DecisionProgramming.JuMP module

Interface for Jump functionality necessary for optimizing models generated from diagrams.

class DecisionProgramming.JuMP.Array(model, dims, binary=False)[source]

An array of JuMP variables. Makes it easier to define contraints using the @constraint syntax.

Parameters

model: dp.Model: A JuMP Model object
dims: List of Integers: A list corresponding to the size of the array in each of its dimensions.
binary: Boolean (optional, default False): Wether the variables are boolean.

class DecisionProgramming.JuMP.Expression(model, *args)[source]

Builds a JuMP expression from a string or set of strings.

model: dp.Model

A JuMP Model object

loop: String (optional)

Set of loop variables in the JuMP constraint format (see the contingent portfolio analysis page in examples)

constraint: String

The contraints in the JuMP format (see the contingent portfolio: analysis page in examples)

class DecisionProgramming.JuMP.Model[source]

Wraps a JuMP optimizer model and decision model variables.

constraint(*args)[source]

Set a model constraints

loop: String (optional)

Set of loop variables in the JuMP constraint format (see the contingent portfolio analysis page in examples)

constraint: String

The contraints in the JuMP format (see the contingent portfolio: analysis page in examples)

objective(objective, operator='Max')[source]

Set the objective for the optimizer

Parameters

op: “Min” or “Max”: Whether to minimize or maximize the objective
expected_value: ExpectedValue: An ExpectedValue object. Describes the objective function.

setup_Gurobi_optimizer(*constraints)[source]

Set Gurobi as the optimizer for this model.

Parameters