# 6.7 Integer Optimization¶

An optimization problem where one or more of the variables are constrained to integer values is called a (mixed) integer optimization problem. MOSEK supports integer variables in combination with linear, quadratic and quadratically constrtained and conic problems (except semidefinite). See the previous tutorials for an introduction to how to model these types of problems.

## 6.7.1 Example MILO1¶

We use the example

(6.13)$\begin{split}\begin{array}{lccl} \mbox{maximize} & x_0 + 0.64 x_1 & & \\ \mbox{subject to} & 50 x_0 + 31 x_1 & \leq & 250, \\ & 3 x_0 - 2 x_1 & \geq & -4, \\ & x_0, x_1 \geq 0 & & \mbox{and integer} \end{array}\end{split}$

to demonstrate how to set up and solve a problem with integer variables. It has the structure of a linear optimization problem (see Sec. 6.1 (Linear Optimization)) except for integrality constraints on the variables. Therefore, only the specification of the integer constraints requires something new compared to the linear optimization problem discussed previously.

First, the integrality constraints are imposed using the function Task.putvartype:

      for (int j = 0; j < numvar; ++j)


Next, the example demonstrates how to set various useful parameters of the mixed-integer optimizer. See Sec. 13.4 (The Optimizer for Mixed-integer Problems) for details.

      /* Set max solution time */


The complete source for the example is listed Listing 6.9. Please note that when Task.getsolutionslice is called, the integer solution is requested by using soltype.itg. No dual solution is defined for integer optimization problems.

Listing 6.9 Source code implementing problem (6.13). Click here to download.
package com.mosek.example;
import mosek.*;

public class milo1 {
static final int numcon = 2;
static final int numvar = 2;

public static void main (String[] args) {
// Since the value infinity is never used, we define
// 'infinity' symbolic purposes only
double infinity = 0;

mosek.boundkey[] bkc
= { mosek.boundkey.up, mosek.boundkey.lo };
double[] blc = { -infinity,         -4.0 };
double[] buc = { 250.0,             infinity };

mosek.boundkey[] bkx
= { mosek.boundkey.lo, mosek.boundkey.lo  };
double[] blx = { 0.0,               0.0 };
double[] bux = { infinity,          infinity };

double[] c   = {1.0, 0.64 };

int[][] asub    = { {0,   1},    {0,    1}   };
double[][] aval = { {50.0, 3.0}, {31.0, -2.0} };

int[] ptrb = { 0, 2 };
int[] ptre = { 2, 4 };

double[] xx  = new double[numvar];

try (Env  env  = new Env();
// Directs the log task stream to the user specified
mosek.streamtype.log,
new mosek.Stream()
{ public void stream(String msg) { System.out.print(msg); }});
new mosek.ItgSolutionCallback() {
public void callback(double[] xx) {
System.out.print("New integer solution: ");
for (double v : xx) System.out.print("" + v + " ");
System.out.println("");
}
});
/* Append 'numcon' empty constraints.
The constraints will initially have no bounds. */

/* Append 'numvar' variables.
The variables will initially be fixed at zero (x=0). */

for (int j = 0; j < numvar; ++j) {
/* Set the linear term c_j in the objective.*/
/* Set the bounds on variable j.
blx[j] <= x_j <= bux[j] */
/* Input column j of A */
asub[j],               /* Row index of non-zeros in column j.*/
aval[j]);              /* Non-zero Values of column j. */
}
/* Set the bounds on constraints.
for i=1, ...,numcon : blc[i] <= constraint i <= buc[i] */
for (int i = 0; i < numcon; ++i)

/* Specify integer variables. */
for (int j = 0; j < numvar; ++j)

/* Set max solution time */

/* A maximization problem */
/* Solve the problem */
try {
} catch (mosek.Warning e) {
System.out.println (" Mosek warning:");
System.out.println (e.toString ());
}

