Quick Start Guide

This quick start guide will introduce the syntax of JuMPChance, again assuming familiarity with JuMP.

Creating a model

JuMPChance models should be created by using the following constructor:

m = ChanceModel()

All variables and constraints are associated with this model object. As in JuMP, solvers can be specified by using the solver= argument to the constructor. For example:

using CPLEX
m = ChanceModel(solver=CplexSolver())

will set the solver to CPLEX, assuming that both CPLEX and the corresponding Julia package are properly installed.

By default, JuMPChance will use ECOS, a lightweight open-source solver which supports the conic constraints needed for the reformulation method for solving chance-constrained problems.

Defining variables

In JuMPChance, you can mix decision variables and random variables in expressions. Decision variables are declared by using JuMP’s @variable syntax. Random variables are declared by using a similar syntax:

@indepnormal(m, x, mean=0, var=1)

creates a single independent normal random variable with the specified mean and variance. The mean and var arguments are always required. Variables indexed over a given set are supported, and the means and variances may depend on the given indices. For example:

@indepnormal(m, x[i=1:N,j=1:M], mean=i*j, var = 2j^2)

creates an N by M matrix of independent normally distributed random variables where the variable in index (i,j) has mean i*j and variance 2j^2.

Index sets do not need to be ranges; they may be arbitrary Julia lists:

S = [:cat, :dog]
@indepnormal(m, x[S], mean=0, var=1)

defines two variables x[:cat] and x[:dog].

Chance constraints

A JuMPChance model may contain a combination of standard JuMP constraints (linear and quadratic) and chance constraints.

Chance constraints are constraints which contain a mix of decision variables and random variables. Products with decision variables and random variables are allowed, but products between two decision variables or two random variables are not currently supported. This restriction ensures that the resulting chance constraint is a convex constraint on the decision variables.

Mathematically, the types of constraints supported are

\[P\left(\sum_{i=1}^k \left(c_i^Tx +d_i\right)z_i \geq b\right) \geq 1- \epsilon\]


\[P\left(\sum_{i=1}^k \left(c_i^Tx +d_i\right)z_i \leq b\right) \geq 1-\epsilon\]

where \(x\) are the decision variables, \(c_i\) are coefficient vectors, \(d_i\) and \(b\) are scalars, \(z_i\) are independent jointly normal random variables with provided means and variances for \(i=1,\ldots,k\), and \(\epsilon \in (0,1)\).

Chance constraints of the above form are added by using the @constraint macro. For example:

@indepnormal(m, x, mean=0,var=1)
@variable(m, z)

@constraint(m, z*x >= -1, with_probability=0.95)

Adds the constraint \(P(z*x \geq -1) \geq 0.95\). Note that the with_probability argument specifies the minimum probability \(\epsilon\) with which the constraint may be satisfied, and so should be a number close to 1.

Distributionally robust chance constraints

One may also specify normally distributed random variables whose parameters (mean and variance) are uncertain, that is, known to fall within a certain interval. These random variables with uncertain distribution are declared as follows:

@indepnormal(m, x, mean=(-1,1), var=(20,30))

Any combination of the mean, variance, or both may be uncertain. When these variables appear in constraints, the constraint is interpreted to be robust, and implies that the the chance constraint must hold for all possible distributions, where the set of possible distributions will be defined more precisely below. Mathematically, this is

\[P\left(\sum_{i=1}^k \left(c_i^Tx +d_i\right)z_i \leq b\right) \geq 1-\epsilon, \forall \text{ distributions of } z_1,\ldots,z_n\]

Using the above notation, let the uncertainty interval on the mean of \(z_i\) be \([\hat\mu_i - \alpha_i,\hat\mu_i + \alpha_i]\) and on the variance \([\hat\sigma_i^2 - \beta_i, \hat\sigma_i^2 + \beta_i]\) where \(\alpha_i \geq 0\) and \(\beta_i \geq 0\).

Currently JuMPChance supports only the following uncertainty sets on the means and variances:

\[ \begin{align}\begin{aligned}M = \left\{ (\mu_1,\ldots,\mu_k) : \exists (s_1,\ldots,s_k) \text{ such that }\mu_i = \hat\mu_i + s_i, |s_i| \leq \alpha_i, \sum_{i=1}^k \frac{|s_i|}{\alpha_i} \leq \Gamma_\mu \right\}\\V = \left\{ (v_1,\ldots,v_k) : \exists (s_1,\ldots,s_k) \text{ such that }v_i = \hat\sigma_i + s_i, |s_i| \leq \beta_i, \sum_{i=1}^k \frac{|s_i|}{\beta_i} \leq \Gamma_\sigma \right\}\end{aligned}\end{align} \]

where \(\Gamma_\mu\) and \(\Gamma_\sigma\) and given (integer) constants, known as the uncertainty budgets. The interpretation of these sets is that at most \(\Gamma\) out of \(k\) uncertain parameters are allows to vary from their nominal values \(\hat\mu_i\) and \(\hat\sigma_i^2\). This is the uncertainty set proposed by Bertsimas and Sim (2004). Note that the means and variances are allowed to vary independently.

The uncertainty budgets \(\Gamma_\mu\) and \(\Gamma_\sigma\) are specified as parameters to @constraint as follows:

@constraint(m, z*x >= -1, with_probability=0.95,
    uncertainty_budget_mean=1, uncertainty_budget_variance=1)

Solving the model

After the model m has been created and all constraints added, calling:




will tell JuMPChance to solve the model. The available solution methods are described in the following section.

The solve function also returns a solution status. This should be checked to confirm that the model was successfully solved to optimality, for example:

status = solve(m)
if status == :Optimal
    println("Solved to optimality")
    println("Not optimal, termination status $status")

Optimal values of the decision variables are available by using getvalue, as with JuMP.