# 7.1 Linear Optimization¶

The simplest optimization problem is a purely linear problem. A linear optimization problem is a problem of the following form:

Minimize or maximize the objective function

$\sum_{j=0}^{n-1} c_j x_j + c^f$

subject to the linear constraints

$l_k^c \leq \sum_{j=0}^{n-1} a_{kj} x_j \leq u_k^c,\quad k=0,\ldots ,m-1,$

and the bounds

$l_j^x \leq x_j \leq u_j^x, \quad j=0,\ldots ,n-1.$

The problem description consists of the following elements:

• $$m$$ and $$n$$ — the number of constraints and variables, respectively,

• $$x$$ — the variable vector of length $$n$$,

• $$c$$ — the coefficient vector of length $$n$$

$\begin{split}c = \left[ \begin{array}{c} c_0 \\ \vdots \\ c_{n-1} \end{array} \right],\end{split}$
• $$c^f$$ — fixed term in the objective,

• $$A$$ — an $$m\times n$$ matrix of coefficients

$\begin{split}A = \left[ \begin{array}{ccc} a_{0,0} & \cdots & a_{0,(n-1)} \\ \vdots & \cdots & \vdots \\ a_{(m-1),0} & \cdots & a_{(m-1),(n-1)} \end{array} \right],\end{split}$
• $$l^c$$ and $$u^c$$ — the lower and upper bounds on constraints,

• $$l^x$$ and $$u^x$$ — the lower and upper bounds on variables.

Please note that we are using $$0$$ as the first index: $$x_0$$ is the first element in variable vector $$x$$.

The Fusion user does not need to specify all of the above elements explicitly — they will be assembled from the Fusion model.

## 7.1.1 Example LO1¶

The following is an example of a small linear optimization problem:

(7.1)$\begin{split}\begin{array} {lccccccccl} \mbox{maximize} & 3 x_0 & + & 1 x_1 & + & 5 x_2 & + & 1 x_3 & & \\ \mbox{subject to} & 3 x_0 & + & 1 x_1 & + & 2 x_2 & & & = & 30, \\ & 2 x_0 & + & 1 x_1 & + & 3 x_2 & + & 1 x_3 & \geq & 15, \\ & & & 2 x_1 & & & + & 3 x_3 & \leq & 25, \end{array}\end{split}$

under the bounds

$\begin{split}\begin{array}{ccccc} 0 & \leq & x_0 & \leq & \infty , \\ 0 & \leq & x_1 & \leq & 10, \\ 0 & \leq & x_2 & \leq & \infty ,\\ 0 & \leq & x_3 & \leq & \infty . \end{array}\end{split}$

We start our implementation in Fusion importing the relevant modules, i.e.

from mosek.fusion import *


Next we declare an optimization model creating an instance of the Model class:

    with Model("lo1") as M:


For this simple problem we are going to enter all the linear coefficients directly:

    A = [[3.0, 1.0, 2.0, 0.0],
[2.0, 1.0, 3.0, 1.0],
[0.0, 2.0, 0.0, 3.0]]
c = [3.0, 1.0, 5.0, 1.0]


The variables appearing in problem (7.1) can be declared as one $$4$$-dimensional variable:

        x = M.variable("x", 4, Domain.greaterThan(0.0))


At this point we already have variables with bounds $$0\leq x_i\leq \infty$$, because the domain is applied element-wise to the entries of the variable vector. Next, we impose the upper bound on $$x_1$$:

        M.constraint(x.index(1), Domain.lessThan(10.0))


The linear constraints can now be entered one by one using the dot product of our variable with a coefficient vector:

        M.constraint("c1", Expr.dot(A[0], x), Domain.equalsTo(30.0))
M.constraint("c2", Expr.dot(A[1], x), Domain.greaterThan(15.0))
M.constraint("c3", Expr.dot(A[2], x), Domain.lessThan(25.0))


We end the definition of our optimization model setting the objective function in the same way:

        M.objective("obj", ObjectiveSense.Maximize, Expr.dot(c, x))


Finally, we only need to call the Model.solve method:

        M.solve()


The solution values can be attained with the method Variable.level.

        sol = x.level()
print('\n'.join(["x[%d] = %f" % (i, sol[i]) for i in range(4)]))

Listing 7.1 Fusion implementation of model (7.1). Click here to download.
from mosek.fusion import *

def main(args):
A = [[3.0, 1.0, 2.0, 0.0],
[2.0, 1.0, 3.0, 1.0],
[0.0, 2.0, 0.0, 3.0]]
c = [3.0, 1.0, 5.0, 1.0]

# Create a model with the name 'lo1'
with Model("lo1") as M:

# Create variable 'x' of length 4
x = M.variable("x", 4, Domain.greaterThan(0.0))

# Create constraints
M.constraint(x.index(1), Domain.lessThan(10.0))
M.constraint("c1", Expr.dot(A[0], x), Domain.equalsTo(30.0))
M.constraint("c2", Expr.dot(A[1], x), Domain.greaterThan(15.0))
M.constraint("c3", Expr.dot(A[2], x), Domain.lessThan(25.0))

# Set the objective function to (c^t * x)
M.objective("obj", ObjectiveSense.Maximize, Expr.dot(c, x))

# Solve the problem
M.solve()

# Get the solution values
sol = x.level()
print('\n'.join(["x[%d] = %f" % (i, sol[i]) for i in range(4)]))