# Algebraic modeling languages

## What is an algebraic modeling language?

If you have taken a class in mixed-integer linear programming, you will have seen a formulation like:

\begin{aligned} \min \; & c^\top x \\ \text{s.t.} & A x = b \\ & x \ge 0 \\ & x_i \in \mathbb{Z}, \quad \forall i \in \mathcal{I} \end{aligned}

where c, A, and b are appropriately sized vectors and matrices of data, and $\mathcal{I}$ denotes the set of variables that are integer.

Solvers expect problems in a standard form like this because it limits the types of constraints that they need to consider. This makes writing a solver much easier.

What is a solver?

A solver is a software package that computes solutions to one or more classes of problems.

For example, GLPK is a solver for linear programming (LP) and mixed integer programming (MIP) problems. It incorporates algorithms such as the simplex method and the interior-point method.

JuMP currently supports a number of open-source and commercial solvers, which can be viewed in the Supported-solvers table.

However, you probably formulated problems algebraically like so:

\begin{aligned} \min \; & \sum\limits_{i = 1}^n c_i x_i \\ \text{s.t.} & \sum\limits_{i = 1}^n w_i x_i \le b \\ & x_i \ge 0 \quad \forall i = 1,\ldots,n \\ & x_i \in \mathbb{Z} \quad \forall i = 1,\ldots,n. \end{aligned}
Info

Do you recognize this formulation? It's the knapsack problem.

Users prefer to write problems in algebraic form because it is more convenient. For example, we just used $\le b$, even though the standard form only supported constraints of the form $Ax = b$.

We could convert our knapsack problem into the standard form by adding a new slack variable $x_0$ like so:

\begin{aligned} \min \; & \sum\limits_{i = 1}^n c_i x_i \\ \text{s.t.} & x_0 + \sum\limits_{i = 1}^n w_i x_i = b \\ & x_i \ge 0 \quad \forall i = 0,\ldots,n \\ & x_i \in \mathbb{Z} \quad \forall i = 1,\ldots,n. \end{aligned}

However, as models get more complicated, this manual conversion becomes more and more error-prone.

An algebraic modeling language is a tool that simplifies the translation between the algebraic form of the modeler, and the standard form of the solver.

Each algebraic modeling language has two main parts:

1. A domain specific language for the user to write down problems in algebraic form.
2. A converter from the algebraic form into a standard form supported by the solver (and back again).

## Part I: writing in algebraic form

JuMP provides the first part of an algebraic modeling language using the @variable, @objective, and @constraint macros.

For example, here's how we would write the knapsack problem in JuMP:

julia> function algebraic_knapsack(c, w, b)
n = length(c)
model = Model()
@variable(model, x[1:n] >= 0, Int)
@objective(model, Min, sum(c[i] * x[i] for i = 1:n))
@constraint(model, sum(w[i] * x[i] for i = 1:n) <= b)
return print(model)
end
algebraic_knapsack (generic function with 1 method)

julia> algebraic_knapsack([1, 2], [0.5, 0.5], 1.25)
Min x[1] + 2 x[2]
Subject to
0.5 x[1] + 0.5 x[2] ≤ 1.25
x[1] ≥ 0.0
x[2] ≥ 0.0
x[1] integer
x[2] integer

This formulation is compact, and it closely matches the algebraic formulation of the model we wrote out above.

Here's what the JuMP code would look like if we didn't use macros:

julia> function nonalgebraic_knapsack(c, w, b)
n = length(c)
model = Model()
x = [VariableRef(model) for i = 1:n]
for i = 1:n
set_lower_bound(x[i], 0)
set_integer(x[i])
set_name(x[i], "x[\$i]")
end
obj = AffExpr(0.0)
for i = 1:n
end
set_objective(model, MOI.MIN_SENSE, obj)
lhs = AffExpr(0.0)
for i = 1:n
end
con = build_constraint(error, lhs, MOI.LessThan(b))
return print(model)
end
nonalgebraic_knapsack (generic function with 1 method)

julia> nonalgebraic_knapsack([1, 2], [0.5, 0.5], 1.25)
Min x[1] + 2 x[2]
Subject to
0.5 x[1] + 0.5 x[2] ≤ 1.25
x[1] ≥ 0.0
x[2] ≥ 0.0
x[1] integer
x[2] integer

Hopefully you agree that the macro version is much easier to read!

## Part II: talking to solvers

Now that we have the algebraic problem from the user, we need a way of communicating the problem to the solver, and a way of returning the solution from the solver back to the user.

This is less trivial than it might seem, because each solver has a unique application programming interface (API) and data structures for representing optimization models and obtaining results.

JuMP uses the MathOptInterface.jl package to abstract these differences between solvers.

### What is MathOptInterface?

MathOptInterface (MOI) is an abstraction layer designed to provide an interface to mathematical optimization solvers so that users do not need to understand multiple solver-specific APIs. MOI can be used directly, or through a higher-level modeling interface like JuMP.

There are three main parts to MathOptInterface:

1. A solver-independent API that abstracts concepts such as adding and deleting variables and constraints, setting and getting parameters, and querying results. For more information on the MathOptInterface API, read the documentation

2. An automatic rewriting system based on equivalent formulations of a constraint. For more information on this rewriting system, read the LazyBridgeOptimizer section of the manual, and our paper on arXiv.

3. Utilities for managing how and when models are copied to solvers. For more information on this, read the CachingOptimizer section of the manual.