infeasibilities can be deceptive! A good strategy to debug is to comment out all constraints. Then, one-by-one, un-comment the constraints and try resolving the model to check if it finds a feasible solution.

Numerical stability

If you aren't aware, SDDP builds an outer-approximation to a convex function using cutting planes. This results in a formulation that is particularly hard for solvers like HiGHS, Gurobi, and CPLEX to deal with. As a result, you may run into weird behavior. This behavior could include:

  • Iterations suddenly taking a long time (the solver stalled)
  • Subproblems turning infeasible or unbounded after many iterations
  • Solvers returning "Numerical Error" statuses

Problem scaling

In almost all cases, the cause of this is poor problem scaling. For our purpose, poor problem scaling means having variables with very large numbers and variables with very small numbers in the same model.

Tip

Gurobi has an excellent set of articles on numerical issues and how to avoid them.

Consider, for example, the hydro-thermal scheduling problem we have been discussing in previous tutorials.

If we define the volume of the reservoir in terms of m³, then a lake might have a capacity of 10^10 m³: @variable(subproblem, 0 <= volume <= 10^10). Moreover, the cost per cubic meter might be around $0.05/m³. To calculate the value of water in our reservoir, we need to multiple a variable on the order of 10^10, by one on the order of 10⁻²! That is twelve orders of magnitude!

To improve the performance of the SDDP algorithm (and reduce the chance of weird behavior), try to re-scale the units of the problem in order to reduce the largest difference in magnitude. For example, if we talk in terms of million m³, then we have a capacity of 10⁴ million m³, and a price of $50,000 per million m³. Now things are only one order of magnitude apart.

Numerical stability report

To aid in the diagnose of numerical issues, you can call SDDP.numerical_stability_report. By default, this aggregates all of the nodes into a single report. You can produce a stability report for each node by passing by_node=true.

using SDDP

model = SDDP.LinearPolicyGraph(stages = 2, lower_bound = -1e10) do subproblem, t
    @variable(subproblem, x >= -1e7, SDDP.State, initial_value = 1e-5)
    @constraint(subproblem, 1e9 * x.out >= 1e-6 * x.in + 1e-8)
    @stageobjective(subproblem, 1e9 * x.out)
end

SDDP.numerical_stability_report(model)
numerical stability report
  matrix range     [1e-06, 1e+09]
  objective range  [1e+00, 1e+09]
  bounds range     [1e+07, 1e+10]
  rhs range        [1e-08, 1e-08]
WARNING: numerical stability issues detected
  - matrix range contains small coefficients
  - matrix range contains large coefficients
  - objective range contains large coefficients
  - bounds range contains large coefficients
  - rhs range contains small coefficients
Very large or small absolute values of coefficients
can cause numerical stability issues. Consider
reformulating the model.

The report analyses the magnitude (in absolute terms) of the coefficients in the constraint matrix, the objective function, any variable bounds, and in the RHS of the constraints. A warning will be thrown in SDDP.jl detects very large or small values. As discussed in Problem scaling, this is an indication that you should reformulate your model.

By default, a numerical stability check is run when you call SDDP.train, although it can be turned off by passing run_numerical_stability_report = false.

Solver-specific options

If you have a particularly troublesome model, you should investigate setting solver-specific options to improve the numerical stability of each solver. For example, Gurobi has a NumericFocus option.

Choosing an initial bound

One of the important requirements when building a SDDP model is to choose an appropriate bound on the objective (lower if minimizing, upper if maximizing). However, it can be hard to choose a bound if you don't know the solution! (Which is very likely.)

The bound should not be as large as possible (since this will help with convergence and the numerical issues discussed above), but if chosen too small, it may cut off the feasible region and lead to a sub-optimal solution.

