Here we provide guidance to various ways InfiniteOpt can be extended.


Extendibility is one of the core ideas of InfiniteOpt so that it can serve as a convenient tool for those developing and implementing advanced techniques for infinite dimensional optimization problems. Thus, InfiniteOpt is developed in a modular manner to readily accommodate user-defined functionality and/or to serve as useful base in writing a JuMP extension. Admittedly, this modularity is not perfect and comments/suggestions are welcomed to help us improve this.

Infinite Domains

Infinite domains are used to characterize the behavior of infinite parameters and used to govern the behavior of supports in InfiniteOpt. Here we walk through how user-defined domains can be added to various degrees of functionality. A template is provided in ./test/extensions/infinite_domain.jl. The extension steps employed are:

  1. Define the new struct infinite domain type (only thing required as bare minimum)
  2. Extend InfiniteOpt.supports_in_domain (enables error checking of supports)
  3. Extend InfiniteOpt.generate_support_values (enables support generation via num_supports keyword arguments)
  4. If a lower bound and upper bound can be reported, extend JuMP lower bound and upper bound methods (enables automatic bound detection in integral)
  5. Extend InfiniteOpt.MeasureToolbox.generate_expect_data (enables the use of expect)

As an example, let's create a univariate disjoint interval domain as an infinite domain type. This corresponds to the domain $[lb_1, ub_1] \cup [lb_2, ub_2]$ where $ub_1 \leq lb_2$. First, we need to create the DataType with inheritance from InfiniteScalarDomain:

using InfiniteOpt

struct DisjointDomain <: InfiniteOpt.InfiniteScalarDomain
    # constructor
    function DisjointDomain(lb1::Number, ub1::Number, lb2::Number, ub2::Number)
        if lb1 > ub1 || lb2 > ub2 || ub1 > lb2
            error("Invalid bounds")
        return new(convert(Float64, lb1), convert(Float64, ub1),
                   convert(Float64, lb2), convert(Float64, ub2))

Notice that we also define the constructor function to error check and convert as needed (this is recommended, but not required). For basic functionality this is all we have to do to add a domain in InfiniteOpt.

We can now define infinite parameters using this domain via @infinite_parameter both anonymously and explicitly:

julia> model = InfiniteModel();

julia> t = @infinite_parameter(model, domain = DisjointDomain(0, 1, 3, 4), base_name = "t")

julia> @infinite_parameter(model, t in DisjointDomain(0, 1, 3, 4))

Once defined (without further extension), these parameters can be used as normal with the following limitations:

  • Supports must be specified manually (num_supports is not enabled)
  • Supports will not be checked if they are in the domain of the infinite domain
  • Domain bounds cannot be queried.
  • The DiscreteMeasureData or FunctionalDiscreteMeasureData must be provided explicitly to evaluate measures

However, all of these limitations except for the last one can be eliminated by extending a few functions as outlined above. To address the last one, we need to extend generate_integral_data. See [Measure Evaluation Techniques] for details.

To enable support domain checking which is useful to avoid strange bugs, we will extend InfiniteOpt.supports_in_domain. This returns a Bool to indicate if a vector of supports are in the domain:

function InfiniteOpt.supports_in_domain(
    supports::Union{Number, Vector{<:Number}},
    return all((domain.lb1 .<= supports .<= domain.ub1) .| (domain.lb2 .<= supports .<= domain.ub2))

Now the checks are enabled, so the following would yield an error because the support is not in the domain:

julia> @infinite_parameter(model, domain = DisjointDomain(0, 1, 3, 4), supports = 2)
ERROR: At none:1: `@infinite_parameter(model, domain = DisjointDomain(0, 1, 3, 4), supports = 2)`: Supports violate the domain bounds.

To enable automatic support generation via the num_supports keyword and with functions such as fill_in_supports!, we will extend InfiniteOpt.generate_support_values:

struct DisjointGrid <: InfiniteOpt.PublicLabel end

function InfiniteOpt.generate_support_values(
    num_supports::Int = InfiniteOpt.DefaultNumSupports,
    sig_digits::Int = InfiniteOpt.DefaultSigDigits
    )::Tuple{Vector{Float64}, DataType}
    length_ratio = (domain.ub1 - domain.lb1) / (domain.ub1 - domain.lb1 + domain.ub2 - domain.lb2)
    num_supports1 = Int64(ceil(length_ratio * num_supports))
    num_supports2 = num_supports - num_supports1
    supports1 = collect(range(domain.lb1, stop = domain.ub1, length = num_supports1))
    supports2 = collect(range(domain.lb2, stop = domain.ub2, length = num_supports2))
    return round.([supports1; supports2], sigdigits = sig_digits), DisjointGrid

