Using lpsolve from Octave
Octave?
GNU Octave is a highlevel language, primarily intended for numerical
computations. It provides a convenient command line interface for
solving linear and nonlinear problems numerically, and for performing
other numerical experiments. It may also be used as a batchoriented
language.
GNU Octave is also freely redistributable software.
We will not discuss the specifics of Octave here but instead refer the reader to the
Octave website and
GNU Octave Repository.
Octave and lpsolve
lpsolve is callable from Octave via a dynamic linked function. As such, it looks like lpsolve is fully integrated
with Octave. Matrices can directly be transferred between Octave and lpsolve in both directions. The complete interface
is written in C so it has maximum performance. The whole lpsolve API is implemented with some extra's specific for
Octave (especially for matrix support). So you have full control to the complete lpsolve functionality via the octlpsolve
Octave driver. If you find that this involves too much work to solve an lp model then you can also work via higherlevel
script files that can make things a lot easier. See further in this article.
Note that your version of Octave must support dynamic linking. To find out if it does, type the command:
octave_config_info ("ENABLE_DYNAMIC_LINKING")
at the Octave prompt. Support for dynamic linking is included if this expression returns the string "true".
If this is not the case, then Octave is not able to call dynamic linked routines and as such also not lpsolve.
In that case you must recompile Octave with dynamic linked routines enabled.
Under Linux, the following commands must be executed for this:
configure enableshared
make
See Installing Octave for more information.
Under Windows, dynamic linking should already be active. If not, then Octave must be recompiled. The
following document can help in this process: README.Windows
The octave dynamic linked routine is in a file with extension .oct. It is impossible to provide a precompiled
octlpsolve.oct file with the lpsolve distribution because the structure of the .oct files change often on new
releases of Octave and are not compatible with each other. Therefore you must build the driver yourself.
Look at the end of this document how to do this.
Installation
A driver program is needed: octlpsolve (octlpsolve.oct).
This driver must be put in a directory known to Octave (in one of the directories from the Octave 'path' command) and Octave can call the octlpsolve solver.
This driver calls lpsolve via the lpsolve shared library (lpsolve51.dll under Windows
and liblpsolve51.so under Unix/Linux). This has the advantage that the octlpsolve driver doesn't have to
be recompiled when an update of lpsolve is provided.
So note the difference between the Octave lpsolve driver that is called octlpsolve and the lpsolve library that implements the
API that is called lpsolve51.
There are also some Octave script files (.m) as a quick start.
To test if everything is installed correctly, enter octlpsolve in the Octave command window.
If it gives the following, then everything is ok:
octlpsolve Octave Interface version 5.1.0.1
using lpsolve version 5.1.1.3
Usage: [ret1, ret2, ...] = octlpsolve('functionname', arg1, arg2, ...)
However, if you get the following:
error: 'octlpsolve' undefined near line 2 column 1
Then Octave cannot find octlpsolve.oct
If you get the following:
error: Failed to initialise lpsolve library.
Then Octave can find the octlpsolve driver program, but the driver program cannot find the lpsolve library
that contains the lpsolve implementation. This library is called lpsolve51.dll under Windows
and liblpsolve51.so under Unix/Linux. Under Windows, the dll should be in a directory specified by the PATH
environment variables and under Unix/Linux in directory /lib, /usr/lib or a directory defined by LD_LIBRARY_PATH.
Solve an lp model from Octave via octlpsolve
In the following text, octave:> before the Octave commands is the Octave prompt.
Only the text after octave:> must be entered.
Note that the default prompt also contains an incrementing number starting from one each time Octave starts.
For this documentation, the default Octave was changed with the following command: PS1 = "\\s:> "
To call an lpsolve function, the following syntax must be used:
octave:> [ret1, ret2, ...] = octlpsolve('functionname', arg1, arg2, ...)
The return values are optional and depend on the function called. functionname must always be enclosed between single or double
quotes to make it alphanumerical and it is case sensitive. The number and type of arguments depend on the function called.
Some functions even have a variable number of arguments and a different behaviour occurs depending on the type of the argument.
functionname can be (almost) any of the lpsolve API routines (see lp_solve API reference)
plus some extra Octave specific functions.
Most of the lpsolve API routines use or return an lprec structure. To make things more robust in Octave, this structure
is replaced by a handle. This is an incrementing number starting from 0 and the lprec structures are maintained
internally by the octlpsolve driver. However you will see not much (if any) difference in the use of it.