Consider the following simple model, where we first set lower_bound to 0.0.

using SDDP, HiGHS

model = SDDP.LinearPolicyGraph(
    stages = 3,
    sense = :Min,
    lower_bound = 0.0,
    optimizer = HiGHS.Optimizer,
) do subproblem, t
    @variable(subproblem, x >= 0, SDDP.State, initial_value = 2)
    @variable(subproblem, u >= 0)
    @variable(subproblem, v >= 0)
    @constraint(subproblem, x.out == x.in - u)
    @constraint(subproblem, u + v == 1.5)
    @stageobjective(subproblem, t * v)
end

SDDP.train(model, iteration_limit = 5, run_numerical_stability_report = false)
-------------------------------------------------------------------
         SDDP.jl (c) Oscar Dowson and contributors, 2017-23
-------------------------------------------------------------------
problem
  nodes           : 3
  state variables : 1
  scenarios       : 1.00000e+00
  existing cuts   : false
options
  solver          : serial mode
  risk measure    : SDDP.Expectation()
  sampling scheme : SDDP.InSampleMonteCarlo
subproblem structure
  VariableRef                             : [5, 5]
  AffExpr in MOI.EqualTo{Float64}         : [2, 2]
  VariableRef in MOI.GreaterThan{Float64} : [4, 4]
  VariableRef in MOI.LessThan{Float64}    : [1, 1]
-------------------------------------------------------------------
 iteration    simulation      bound        time (s)     solves  pid
-------------------------------------------------------------------
         1   6.500000e+00  3.000000e+00  3.306150e-03         6   1
         5   3.500000e+00  3.500000e+00  6.600142e-03        30   1
-------------------------------------------------------------------
status         : iteration_limit
total time (s) : 6.600142e-03
total solves   : 30
best bound     :  3.500000e+00
simulation ci  :  4.100000e+00 ± 1.176000e+00
numeric issues : 0
-------------------------------------------------------------------

Now consider the case when we set the lower_bound to 10.0:

using SDDP, HiGHS

model = SDDP.LinearPolicyGraph(
    stages = 3,
    sense = :Min,
    lower_bound = 10.0,
    optimizer = HiGHS.Optimizer,
) do subproblem, t
    @variable(subproblem, x >= 0, SDDP.State, initial_value = 2)
    @variable(subproblem, u >= 0)
    @variable(subproblem, v >= 0)
    @constraint(subproblem, x.out == x.in - u)
    @constraint(subproblem, u + v == 1.5)
    @stageobjective(subproblem, t * v)
end

SDDP.train(model, iteration_limit = 5, run_numerical_stability_report = false)
-------------------------------------------------------------------
         SDDP.jl (c) Oscar Dowson and contributors, 2017-23
-------------------------------------------------------------------
problem
  nodes           : 3
  state variables : 1
  scenarios       : 1.00000e+00
  existing cuts   : false
options
  solver          : serial mode
  risk measure    : SDDP.Expectation()
  sampling scheme : SDDP.InSampleMonteCarlo
subproblem structure
  VariableRef                             : [5, 5]
  AffExpr in MOI.EqualTo{Float64}         : [2, 2]
  VariableRef in MOI.GreaterThan{Float64} : [4, 4]
  VariableRef in MOI.LessThan{Float64}    : [1, 1]
-------------------------------------------------------------------
 iteration    simulation      bound        time (s)     solves  pid
-------------------------------------------------------------------
         1   6.500000e+00  1.100000e+01  3.015041e-03         6   1
         5   5.500000e+00  1.100000e+01  5.941153e-03        30   1
-------------------------------------------------------------------
status         : iteration_limit
total time (s) : 5.941153e-03
total solves   : 30
best bound     :  1.100000e+01
simulation ci  :  5.700000e+00 ± 3.920000e-01
numeric issues : 0
-------------------------------------------------------------------

How do we tell which is more appropriate? There are a few clues that you should look out for.

  • The bound converges to a value above (if minimizing) the simulated cost of the policy. In this case, the problem is deterministic, so it is easy to tell. But you can also check by performing a Monte Carlo simulation like we did in An introduction to SDDP.jl.

  • The bound converges to different values when we change the bound. This is another clear give-away. The bound provided by the user is only used in the initial iterations. It should not change the value of the converged policy. Thus, if you don't know an appropriate value for the bound, choose an initial value, and then increase (or decrease) the value of the bound to confirm that the value of the policy doesn't change.

  • The bound converges to a value close to the bound provided by the user. This varies between models, but notice that 11.0 is quite close to 10.0 compared with 3.5 and 0.0.