Now automatic support generation is enabled, for example:

julia> par = @infinite_parameter(model, domain = DisjointDomain(0, 2, 3, 4), num_supports = 10)

julia> supports(par)
10-element Vector{Float64}:

We can extend the appropriate JuMP upper and lower bound functions if desired which are:

However, if we want has_lower_bound = false and has_upper_bound = false then no extension is needed. For our current example we won't do this since lower and upper bounds aren't exactly clear for a disjoint interval. Please refer to the template in ./InfiniteOpt/test/extensions/infinite_domain.jl to see how this is done.

Finally, we can optionally enable the use of expect taken with respect to infinite parameters with this new domain type by extending InfiniteOpt.MeasureToolbox.generate_expect_data:

function InfiniteOpt.MeasureToolbox.generate_expect_data(domain::DisjointDomain, 
    for (k, _) in kwargs
        error("Keyword argument `$k` not supported for expectations over ",
              "disjoint domains.")
    coeff_func = (supps) -> ones(size(supps)[end]) ./ size(supps)[end] 
    return InfiniteOpt.FunctionalDiscreteMeasureData(pref, coeff_func, 0, All)

The above implementation simply sums over all the supports associated with pref and divides by the total number. Now we can use expect:

julia> @variable(model, y, Infinite(t))

julia> expect(y, t)

Derivative Evaluation Methods

Derivative evaluation methods are used to dictate how we form the auxiliary derivative evaluation equations (derivative constraints) when we evaluate derivatives in InfiniteOpt. Users may wish to implement their own methods beyond the finite difference and orthogonal collocation ones we natively provide. Thus, we provide an API to do just this. A complete template is provided in ./test/extensions/derivative_method.jl to help streamline this process. The extension steps are:

  1. Define the new method struct that inherits from the correct AbstractDerivativeMethod subtype
  2. Extend InfiniteOpt.generative_support_info if the method is a GenerativeDerivativeMethod
  3. Extend InfiniteOpt.evaluate_derivative.

To exemplify this process let's implement explicit Euler which is already implemented via FiniteDifference(Forward()), but let's make our own anyway for the sake of example. For a first order derivative $\frac{d y(t)}{dt}$ explicit Euler is expressed:

\[y(t_{n+1}) = y(t_n) + (t_{n+1} - t_{n})\frac{d y(t_n)}{dt}, \ \forall n = 0, 1, \dots, k-1\]

Let's get started with step 1 and define our new method struct:

using InfiniteOpt

struct ExplicitEuler <: NonGenerativeDerivativeMethod end

Notice that our method ExplicitEuler inherits from NonGenerativeDerivativeMethod since explicit Euler uses the existing support scheme without adding any additional supports. If our desired method needed to add additional supports (e.g., orthogonal collocation over finite elements) then we would need to have used GenerativeDerivativeMethod.

Since, this is a NonGenerativeDerivativeMethod we skip step 2. This is however exemplified in the extension template.

Now we just need to do step 3 which is to extend InfiniteOpt.evaluate_derivative. This function generates all the expressions necessary to build the derivative evaluation equations (derivative constraints). We assume these relations to be of the form $h = 0$ where $h$ is a vector of expressions and is what the output of InfiniteOpt.evaluate_derivative should be. Thus, mathematically $h$ should be of the form:

\[\begin{aligned} &&& y(t_{1}) - y(0) - (t_{1} - t_{0})\frac{d y(0)}{dt} \\ &&& \vdots \\ &&& y(t_{n+1}) - y(t_n) - (t_{n+1} - t_{n})\frac{d y(t_n)}{dt} \\ &&& \vdots \\ &&& y(t_{k}) - y(k-1) - (t_{k} - t_{k-1})\frac{d y(k-1)}{dt} \\ \end{aligned}\]