Almost all callable functions can be found in the lp_solve API reference.
Some are exactly as described in the reference guide, others have a slightly different syntax to make maximum
use of the Octave functionality. For example make_lp is used identical as described. But get_variables is slightly
different. In the API reference, this function has two arguments. The first the lp handle and the second the
resulting variables and this array must already be dimensioned. When lpsolve is used from Octave, nothing must
be dimensioned in advance. The octlpsolve driver takes care of dimensioning all return variables and they are
always returned as return value of the call to octlpsolve. Never as argument to the routine. This can be a single
value as for get_objective (although Octave stores this in a 1x1 matrix) or a matrix or vector as in get_variables.
In this case, get_variables returns a 4x1 matrix (vector) with the result of the 4 variables of the lp model.
Note that you can get an overview of the available functionnames and their arguments by entering the following in Octave:
>> help octlpsolve.m
An example
(Note that you can execute this example by entering command per command as shown below or by just entering example1.
This will execute example1.m. You can see its contents by entering type example1.m)
octave:> lp=octlpsolve('make_lp', 0, 4);
octave:> octlpsolve('set_verbose', lp, 3);
octave:> octlpsolve('set_obj_fn', lp, [1, 3, 6.24, 0.1]);
octave:> octlpsolve('add_constraint', lp, [0, 78.26, 0, 2.9], 2, 92.3);
octave:> octlpsolve('add_constraint', lp, [0.24, 0, 11.31, 0], 1, 14.8);
octave:> octlpsolve('add_constraint', lp, [12.68, 0, 0.08, 0.9], 2, 4);
octave:> octlpsolve('set_lowbo', lp, 1, 28.6);
octave:> octlpsolve('set_lowbo', lp, 4, 18);
octave:> octlpsolve('set_upbo', lp, 4, 48.98);
octave:> octlpsolve('set_col_name', lp, 1, 'COLONE');
octave:> octlpsolve('set_col_name', lp, 2, 'COLTWO');
octave:> octlpsolve('set_col_name', lp, 3, 'COLTHREE');
octave:> octlpsolve('set_col_name', lp, 4, 'COLFOUR');
octave:> octlpsolve('set_row_name', lp, 1, 'THISROW');
octave:> octlpsolve('set_row_name', lp, 2, 'THATROW');
octave:> octlpsolve('set_row_name', lp, 3, 'LASTROW');
octave:> octlpsolve('write_lp', lp, 'a.lp');
octave:> octlpsolve('get_mat', lp, 1, 2)
ans = 78.260
octave:> octlpsolve('solve', lp)
ans = 0
octave:> octlpsolve('get_objective', lp)
ans = 31.783
octave:> octlpsolve('get_variables', lp)
ans =
28.60000
0.00000
0.00000
31.82759
octave:> octlpsolve('get_constraints', lp)
ans =
92.3000
6.8640
391.2928
Note that there are some commands that return an answer. To see the answer, the command was not terminated with
a semicolon (;). If the semicolon is put at the end of a command, the answer is not shown. However it is also possible
to write the answer in a variable. For example:
octave:> obj=octlpsolve('get_objective', lp)
obj = 31.783
Or without echoing on screen:
octave:> obj=octlpsolve('get_objective', lp);
The last command will only write the result in variable obj without showing anything on screen.
get_variables and get_constraints return a vector with the result. This can also be put in a variable:
octave:> x=octlpsolve('get_variables', lp);
octave:> b=octlpsolve('get_constraints', lp);
It is always possible to show the contents of a variable by just giving it as command:
octave:> x
x =
28.60000
0.00000
0.00000
31.82759
Don't forget to free the handle and its associated memory when you are done:
octave:> octlpsolve('delete_lp', lp);
Matrices
In Octave, all numerical data is stored in matrices; even a scalar variable. Octave also supports complex numbers
(a + b * i with i=sqrt(1)). octlpsolve can only work with real numbers.
For example:
octave:> octlpsolve('add_constraint', lp, [0.24, 0, 11.31, 0], 1, 14.8);
Most of the time, variables are used to provide the data:
octave:> octlpsolve('add_constraint', lp, a1, 1, 14.8);
Where a1 is a matrix variable.
Most of the time, octlpsolve needs vectors (rows or columns).
In all situations, it doesn't matter if the vectors are row or column vectors. The driver accepts them both.
