# integer variables Integer variables are variables that must take an integer value (0, 1, 2, ...). A special kind of integer variables is binary variables. Binary variables can only take the value 0 or 1. They are integer variables with a maximum of 1 on them (and don't forget there is always an implicit minimum of 0 on each variable). If all variables are integer then it is a pure integer model, else it is a mixed-integer model, sometimes denoted as MIP (Mixed Integer Programming). There are many practical uses of such variables. Binary variables for example are used to specify that something may be used or not. Integer variables say that a variable must take a multiple of a given value. For example if you want that a given variable must be a multiple of 25 then you can construct following equation: var - 25 i = 0 with i the integer variable and var the variable that must be a multiple of 25. So an extra variable and an extra constraint is needed. This is ok if only a limited number of such variables are in the model, but if there are alot then the model dimensions will increase considerably. As an alternative you can also do a substitution of the variable: var = 25 i Everywhere where variable var is used, substitute it by 25 i. This in the objective function, the constraints and bounds. So variable var is removed from the model and replaced by i. i can be defined as integer. The objective value of this substituted model will be the same as the original one. However to obtain the value of variable var, you must multiply the returned variable i with 25. There is however one drawback on integer variables. These models are harder to solve and solution time can increment exponentially. The more integer variables there are the more time it takes to solve the model. A model without the integer variables may for example be solved in 0.1 seconds while the same model with some of the variables integer can take several minutes to solve. Be aware of this. Also try to limit the solution as much as possible. If you know some extra bounds on variables then it can be very good for the solution time to set them because this limits the number of combinations the algorithm has to examine. Another negative site on integer variables is the inaccurate sensitivity analysis when integer variables are used. See Inaccurate sensitivity analysis in a model with integer variables The integer solution is searched via the so-called 'branch-and-bound' algorithm. The model is first solved without the integer restrictions. Then it is investigated which of the integer variables are non-integer. When such a variable is found the model is split in two sub models. A first one with a minimum restriction on this variable that has the ceiling integer value and a second one with a maximum restriction on this variable that has the floor integer value. Both these sub models are optimised again and now this variable will have an integer value (if there is a solution). The algorithm then looks again if there are still (other) integer variables that have a non-integer value and if so the process is done again. This until a solution is found where are integer variables have integer values. This solution is then remembered as the best-until-now solution and the algorithm continues until it finds again an integer solution and if it is better it takes this one as the best-until-now solution. The more integer variables there are, the more combinations must be investigated and the more time it takes to solve the model. lp_solve supports integer variables since a long time. The API call set_int can be used to define a variable as integer. The API call set_binary can be used to define a variable as binary. In the mps format, integer variables can be specified in the COLUMNS section. See mps-format . Example:
    ROWS
     N  r_0
     L  r_1
     G  r_2
     G  r_3
     G  r_4
    COLUMNS
        x1        r_0                 -1   r_1                  1
        x1        r_2                  2   r_3                 -1
        x2        r_0                 -2   r_1                  1
        x2        r_2                 -1   r_3                  3
        MARK0000  'MARKER'                 'INTORG'
        x3        r_0                0.1   r_4                  1
        MARK0001  'MARKER'                 'INTEND'
        x4        r_0                  3   r_4                  1
    RHS
        RHS       r_1                  5   r_4                0.5
    BOUNDS
     LO BND       x3                 1.1
    ENDATA
    The red lines are two lines that specify that variable x3 is an integer variable. It also has a lower bound of 1.1 set on it. The solution of this model is:
    Value of objective function: -8.13333

    Actual values of the variables:
    x1                   1.66667
    x2                   3.33333
    x3                   2
    x4                   0
As can be seen, the value of x3 is 2, an integer value. If the integer restrictions would not be set on x3 then the value would be 1.1. In the lp-format, variables can be specified as integer by putting them in the int section. See lp-format. The above mps example would be in lp-format:
    min: -x1 -2 x2 +0.1 x3 +3 x4;
    r_1: +x1 +x2 <= 5;
    r_2: +2 x1 -x2 >= 0;
    r_3: -x1 +3 x2 >= 0;
    r_4: +x3 +x4 >= 0.5;
    x3 >= 1.1;

    int x3;
In the lp-format, variables can be specified as binary by putting them in the bin section. See lp-format. For example:
    min: -x1 -2 x2 +0.1 x3 +3 x4;
    r_1: +x1 +x2 <= 5;
    r_2: +2 x1 -x2 >= 0;
    r_3: -x1 +3 x2 >= 0;
    r_4: +x3 +x4 >= 0.5;

    bin x3;