With this in mind let's now extend InfiniteOpt.evaluate_derivative:

function InfiniteOpt.evaluate_derivative(
    # get the basic derivative information 
    vref = derivative_argument(dref)
    pref = operator_parameter(dref)
    # generate the derivative expressions h_i corresponding to the equations of 
    # the form h_i = 0
    supps = supports(pref, label = All)
    exprs = Vector{JuMP.AbstractJuMPScalar}(undef, length(supps) - 1)
    for i in eachindex(exprs)
        d = InfiniteOpt.make_reduced_expr(dref, pref, supps[i], write_model)
        v1 = InfiniteOpt.make_reduced_expr(vref, pref, supps[i], write_model)
        v2 = InfiniteOpt.make_reduced_expr(vref, pref, supps[i + 1], write_model)
        change = supps[i + 1] - supps[i]
        exprs[i] = JuMP.@expression(write_model, v2 - v1 - change * d)
    return exprs

We used InfiniteOpt.make_reduced_expr as a convenient helper function to generate the semi-infinite variables/expressions we need to generate at each support point. Also note that InfiniteOpt.add_generative_supports needs to be included for GenerativeDerivativeMethods, but is not necessary in this example.

Now that we have completed all the necessary steps, let's try it out!

julia> model = InfiniteModel();

julia> @infinite_parameter(model, t in [0, 10], num_supports = 3, 
                           derivative_method = ExplicitEuler());

julia> @variable(model, y, Infinite(t));

julia> dy = deriv(y, t);

julia> evaluate(dy)

julia> derivative_constraints(dy)
2-element Vector{InfOptConstraintRef}:
 y(5) - y(0) - 5 ∂/∂t[y(t)](0) = 0.0
 y(10) - y(5) - 5 ∂/∂t[y(t)](5) = 0.0

We implemented explicit Euler and it works! Now go and extend away!

Measure Evaluation Techniques

Measure evaluation methods are used to dictate how to evaluate measures. Users may wish to apply evaluation methods other than Monte Carlo sampling and/or Gaussian quadrature methods. To create multiple measures using the same new evaluation methods, users may want to embed the new evaluation method under the integral function that does not require explicit construction of AbstractMeasureData.

Creating a DiscreteMeasureData Object

The basic way to do that is to write a function that creates DiscreteMeasureData object and pass the object to measure. This considers a measure approximation of the form:

\[\sum_{i \in I} \alpha_i f(\tau_i) w(\tau_i)\]

where $\alpha_i$ are coefficients, $f(\cdot)$ is the expression being measured, $w(\cdot)$ is a weighting function, and $i \in I$ indexes the support points. Let's consider defining a function that enables the definition of a uniform grid for a univariate infinite parameter in IntervalDomain. This example approximation uses a uniformly spaced supports $\tau_i$ with $\alpha_i = \frac{ub - lb}{|I|}$:

function uniform_grid(param, num_supports)
    lb = lower_bound(param)
    ub = upper_bound(param)
    supps = collect(LinRange(lb, ub, num_supports))
    coeffs = ones(num_supports) / num_supports * (ub - lb)
    return DiscreteMeasureData(param, coeffs, supps, lower_bound = lb, upper_bound = ub)

It is necessary to pass the infinite parameter reference since the construction of measure data object needs parameter information. Now let's apply the new uniform_grid function to infinite parameters in intervals. We consider a time parameter t and 2D spatial parameter x, and two variables f(t) and g(x) parameterized by t and x, respectively:

julia> m = InfiniteModel();

julia> @infinite_parameter(m, t in [0, 5]);

julia> @variable(m, y, Infinite(t));

Now we can use uniform_grid to construct a DiscreteMeasureData and create a measure using the measure data, as shown below:

julia> tdata = uniform_grid(t, 6);

julia> y_meas = measure(y, tdata)
measure{t ∈ [0, 5]}[y(t)]

julia> expand(y_meas)
0.8333333333333333 y(0) + 0.8333333333333333 y(1) + 0.8333333333333333 y(2) + 0.8333333333333333 y(3) + 0.8333333333333333 y(4) + 0.8333333333333333 y(5)

Integral Evaluation Methods

For integrals, we can implement a new approximation method via the extension of InfiniteOpt.MeasureToolbox.generate_integral_data. This will allow users to use their custom measure evaluation methods in the integral function that does not explicitly require a measure data object. A template for how such an extension is accomplished is provided in ./test/extensions/measure_eval.jl. In general, such an extension can be created as follows:

  1. Define a new empty struct (e.g. my_new_fn) that dispatches your function
  2. Extend InfiniteOpt.MeasureToolbox.generate_integral_data, where method is of the type my_new_fn, and domain needs to be a subtype of AbstractInfiniteDomain that you wish to apply the new evaluation method to.