For example:
octave:> octlpsolve('add_constraint', lp, [0.24; 0; 11.31; 0], 1, 14.8);
Which is a column vector, but it is also accepted.
An important final note. Several lp_solve API routines accept a vector where the first element (element 0) is not used.
Other lp_solve API calls do use the first element. In the Octave interface, there is never an unused element in the matrices.
So if the lp_solve API specifies that the first element is not used, then this element is not in the Octave matrix.
Sets
All numerical data is stored in matrices. Alphanumerical data, however, is more difficult to store in matrices.
Matrices require that each element has the same size (length) and that is difficult and unpractical for alphanumerical
data. In a limited number of lpsolve routines, alphanumerical data is required or returned and in some also multiple
elements. An example is set_col_name. For this, Octave sets are used. To specify a set of alphanumerical elements,
the following notation is used: { 'element1', 'element2', ... }. Note the { and } symbols instead of [ and ] that
are used with matrices.
Maximum usage of matrices/sets with octlpsolve
Because Octave is all about matrices, all lpsolve API routines that need a column or row number to get/set information for that
column/row are extended in the octlpsolve Octave driver to also work with matrices. For example set_int in the API can
only set the integer status for one column. If the status for several integer variables must be set, then set_int
must be called multiple times. The octlpsolve Octave driver however also allows specifying a vector to set the integer
status of all variables at once. The API call is: return = octlpsolve('set_int', lp_handle, column, must_be_int). The
matrix version of this call is: return = octlpsolve('set_int', lp_handle, [must_be_int]).
The API call to return the integer status of a variable is: return = octlpsolve('is_int', lp_handle, column). The
matrix version of this call is: [is_int] = octlpsolve('is_int', lp_handle)
Also note the get_mat and set_mat routines. In Octave these are extended to return/set the complete constraint matrix.
See following example.
Above example can thus also be done as follows:
(Note that you can execute this example by entering command per command as shown below or by just entering example2.
This will execute example2.m. You can see its contents by entering type example2.m)
octave:> lp=octlpsolve('make_lp', 0, 4);
octave:> octlpsolve('set_verbose', lp, 3);
octave:> octlpsolve('set_obj_fn', lp, [1, 3, 6.24, 0.1]);
octave:> octlpsolve('add_constraint', lp, [0, 78.26, 0, 2.9], 2, 92.3);
octave:> octlpsolve('add_constraint', lp, [0.24, 0, 11.31, 0], 1, 14.8);
octave:> octlpsolve('add_constraint', lp, [12.68, 0, 0.08, 0.9], 2, 4);
octave:> octlpsolve('set_lowbo', lp, [28.6, 0, 0, 18]);
octave:> octlpsolve('set_upbo', lp, [Inf, Inf, Inf, 48.98]);
octave:> octlpsolve('set_col_name', lp, {'COLONE', 'COLTWO', 'COLTHREE', 'COLFOUR'});
octave:> octlpsolve('set_row_name', lp, {'THISROW', 'THATROW', 'LASTROW'});
octave:> octlpsolve('write_lp', lp, 'a.lp');
octave:> octlpsolve('get_mat', lp)
ans =
0.00000 78.26000 0.00000 2.90000
0.24000 0.00000 11.31000 0.00000
12.68000 0.00000 0.08000 0.90000
octave:> octlpsolve('solve', lp)
ans = 0
octave:> octlpsolve('get_objective', lp)
ans = 31.783
octave:> octlpsolve('get_variables', lp)
ans =
28.60000
0.00000
0.00000
31.82759
octave:> octlpsolve('get_constraints', lp)
ans =
92.3000
6.8640
391.2928
Note the usage of Inf in set_upbo. This stands for 'infinity'. Meaning an infinite upper bound.
It is also possible to use Inf to express minus infinity. This can for example be used to create a free variable.
To show the full power of the matrices, let's now do some matrix calculations to check the solution.
It works further on above example:
octave:> A=octlpsolve('get_mat', lp);
octave:> X=octlpsolve('get_variables', lp);
octave:> B = A * X
B =
92.3000
6.8640
391.2928
So what we have done here is calculate the values of the constraints (RHS) by multiplying the constraint matrix
with the solution vector. Now take a look at the values of the constraints that lpsolve has found:
octave:> octlpsolve('get_constraints', lp)
ans =
92.3000
6.8640
391.2928
Exactly the same as the calculated B vector, as expected.