// Print a summary containing information
//   about the solution for debugging purposes
xx);
mosek.solsta solsta[] = new mosek.solsta;
/* Get status information about the solution */

switch (solsta) {
case integer_optimal:
System.out.println("Optimal solution\n");
for (int j = 0; j < numvar; ++j)
System.out.println ("x[" + j + "]:" + xx[j]);
break;
case prim_feas:
System.out.println("Feasible solution\n");
for (int j = 0; j < numvar; ++j)
System.out.println ("x[" + j + "]:" + xx[j]);
break;

case unknown:
mosek.prosta prosta[] = new mosek.prosta;
switch (prosta) {
case prim_infeas_or_unbounded:
System.out.println("Problem status Infeasible or unbounded");
break;
case prim_infeas:
System.out.println("Problem status Infeasible.");
break;
case unknown:
System.out.println("Problem status unknown.");
break;
default:
System.out.println("Other problem status.");
break;
}
break;
default:
System.out.println("Other solution status");
break;
}
}
catch (mosek.Exception e) {
System.out.println ("An error or warning was encountered");
System.out.println (e.getMessage ());
throw e;
}
}
}


## 6.7.2 Specifying an initial solution¶

It is a common strategy to provide a starting feasible point (if one is known in advance) to the mixed-integer solver. This can in many cases reduce solution time.

It is not necessary to specify the whole solution. MOSEK will attempt to use it to speed up the computation. MOSEK will first try to construct a feasible solution by fixing integer variables to the values provided by the user (rounding if necessary) and optimizing over the continuous variables. The outcome of this process can be inspected via information items iinfitem.mio_construct_solution and dinfitem.mio_construct_solution_obj, and via the Construct solution objective entry in the log. We concentrate on a simple example below.

(6.14)$\begin{split}\begin{array} {ll} \mbox{maximize} & 7 x_0 + 10 x_1 + x_2 + 5 x_3 \\ \mbox{subject to} & x_0 + x_1 + x_2 + x_3 \leq 2.5\\ & x_0,x_1,x_2 \in \integral \\ & x_0,x_1,x_2,x_3 \geq 0 \end{array}\end{split}$

Solution values can be set using Task.putsolution .

Listing 6.10 Implementation of problem (6.14) specifying an initial solution. Click here to download.
      // Assign values to integer variables
// We only set that slice of xx
task.putxxslice(mosek.soltype.itg, 0, 3, new double[]{1.0, 1.0, 0.0});


The log output from the optimizer will in this case indicate that the inputted values were used to construct an initial feasible solution:

Construct solution objective       : 1.950000000000e+01


The same information can be obtained from the API:

Listing 6.11 Retrieving information about usage of initial solution Click here to download.
      int constr = task.getintinf(mosek.iinfitem.mio_construct_solution);
System.out.println("Initial solution utilization: " + constr);
System.out.println("Initial solution objective: " +  constrVal);


## 6.7.3 Example MICO1¶

Integer variables can also be used arbitrarily in conic problems (except semidefinite). We refer to the previous tutorials for how to set up a conic optimization problem. Here we present sample code that sets up a simple optimization problem:

(6.15)$\begin{split}\begin{array}{ll} \mbox{minimize} & x^2+y^2 \\ \mbox{subject to} & x \geq e^y+3.8, \\ & x, y \ \mbox{integer}. \end{array}\end{split}$

The canonical conic formulation of (6.15) suitable for Optimizer API for Java is

(6.16)$\begin{split}\begin{array}{lrclr} \mbox{minimize} & x_0 & & & \\ \mbox{subject to} & (x_0,x_1,x_2)&\in&\Q^3 & (x_0\geq\sqrt{x_1^2+x_2^2}) \\ & (x_3,x_4,x_5)&\in&\EXP & (x_3\geq x_4 \exp(x_5/x_4)) \\ & -x_1 +x_3 &=& -3.8 & \\ & x_4 &=& 1 & \\ & x_2 - x_5 &=& 0 & \\ & x_1, x_2 & & \mbox{integer}. & \end{array}\end{split}$
Listing 6.12 Implementation of problem (6.16). Click here to download.
public class mico1 {
public static void main (String[] args)  {
try (Env  env  = new Env();
// Directs the log task stream to the user specified
mosek.streamtype.log,
new mosek.Stream()
{ public void stream(String msg) { System.out.print(msg); }});

// Integrality constraints
new mosek.variabletype[]{mosek.variabletype.type_int, mosek.variabletype.type_int});

// Set up the three auxiliary linear constraints
new int[]{1,3,4,2,5},
new double[]{-1,1,1,1,-1});
new mosek.boundkey[]{mosek.boundkey.fx, mosek.boundkey.fx, mosek.boundkey.fx},
new double[]{-3.8, 1, 0}, new double[]{-3.8, 1, 0});

// Objective

// Conic part of the problem