Note that this procedure can be used to generate new measure evaluation methods not only for existing infinite domains, but also for user-defined infinite domains.

For example, an extension of InfiniteOpt.MeasureToolbox.generate_integral_data that implements uniform grid for univariate and multivariate parameters in IntervalDomain can be created as follows:

struct UnifGrid <: InfiniteOpt.MeasureToolbox.AbstractUnivariateMethod end

function InfiniteOpt.MeasureToolbox.generate_integral_data(
    num_supports::Int = InfiniteOpt.DefaultNumSupports,
    weight_func::Function = InfiniteOpt.default_weight
    increment = (upper_bound - lower_bound) / (num_supports - 1)
    supports = [lower_bound + (i - 1) * increment for i in 1:num_supports]
    coeffs = ones(num_supports) / num_supports * (upper_bound - lower_bound)
    return InfiniteOpt.DiscreteMeasureData(
        pref, coeffs, supports,
        weight_function = weight_func,
        lower_bound = lower_bound, 
        upper_bound = upper_bound)

Also notice that users are free to pass keyword arguments for their new functions in addition to the required positional arguments. This might be needed in case if the new evaluation method requires additional information not captured in the default positional arguments. For example, the multivariate parameter version above needs to know if the multivariate parameter is independent in order to throw a warning when needed.

We create measure for y using the uniform_grid method:

julia> y_int = integral(y, t, num_supports = 6, eval_method = UnifGrid())
∫{t ∈ [0, 5]}[y(t)]

julia> expand(y_int)
0.8333333333333333 y(0) + 0.8333333333333333 y(1) + 0.8333333333333333 y(2) + 0.8333333333333333 y(3) + 0.8333333333333333 y(4) + 0.8333333333333333 y(5)

Here we go! We can freely use UnifGrid for infinite parameters residing in IntervalDomains now.

Measure Data

Measures are used to evaluate over infinite domains. Users may wish to employ measure abstractions that cannot be readily represented with coefficients and discretized supports, and thus may wish to extend InfiniteOpt's measure framework to accommodate other paradigms. This can be accomplished by implementing a user-defined measure data structure that inherits from AbstractMeasureData. A template for how such an extension is accomplished is provided in ./test/extensions/measure_data.jl. The extension steps employed are:

  1. Define the new data struct inheriting from AbstractMeasureData (required)
  2. Extend InfiniteOpt.parameter_refs (required)
  3. Extend InfiniteOpt.expand_measure (required)
  4. Extend InfiniteOpt.supports (required if parameter supports are employed in any way)
  5. Extend InfiniteOpt.add_supports_to_parameters (required if parameter supports are employed in measure evaluation)
  6. Extend InfiniteOpt.coefficients (useful getter method if applicable)
  7. Extend InfiniteOpt.weight_function (useful getter method if applicable)
  8. Extend InfiniteOpt.support_label (needed to enable deletion if supports are added.)
  9. Extend InfiniteOpt.generative_support_info (Needed if the measure will cause the creation of generative supports)
  10. Make simple measure constructor wrapper of measure to ease definition.

To illustrate how this process can be done, let's consider extending InfiniteOpt to include measure support for assessing the variance of random expressions. The variance of an expression $f(x, \xi)$ where $x \in \mathbb{R}^n$ are finite variables and $\xi \in \mathbb{R}^m$ are random infinite parameters is defined:

\[\mathbb{V}[f(x, \xi)] = \mathbb{E}\left[(f(x, \xi) - \mathbb{E}[f(x, \xi)])^2 \right].\]

Note, we could just accomplish this by nested use of expect, but we implement this example to illustrate the mechanics of extension.

First, let's define our new struct inheriting from AbstractMeasureData:

using InfiniteOpt, Distributions

struct DiscreteVarianceData <: AbstractMeasureData
    parameter_refs::Union{GeneralVariableRef, Vector{GeneralVariableRef}}
    # constructor
    function DiscreteVarianceData(
        parameter_refs::Union{GeneralVariableRef, AbstractArray{<:GeneralVariableRef}},
        label::DataType = InfiniteOpt.generate_unique_label()
        # convert input as necessary to proper array format
        if parameter_refs isa AbstractArray
            parameter_refs = convert(Vector, parameter_refs)
            supports = [convert(Vector, arr) for arr in supports]
        return new(parameter_refs, supports, label)