Also the value of the objective can be calculated in a same way:
octave:> C=octlpsolve('get_obj_fn', lp);
octave:> X=octlpsolve('get_variables', lp);
octave:> obj = C * X
obj = 31.783
So what we have done here is calculate the value of the objective by multiplying the objective vector
with the solution vector. Now take a look at the value of the objective that lpsolve has found:
octave:> octlpsolve('get_objective', lp)
ans = 31.783
Again exactly the same as the calculated obj value, as expected.
Script files
Octave can execute a sequence of statements stored in diskfiles. Script files mostly have the file type of ".m"
as the last part of their filename (extension).
Much of your work with Octave will be in creating and refining script files. Script files are
usually created using your local editor.
Script files can be compared with batch files or scripts. You can put Octave commands in them and execute them at
any time. The script file is executed like any other command, by entering its name (without the .m extension).
The octlpsolve Octave distribution contains some example script files to demonstrate this.
To see the contents of such a file, enter the command 'type filename'. You can also edit these files with your
favourite text editor (or notepad).
example1.m
Contains the commands as shown in the first example of this article.
example2.m
Contains the commands as shown in the second example of this article.
example3.m
Contains the commands of a practical example. See further in this article.
example4.m
Contains the commands of a practical example. See further in this article.
example5.m
Contains the commands of a practical example. See further in this article.
example6.m
lp_solve.m
This script uses the API to create a higherlevel function called lp_solve.
This function accepts as arguments some matrices and options to create and solve an lp model.
See the beginning of the file or type help lp_solve or just lp_solve to see its usage:
LP_SOLVE Solves mixed integer linear programming problems.
SYNOPSIS: [obj,x,duals] = lp_solve(f,a,b,e,vlb,vub,xint,scalemode,keep)
solves the MILP problem
max v = f'*x
a*x <> b
vlb <= x <= vub
x(int) are integer
ARGUMENTS: The first four arguments are required:
f: n vector of coefficients for a linear objective function.
a: m by n matrix representing linear constraints.
b: m vector of right sides for the inequality constraints.
e: m vector that determines the sense of the inequalities:
e(i) = 1 ==> Less Than
e(i) = 0 ==> Equals
e(i) = 1 ==> Greater Than
vlb: n vector of lower bounds. If empty or omitted,
then the lower bounds are set to zero.
vub: n vector of upper bounds. May be omitted or empty.
xint: vector of integer variables. May be omitted or empty.
scalemode: scale flag. Off when 0 or omitted.
keep: Flag for keeping the lp problem after it's been solved.
If omitted, the lp will be deleted when solved.
OUTPUT: A nonempty output is returned if a solution is found:
obj: Optimal value of the objective function.
x: Optimal value of the decision variables.
duals: solution of the dual problem.
Example of usage. To create and solve following lpmodel:
max: x1 + 2 x2;
C1: 2x1 + x2 < 5;
4 x1 + 4 x2 <5;
int x2,x1;
The following command can be used:
octave:> [obj, x]=lp_solve([1, 2], [2, 1; 4, 4], [5, 5], [1, 1], [], [], [1, 2])
obj = 3
x =
1
2
lp_maker.m
This script is analog to the lp_solve script and also uses the API to create a higherlevel function called lp_maker.
This function accepts as arguments some matrices and options to create an lp model. Note that this scripts only
creates a model and returns a handle.
See the beginning of the file or type help lp_maker or just lp_maker to see its usage:
octave:> help lp_maker
LP_MAKER Makes mixed integer linear programming problems.
SYNOPSIS: lp_handle = lp_maker(f,a,b,e,vlb,vub,xint,scalemode,setminim)
make the MILP problem
max v = f'*x
a*x <> b
x >= vlb >= 0
x <= vub
x(int) are integer
ARGUMENTS: The first four arguments are required:
f: n vector of coefficients for a linear objective function.
a: m by n matrix representing linear constraints.
b: m vector of right sides for the inequality constraints.
e: m vector that determines the sense of the inequalities:
e(i) < 0 ==> Less Than
e(i) = 0 ==> Equals
e(i) > 0 ==> Greater Than
vlb: n vector of nonnegative lower bounds. If empty or omitted,
then the lower bounds are set to zero.
vub: n vector of upper bounds. May be omitted or empty.
xint: vector of integer variables. May be omitted or empty.
scalemode: scale flag. Off when 0 or omitted.
setminim: Set maximum lp when this flag equals 0 or omitted.
