Optimization for the Mimi.jl modeling framework
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October 2015

OptiMimi: Optimization of Mimi models

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OptiMimi provides a simplified interface for finding optimal parameter values for Mimi models (https://github.com/anthofflab/Mimi.jl). The core interface consists of problem to define the optimization problem, and calls to solution to solve it.

The package provides two major approaches for performing optimization: General and Linear programming. The General approach allows takes full models and applies non-linear optimization techniques to optimize parameters within them. The Linear programming approach allows models to define linear operation matrices which represent the computations they perform in Mimi's run_timestep function. In addition, OptiMimi offers a way to automatically generate these matrices.

OptiMimi supports autodifferentiation using ForwardDiff. To use it, the Model object must be created with the optional argument Number, to specify a general-enough type to handle dual-numbers: that is, m = Model(Number) If this is used, when the optimization problem is created, OptiMimi use gradient algorithms.

The General approach in OptiMimi can use algorithms in both NLopt and BlackBoxOptim. The Linear programming approach uses any solver supported by MathProgBase.

Constructing an optimization problem (General approach)

Setup an optimization problem with the problem function:

problem(model, names, lowers, uppers, objective; constraints, algorithm)
  • model is a Mimi model, with some parameters intended for optimization.
  • components and names are lists of the parameter names to be optimized, and must be the same length.
  • lowers and uppers are a list of lower and upper bounds and must be the same length as names; the same bounds are used for all values within a parameter array.
  • objective is a function that takes a Mimi Model, with parameters set and fully executed, and returns a value to be maximized.
  • constraints (optional) is a vector of inequality constraint functions; each takes a Mimi Model, with parameters set (but not necessarily executed), and should return < 0 when the constraint is satisfied.
  • algorithm (optional) is a symbol, currently chosen from the NLopt algorithms.

The return value is an object of the OptimizationProblem type, to be passed to solution.


Start by creating a Mimi model and ensuring that it runs with all parameters set. In the example below, my_model is a model with an agriculture component, in which N regions are evaluated in a single timestep to consume energy and produce corn.

The optimization maximizes economic value, trading off the value of the corn against the cost of the energy for fertilizer. We also add a constraint that the total fertilizer cannot be more than 1 million kg, to reduce environmental impacts.

using OptiMimi

# Prices of goods
p_F = 0.25  # the global price of food (per kg of corn)
p_E = 0.4   # the global price of fuel (per kWh)

# Objective to maximize economic output
function objective(model::Model)
    sum(my_model[:agriculture, :cornproduction] * p_F - my_model[:agriculture, :cornenergyuse] * p_E)

constraints = [model -> sum(model.components[:agriculture].Parameters.fertilizer) - 1e6]

# Setup the optimization
optprob = problem(my_model, [:agriculture], [:fertilizer], [0.], [1e6], objective, constraints=constraints)

Note that (1) the objective function is provided with the prepared model, not with the raw initialization values, and (2) even though there are N values to be set and optimized over in the fertilizer parameter, the lower and upper bounds are only specified once.

Solving the optimization problem

The optimization problem, returned by problem is solved by solution:

solution(optprob, generator; maxiter, verbose)
  • optprob is the result of the problem function.
  • generator is a function of no arguments, which returns a full set of parameter values, with values concatenated across parameters in the order of names above. This should generally be stochastic, and if the specified model fails the constraints then generator will be called again until it succeeds.
  • maxiter (optional) is the maximum number of iterations for the optimization; currently it only is used for the maximum number of times that generator will be called.
  • verbose (optional) is a boolean to specify if status messages should be printed.

The return value is a tuple of the maximum found objective value, and the concatenated collection of model parameters that produced it.


Continuing the example above, we solve the optimization problem:

(maxf, maxx) = solution(optprob, () -> [0. for i in 1:5])


Our generator function can only generate a single initial condition: all 0's.

Once you have a solution, you can initialize a model with it usnig the setparameters function:

setparameters(my_model, [:agriculture], [:fertilizer], maxx)

This is most useful when there are multiple parameters being optimized, or the parameters have multiple dimensions.

Constructing an optimization problem (Linear programming approach)

Linear programming allows for vastly faster optimizations, so long as the constraints and objective can be translated into linear algebra relationships. See https://en.wikipedia.org/wiki/Linear_programming for details.

Within OptiMimi, the large matrix and vectors which define the linear programming constraints and objective are developed in segments. In line with the model organization structure of Mimi, the computations which relate variables to parameters are kept separate, and organized with each component. These computations can be used directly, if variables of a component are constrained and the parameters of that same component are optimized over; or, they can be connected across multiple components. Rather than defining the entire matrix at once, OptiMimi allows segments of the matrix specific to each component to be defined separately.

Segments of the constraint matrix are encapsulated in LinearProgrammingRoom objects, which combine a sparse matrix with information about a single model parameter and variable. A column vector, combined with information about a single model parameter or variable, is encapsulated in a LinearProgrammingHall or LinearProgrammingShaft (its transpose) object. A LinearProgrammingHouse contains the set of all matrices for the optimization.

There are a variety of functions available which create these objects or manipulate them. Some of the most commonly used operations are below:

  • roomdiagonal: creates a room for a variable which is a direct scaling of a parameter.
  • *: Allows two rooms to be multiplied together, which corresponds to "connecting" the variable of the first as the parameter of the second; or allows a room and a hall or shaft to be multiplied so which results in a weighted sum of the variables in the room (e.g., as an objective).
  • room_relabel: Relabel the variable of a room so that it corresponds to the parameter name of another room which it is to connected to (multiplied with).

The usual process for setting up a linear programming problem is as follows:

  1. Mimi components are written as normal, with @defcomp calls, and the Mimi model is constructed and external parameters are initialized.

  2. Individual functions are specified for each component describing the gradient of a variable with respect to a parameter, using the room functions or the automated option in makeroom.jl. The naming typically is as follows: grad_COMPONENT_VARIABLE_PARAMETER(model) constraintoffset_COMPONENT_VARIABLE(model)

  3. A LinearProgrammingHosue is constructed, specifying the optimization parameters and constraint variables, like so:

    parameters, constcomponents, variables)```
  4. The objective is specified with a setobjective! call, often as a sum over variable specified by a gradient function, e.g.,

  5. Constraints are specified with setconstraint! and setconstraintoffset! calls. In all cases, the relationship must be specified so that variable < offset. This looks like,

  6. The optimization is performed, using any solver supported by MathProgBase and the houseoptimize function. For example:

using MathProgBase
using Gurobi
solver = GurobiSolver()

sol = houseoptimize(house, solver)
  1. The result is studied using the summarizeparameters, or, if the optimzation failed, findinfeasiblepair.