We have defined our data type, so let's extend the measure data query methods to enable its definition. These include:

function InfiniteOpt.parameter_refs(data::DiscreteVarianceData)
    return data.parameter_refs

function InfiniteOpt.supports(data::DiscreteVarianceData)::Vector
    return data.supports

function InfiniteOpt.support_label(data::DiscreteVarianceData)::DataType
    return data.label

We also need to extend InfiniteOpt.add_supports_to_parameters since support points will be used for measure evaluation later:

function InfiniteOpt.add_supports_to_parameters(data::DiscreteVarianceData)::Nothing
    pref = parameter_refs(data)
    supps = supports(data)
    label = support_label(data)
    add_supports(pref, supps, label = label)

Note that extending supports is not needed for abstractions that don't involve discretization of the infinite parameter(s), such as the case for certain outer approximation techniques. Our extension is now sufficiently constructed to allow us to define out the new variance measure via measure. For example:

# Setup the infinite model
model = InfiniteModel()
@infinite_parameter(model, xi ~ Normal(), num_supports = 2) # few for simplicity
@variable(model, y, Infinite(xi))
@variable(model, z)

# Define out new variance measure
data = DiscreteVarianceData(xi, supports(xi))
mref = measure(2y + z, data, name = "Var")

# output
Var{xi}[2 y(xi) + z]

Thus, we can define measure references that employ this our new data type.

We can define variance measures now, but now let's extend expand_measure so that they can be expanded into finite expressions:

function InfiniteOpt.expand_measure(
    # define the expectation data
    expect_data = DiscreteMeasureData(
                      1 / length(data.supports) * ones(length(data.supports)),
                      data.supports, is_expect = true, label = data.label)
    # define the mean
    mean = measure(expr, expect_data)
    # return the expansion of the variance using the data mean
    return expand_measure((copy(expr) - mean)^2, expect_data, write_model)

Notice that we reformulated our abstraction in terms of measures with DiscreteMeasureData so that we could leverage the existing expand_measure library. Now, new the measure type can be expanded and moreover infinite models using this new type can be optimized. Let's try expanding the measure we already defined:

julia> expand(mref)
y(-0.556026876146)² + 0 z*y(-0.556026876146) - 2 y(-0.44438335711)*y(-0.556026876146) + 0 z² + 0 z*y(-0.44438335711) + y(-0.44438335711)²

Finally, as per recommendation let's make a wrapper method to make defining variance measures more convenient:

function variance(
    expr::Union{JuMP.GenericAffExpr, GeneralVariableRef},
    params::Union{GeneralVariableRef, AbstractArray{GeneralVariableRef}};
    name::String = "Var", 
    num_supports::Int = 10,
    use_existing::Bool = false
    # get the supports
    if use_existing
        supps = supports.(params)
        supps = generate_support_values(infinite_domain(first(params)),
                                        num_supports = num_supports)
    # make the data
    data = DiscreteVarianceData(params, supps)
    # built the measure
    return measure(expr, data, name = name)

Notice in this case that we only permit linear expressions for expr since it will be squared by our new measure and we currently only support quadratic expressions. (This could be overcome by defining a place holder variable for expr.

Now let's use our constructor to repeat the above measure example:

julia> expand(variance(2y + z, xi, use_existing = true))
y(-0.556026876146)² + 0 z*y(-0.556026876146) - 2 y(-0.44438335711)*y(-0.556026876146) + 0 z² + 0 z*y(-0.44438335711) + y(-0.44438335711)²

We have done it! Now go and extend away!

Generative Support Information

As discussed in the Generative Supports section, generative supports help enable measure and/or derivative evaluation techniques that require the creation of generative supports (e.g., orthogonal collocation). Natively, we provide UniformGenerativeInfo to help accomplish this which works for creating generative supports uniformly over finite elements as is the case for orthogonal collocation (note this includes scaling them as need to the size of each finite element). However, more complex generative support schemes can be enabled by defining a new concrete AbstractGenerativeInfo subtype. This section will detail how this can be accomplished in InfiniteOpt. A template for implementing this is provided in ./test/extensions/generative_info.jl.

A new generative support information type can be created via the following:

  1. Define a concrete subtype of AbstractGenerativeInfo (required)
  2. Make a unique support label that inherits InternalLabel (recommended)
  3. Extend InfiniteOpt.support_label (required)
  4. Extend InfiniteOpt.make_generative_supports (required).