OUTPUT: lp_handle is an integer handle to the lp created.
Example of usage. To create following lpmodel:
max: x1 + 2 x2;
C1: 2x1 + x2 < 5;
4 x1 + 4 x2 <5;
int x2,x1;
The following command can be used:
octave:> lp=lp_maker([1, 2], [2, 1; 4, 4], [5, 5], [1, 1], [], [], [1, 2])
lp = 0
To solve the model and get the solution:
octave:> octlpsolve('solve', lp)
ans = 0
octave:> octlpsolve('get_objective', lp)
ans = 3
octave:> octlpsolve('get_variables', lp)
ans =
1
2
Don't forget to free the handle and its associated memory when you are done:
octave:> octlpsolve('delete_lp', lp);
lpdemo.m
Contains several examples to build and solve lp models.
ex.m
Contains several examples to build and solve lp models.
Also solves the lp_examples from the lp_solve distribution.
A practical example
We shall illustrate the method of linear programming by means of a simple example,
giving a combination graphical/numerical solution, and then solve both a slightly as well as a substantially
more complicated problem.
Suppose a farmer has 75 acres on which to plant two crops: wheat and barley.
To produce these crops, it costs the farmer (for seed, fertilizer, etc.) $120 per acre for the
wheat and $210 per acre for the barley. The farmer has $15000 available for expenses.
But after the harvest, the farmer must store the crops while awaiting favourable market conditions.
The farmer has storage space for 4000 bushels. Each acre yields an average of 110 bushels of wheat
or 30 bushels of barley. If the net profit per bushel of wheat (after all expenses have been subtracted)
is $1.30 and for barley is $2.00, how should the farmer plant the 75 acres to maximize profit?
We begin by formulating the problem mathematically.
First we express the objective, that is the profit, and the constraints
algebraically, then we graph them, and lastly we arrive at the solution
by graphical inspection and a minor arithmetic calculation.
Let x denote the number of acres allotted to wheat and y the number of acres allotted to barley.
Then the expression to be maximized, that is the profit, is clearly
P = (110)(1.30)x + (30)(2.00)y = 143x + 60y.
There are three constraint inequalities, specified by the limits on expenses, storage and acreage.
They are respectively:
120x + 210y <= 15000
110x + 30y <= 4000
x + y <= 75
Strictly speaking there are two more constraint inequalities forced by the fact that the farmer cannot plant
a negative number of acres, namely:
x >= 0, y >= 0.
Next we graph the regions specified by the constraints. The last two say that we only need to consider
the first quadrant in the xy plane. Here's a graph delineating the triangular region in the first quadrant determined
by the first inequality.
octave:> X = 0.1:0.1:125;
octave:> Y1 = (15000  120.*X)./210;
octave:> bar(X, Y1);
Now let's put in the other two constraint inequalities.
octave:> X = 0.1:0.05:40;
octave:> Y1 = (15000.  120*X)/210;
octave:> Y2 = max((4000  110.*X)./30, 0);
octave:> Y3 = max(75  X, 0);
octave:> Ytop = min([Y1; Y2; Y3]);
octave:> bar(X, Ytop);
The red area is the solution space that holds valid solutions. This means that any point in this area fulfils the
constraints.
Now let's superimpose on top of this picture the objective function P.
octave:> hold on
octave:> X=15:25:40;
octave:> title('Solution space and objective')
octave:> plot(X,(6315.63143.0*X)/60.0);
octave:> hold off
The line gives a picture of the objective function.
All solutions that intersect with the red area are valid solutions, meaning that this result also fulfils
the set constraints. The more the line goes to the right, the higher the objective value is. The optimal solution
or best objective is a line that is still in the red area, but with an as large as possible value.
It seems apparent that the maximum value of P will occur on the level curve (that is, level
line) that passes through the vertex of the polygon that lies near (22,53).
It is the intersection of x + y = 75 and 110*x + 30*y = 4000
This is a corner point of the diagram. This is not a coincidence. The simplex algorithm, which is used
by lp_solve, starts from a theorem that the optimal solution is such a corner point.
In fact we can compute the result:
octave:> x = [1 1; 110 30] \ [75; 4000]
x =
21.875
53.125
The acreage that results in the maximum profit is 21.875 for wheat and 53.125 for barley.
In that case the profit is:
octave:> P = [143 60] * x
P = 6315.6
That is, $6315.6.