For the sake of example, let's suppose we want to make a method that generates a certain amount of random supports for each finite element. First, let's define our struct RandomGenerativeInfo:

using InfiniteOpt, Random

struct RandomGenerativeInfo <: InfiniteOpt.AbstractGenerativeInfo
    amount::Int # amount of random supports per finite element

With that done, let's define a unique support label RandomInternal for these types of supports and extend support_label:

struct RandomInternal <: InternalLabel end

function InfiniteOpt.support_label(info::RandomGenerativeInfo)::Type{RandomInternal}
    return RandomInternal

Finally, let's extend make_generative_supports to create a vector of the generative supports based on a RandomGenerativeInfo and the existing model supports which are passed in the function as input:

function InfiniteOpt.make_generative_supports(info::RandomGenerativeInfo, pref, supps)::Vector{Float64}
    num_existing = length(supps)
    num_existing <= 1 && error("`$pref` doesn't have enough supports.")
    num_internal = info.attr
    gen_supps = Float64[]
    for i = 1:num_existing-1 
        lb = supps[i]
        ub = supps[i+1]
        append!(gen_supps, rand(num_internal) * (ub - lb) .+ lb)
    return gen_supps

Our extension is done and now RandomGenerativeInfo can be incorporated by a GenerativeDerivativeMethod we create or an AbstractMeasureData object of our choice like FunctionalDiscreteMeasureData.

Optimizer Models

InfiniteOpt provides a convenient interface and abstraction for modeling infinite-dimensional optimization problems. By default, InfiniteModels are reformulated into a solvable JuMP.Model (referred to as an optimizer model) via TranscriptionOpt which discretizes the model in accordance with the infinite parameter supports. However, users may wish to employ some other reformulation method to produce the optimizer model. This section will explain how this can be done in InfiniteOpt. A template for implementing this extension is provided in ./test/extensions/optimizer_model.jl. Our default sub-module InfiniteOpt.TranscriptionOpt also serves as a good example.


We are currently working on a fundamental overhaul of the optimizer model interface. The new interface will be much more modular, will permit non-JuMP backends, and should generally make extending more intuitive. Track the progress here.

A new reformulation method and its corresponding optimizer model can be extended using the following steps:

  1. Define a mutable struct for variable/constraint mappings and other needed info (required)
  2. Define a JuMP.Model constructor that uses (1.) in Model.ext[:my_ext_key] (recommended)
  3. Extend build_optimizer_model! to in accordance with the new optimizer model (required)
  4. Extend optimizer_model_variable if possible (enables result queries)
  5. Extend optimizer_model_expression if possible (enables result queries)
  6. Extend optimizer_model_constraint if possible (enables result queries)
  7. Extend InfiniteOpt.variable_supports if appropriate
  8. Extend InfiniteOpt.expression_supports if appropriate
  9. Extend InfiniteOpt.constraint_supports if appropriate
  10. If steps 4-6 are skipped then extend the following:
  11. Extend InfiniteOpt.add_point_variable and InfiniteOpt.add_semi_infinite_variable to use expand_measure without modifying the infinite model.

For the sake of example, let's suppose we want to define a reformulation method for InfiniteModels that are 2-stage stochastic programs (i.e., only DistributionDomains are used, infinite variables are random 2nd stage variables, and finite variables are 1st stage variables). In particular, let's make a simple method that replaces the infinite parameters with their mean values, giving us the deterministic mean-valued problem.

First, let's define the mutable struct that will be used to store our variable and constraint mappings. This case it is quite simple since our deterministic model will have a 1-to-1 mapping:

using InfiniteOpt, Distributions

mutable struct DeterministicData
    # variable and constraint mapping
    infvar_to_detvar::Dict{GeneralVariableRef, VariableRef}
    infconstr_to_detconstr::Dict{InfOptConstraintRef, ConstraintRef}
    # constructor
    function DeterministicData()
        return new(Dict{GeneralVariableRef, VariableRef}(),
                   Dict{InfOptConstraintRef, ConstraintRef}())

Now let's define a constructor for optimizer models that will use DeterministicData and let's define a method to access that data:

const DetermKey = :DetermData

function DeterministicModel(args...; kwargs...)::Model
    # initialize the JuMP Model
    model = Model(args...; kwargs...)
    model.ext[DetermKey] = DeterministicData()
    return model

function deterministic_data(model::Model)::DeterministicData
    haskey(model.ext, DetermKey) || error("Model is not a DeterministicModel.")
    return model.ext[DetermKey]

The use of an extension key such as DetermKey is required since it used to dispatch reformulation and querying methods making optimizer model extensions possible.