Note that these command are in script example3.m
Now, lp_solve comes into the picture to solve this linear programming problem more generally.
After that we will use it to solve two more complicated problems involving more variables
and constraints.
For this example, we use the higherlevel script lp_maker to build the model and then some lp_solve API calls
to retrieve the solution. Here is again the usage of lp_maker:
LP_MAKER Makes mixed integer linear programming problems.
SYNOPSIS: lp_handle = lp_maker(f,a,b,e,vlb,vub,xint,scalemode,setminim)
make the MILP problem
max v = f'*x
a*x <> b
x >= vlb >= 0
x <= vub
x(int) are integer
ARGUMENTS: The first four arguments are required:
f: n vector of coefficients for a linear objective function.
a: m by n matrix representing linear constraints.
b: m vector of right sides for the inequality constraints.
e: m vector that determines the sense of the inequalities:
e(i) < 0 ==> Less Than
e(i) = 0 ==> Equals
e(i) > 0 ==> Greater Than
vlb: n vector of nonnegative lower bounds. If empty or omitted,
then the lower bounds are set to zero.
vub: n vector of upper bounds. May be omitted or empty.
xint: vector of integer variables. May be omitted or empty.
scalemode: scale flag. Off when 0 or omitted.
setminim: Set maximum lp when this flag equals 0 or omitted.
OUTPUT: lp_handle is an integer handle to the lp created.
Now let's formulate this model with lp_solve:
octave:> f = [143 60];
octave:> A = [120 210; 110 30; 1 1];
octave:> b = [15000; 4000; 75];
octave:> lp = lp_maker(f, A, b, [1; 1; 1], [], [], [], 1, 0);
octave:> solvestat = octlpsolve('solve', lp)
solvestat = 0
octave:> obj = octlpsolve('get_objective', lp)
obj = 6315.6
octave:> x = octlpsolve('get_variables', lp)
x =
21.875
53.125
octave:> octlpsolve('delete_lp', lp);
Note that these command are in script example4.m
With the higherlevel script lp_maker, we provide all data to lp_solve. lp_solve returns a handle (lp) to the
created model. Then the API call 'solve' is used to calculate the optimal solution of the model.
The value of the objective function is retrieved via the API call 'get_objective' and the values of the variables
are retrieved via the API call 'get_variables'. At last, the model is removed from memory via a call to 'delete_lp'.
Don't forget this to free all memory allocated by lp_solve.
The solution is the same answer we obtained before.
Note that the nonnegativity constraints are accounted implicitly because variables are by default nonnegative
in lp_solve.
Well, we could have done this problem by hand (as shown in the introduction) because it is very small and it
can be graphically presented.
Now suppose that the farmer is dealing with a third crop, say corn, and that the corresponding data is:
cost per acre  $150.75 
yield per acre  125 bushels 
profit per bushel  $1.56 
With three variables it is already a lot more difficult to show this model graphically. Adding more variables
makes it even impossible because we can't imagine anymore how to represent this. We only have a practical understanding
of 3 dimentions, but beyound that it is all very theorethical.
If we denote the number of acres allotted to corn by z, then the objective function becomes:
P = (110)(1.30)x + (30)(2.00)y + (125)(1.56) = 143x + 60y + 195z
And the constraint inequalities are:
120x + 210y + 150.75z <= 15000
110x + 30y + 125z <= 4000
x + y + z <= 75
x >= 0, y >= 0, z >= 0
The problem is solved with lp_solve as follows:
octave:> f = [143 60 195];
octave:> A = [120 210 150.75; 110 30 125; 1 1 1];
octave:> b = [15000; 4000; 75];
octave:> lp = lp_maker(f, A, b, [1; 1; 1], [], [], [], 1, 0);
octave:> solvestat = octlpsolve('solve', lp)
solvestat = 0
octave:> obj = octlpsolve('get_objective', lp)
obj = 6986.8
octave:> x = octlpsolve('get_variables', lp)
x =
0.00000
56.57895
18.42105
octave:> octlpsolve('delete_lp', lp);
Note that these command are in script example5.m
So the farmer should ditch the wheat and plant 56.5789 acres of barley and 18.4211 acres of corn.
There is no practical limit on the number of variables and constraints that Octave can handle.