With the constructor we can now specify that a given InfiniteModel uses a DeterministicModel instead of a TranscriptionModel using the OptimizerModel keyword argument or via set_optimizer_model:

using Ipopt

# Make model using Ipopt and DeterministicModels
model = InfiniteModel(optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0),
                      OptimizerModel = DeterministicModel)

# Or equivalently
model = InfiniteModel()
set_optimizer_model(model, DeterministicModel())
set_optimizer(model, optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0))

Now model uses a DeterministicModel as its optimizer model! With that we can build our InfiniteModel as normal, for example:

@infinite_parameter(model, ξ ~ Uniform())
@variable(model, y[1:2] >= 0, Infinite(ξ))
@variable(model, z)
@objective(model, Min, z + expect(y[1] + y[2], ξ))
@constraint(model, 2y[1] - z <= 42)
@constraint(model, y[2]^2 + ξ == 2)
@constraint(model, sin(z) >= -1)

# output
Min z + 𝔼{ξ}[y[1](ξ) + y[2](ξ)]
Subject to
 y[1](ξ) ≥ 0.0, ∀ ξ ~ Uniform
 y[2](ξ) ≥ 0.0, ∀ ξ ~ Uniform
 2 y[1](ξ) - z ≤ 42.0, ∀ ξ ~ Uniform
 y[2](ξ)² + ξ = 2.0, ∀ ξ ~ Uniform
 sin(z) - -1 ≥ 0.0

We have defined our InfiniteModel, but now we need to specify how to reformulate it into a DeterministicModel. This is accomplished by extending build_optimizer_model!. This will enable the use of optimize!. First, let's define an internal function _make_expression that will use dispatch to convert and InfiniteOpt expression into a JuMP expression using the mappings stored in opt_model in its DeterministicData:

## Make dispatch methods for converting InfiniteOpt expressions
# GeneralVariableRef
function _make_expression(opt_model::Model, expr::GeneralVariableRef)
    return _make_expression(opt_model, expr, index(expr))
# IndependentParameterRef
function _make_expression(
    return mean(infinite_domain(expr).distribution) # assuming univariate
# FiniteParameterRef
function _make_expression(
    return parameter_value(expr)
# DependentParameterRef
function _make_expression(
    return mean(infinite_domain(expr).distribution) # assuming valid dist.
# DecisionVariableRef
function _make_expression(
    ::Union{InfiniteVariableIndex, FiniteVariableIndex}
    return deterministic_data(opt_model).infvar_to_detvar[expr]
# MeasureRef --> assume is expectation
function _make_expression(
    return _make_expression(opt_model, measure_function(expr))
# AffExpr/QuadExpr
function _make_expression(opt_model::Model, expr::Union{GenericAffExpr, GenericQuadExpr})
    return map_expression(v -> _make_expression(opt_model, v), expr)
# NLPExpr
function _make_expression(opt_model::Model, expr::NLPExpr)
    return add_nonlinear_expression(opt_model, map_nlp_to_ast(v -> _make_expression(opt_model, v), expr))

For simplicity in example, above we assume that only DistributionDomains are used, there are not any PointVariableRefs, and all MeasureRefs correspond to expectations. Naturally, a full extension should include checks to enforce that such assumptions hold. Notice that map_expression and map_nlp_to_ast are useful for converting expressions.

Now let's extend build_optimizer_model! for DeterministicModels. Such extensions should build an optimizer model in place and in general should employ the following:

In place builds without the use of clear_optimizer_model_build! are also possible, but will require some sort of active mapping scheme to update in accordance with the InfiniteModel in the case that the optimizer model is built more than once. Thus, for simplicity we extend build_optimizer_model! below using an initial clearing scheme:

function InfiniteOpt.build_optimizer_model!(
    # TODO check that `model` is a stochastic model
    # clear the model for a build/rebuild
    determ_model = InfiniteOpt.clear_optimizer_model_build!(model)

    # add the registered functions if there are any
    add_registered_to_jump(determ_model, model)

    # add variables
    for vref in all_variables(model)
        if index(vref) isa InfiniteVariableIndex
            start = NaN # easy hack
            start = start_value(vref)
            start = isnothing(start) ? NaN : start
        lb = has_lower_bound(vref) ? lower_bound(vref) : NaN
        ub = has_upper_bound(vref) ? upper_bound(vref) : NaN
        if is_fixed(vref)
            lb = fix_value(vref)
        info = VariableInfo(!isnan(lb), lb, !isnan(ub), ub, is_fixed(vref), lb, 
                            !isnan(start), start, is_binary(vref), is_integer(vref))
        new_vref = add_variable(determ_model, ScalarVariable(info), name(vref))
        deterministic_data(determ_model).infvar_to_detvar[vref] = new_vref

    # add the objective
    obj_func = _make_expression(determ_model, objective_function(model))
    if obj_func isa NonlinearExpression
        set_nonlinear_objective(determ_model, objective_sense(model), obj_func)
        set_objective(determ_model, objective_sense(model), obj_func)

    # add the constraints
    for cref in all_constraints(model, Union{GenericAffExpr, GenericQuadExpr, NLPExpr})
        constr = constraint_object(cref)
        new_func = _make_expression(determ_model, constr.func)
        if new_func isa NonlinearExpression
            if constr.set isa MOI.LessThan
                ex = :($new_func <= $(constr.set.upper))
            elseif constr.set isa MOI.GreaterThan
                ex = :($new_func >= $(constr.set.lower))
            else # assume it is MOI.EqualTo
                ex = :($new_func == $(constr.set.value))
            new_cref = add_nonlinear_constraint(determ_model, ex)
            new_constr = build_constraint(error, new_func, constr.set)
            new_cref = add_constraint(determ_model, new_constr, name(cref))
        deterministic_data(determ_model).infconstr_to_detconstr[cref] = new_cref

    # update the status
    set_optimizer_model_ready(model, true)

Now we can build our optimizer model to obtain a DeterministicModel which can be leveraged to call optimize!


# output
Min z + y[1] + y[2]
Subject to
 2 y[1] - z ≤ 42.0
 y[2]² = 1.5
 y[1] ≥ 0.0
 y[2] ≥ 0.0
 subexpression[1] - 0.0 ≥ 0
With NL expressions
 subexpression[1]: sin(z) - -1.0

Note that better variable naming could be used with the reformulated infinite variables. Moreover, in general extensions of build_optimizer_model! should account for the possibility that InfiniteModel contains constraints wiht DomainRestrictions as accessed via domain_restrictions.

Now that we have optimized out InfiniteModel via the use the of a DeterministicModel, we probably will want to access the results. All queries are enabled when we extend optimizer_model_variable, optimizer_model_expression, and optimizer_model_constraint to return the variable(s)/expression(s)/constraint(s) in the optimizer model corresponding to their InfiniteModel counterparts. These will use the mutable struct of mapping data and should error if no mapping can be found, Let's continue our example using DeterministicModels:

function InfiniteOpt.optimizer_model_variable(
    model = optimizer_model(JuMP.owner_model(vref))
    map_dict = deterministic_data(model).infvar_to_detvar
    haskey(map_dict, vref) || error("Variable $vref not used in the optimizer model.")
    return map_dict[vref]

function InfiniteOpt.optimizer_model_expression(
    model = optimizer_model(InfiniteOpt._model_from_expr(expr))
    return _make_expression(model, expr)

function InfiniteOpt.optimizer_model_constraint(
    model = optimizer_model(JuMP.owner_model(cref))
    map_dict = deterministic_data(model).infconstr_to_detconstr
    haskey(map_dict, cref) || error("Constraint $cref not used in the optimizer model.")
    return map_dict[cref]

With these extensions we can now access all the result queries. For example:

julia> termination_status(model)
LOCALLY_SOLVED::TerminationStatusCode = 4

julia> result_count(model)

julia> value.(y)
2-element Vector{Float64}:

julia> optimizer_index(z)

If optimizer_model_variable, optimizer_model_expression, and/or optimizer_model_constraint cannot be extended due to the nature of the reformulation then please refer to step 10 of the extension steps listed at the beginning of this section.

Furthermore, if appropriate for the given reformulation the following should be extended:

That's it!

Wrapper Packages

InfiniteOpt provides a convenient modular interface for defining infinite dimensional optimization problems, implementing many tedious JuMP extensions such as facilitating mixed variable expressions. Thus, InfiniteOpt can serve as a base package for specific types of infinite dimensional problems and/or solution techniques. These extension packages can implement any of the extensions shown above and likely will want to introduce wrapper functions and macros to use package specific terminology (e.g., using random variables instead of infinite variables).

Please reach out to us via the discussion forum to discuss your plans before starting this on your own.