Certainly none that the relatively unsophisticated user will encounter. Indeed, in
many true applications of the technique of linear programming, one needs
to deal with many variables and constraints. The solution of such
a problem by hand is not feasible, and software like Octave is crucial
to success. For example, in the farming problem with which we
have been working, one could have more crops than two or three. Think
agribusiness instead of family farmer. And one could have constraints
that arise from other things beside expenses, storage and acreage limitations. For example:
 Availability of seed. This might lead to constraint inequalities like xj < k.
 Personal preferences. Thus the farmer's spouse might have a preference
for one variety over another and insist on a corresponding planting,
or something similar with a collection of crops; thus constraint inequalities
like xi < xj or x1 + x2 > x3.
 Government subsidies. It may take a moment's reflection on the reader's part,
but this could lead to inequalities like xj > k.
Below is a sequence of commands that solves exactly such a problem.
You should be able to recognize the objective expression and the constraints from the data that is entered.
But as an aid, you might answer the following questions:
 How many crops are under consideration?
 What are the corresponding expenses? How much is available for expenses?
 What are the yields in each case? What is the storage capacity?
 How many acres are available?
 What crops are constrained by seed limitations? To what extent?
 What about preferences?
 What are the minimum acreages for each crop?
octave:> f = [110*1.3 30*2.0 125*1.56 75*1.8 95*.95 100*2.25 50*1.35];
octave:> A = [120 210 150.75 115 186 140 85;
110 30 125 75 95 100 50;
1 1 1 1 1 1 1;
1 1 0 0 0 0 0;
0 0 1 0 2 0 0;
0 0 0 1 0 1 1];
octave:> b = [55000;40000;400;0;0;0];
octave:> lp = lp_maker(f, A, b, [1; 1; 1; 1; 1; 1], [10 10 10 10 20 20 20], [100 Inf 50 Inf Inf 250 Inf], [], 1, 0);
octave:> solvestat = octlpsolve('solve', lp)
solvestat = 0
octave:> obj = octlpsolve('get_objective', lp)
obj = 7.5398e+04
octave:> x = octlpsolve('get_variables', lp)
x =
10.000
10.000
40.000
45.652
20.000
250.000
20.000
octave:> octlpsolve('delete_lp', lp);
Note that these command are in script example6.m
Note that we have used in this formulation the vlb and vub arguments of lp_maker. This to set lower and upper bounds
on variables. This could have been done via extra constraints, but it is more performant to set bounds on variables.
Also note that Inf is used for variables that have no upper limit. This stands for Infinity.
Note that despite the complexity of the problem, lp_solve solves it almost instantaneously. It seems the
farmer should bet the farm on crop number 6. We strongly suggest
you alter the expense and/or the storage limit in the problem and see
what effect that has on the answer.
Another, more theoretical, example
Suppose we want to solve the following linear program using Octave:
max 4x1 + 2x2 + x3
s. t. 2x1 + x2 <= 1
x1 + 2x3 <= 2
x1 + x2 + x3 = 1
x1 >= 0
x1 <= 1
x2 >= 0
x2 <= 1
x3 >= 0
x3 <= 2
Convert the LP into Octave format we get:
f = [4 2 1]
A = [2 1 0; 1 0 2; 1 1 1]
b = [1; 2; 1]
Note that constraints on single variables are not put in the constraint matrix.
lp_solve can set bounds on individual variables and this is more performant than creating
additional constraints. These bounds are:
l = [ 0 0 0]
u = [ 1 1 2]
Now lets enter this in Octave:
octave:> f = [4 2 1];
octave:> A = [2 1 0; 1 0 2; 1 1 1];
octave:> b = [1; 2; 1];
octave:> l = [ 0 0 0];
octave:> u = [ 1 1 2];
Now solve the linear program using Octave: Type the commands
octave:> lp = lp_maker(f, A, b, [1; 1; 1], l, u, [], 1, 0);
octave:> solvestat = octlpsolve('solve', lp)
solvestat = 0
octave:> obj = octlpsolve('get_objective', lp)
obj = 2.5000
octave:> x = octlpsolve('get_variables', lp)
x =
0.50000
0.00000
0.50000
octave:> octlpsolve('delete_lp', lp)
What to do when some of the variables are missing ?
For example, suppose there are no lower bounds on the variables. In this case define l to be the empty set using the Octave command:
octave:> l = [];
This has the same effect as before, because lp_solve has as default lower bound for variables 0.
But what if you want that variables may also become negative?
Then you can use Inf as lower bounds:
octave:> l = [Inf Inf Inf];
Solve this and you get a different result:
octave:> lp = lp_maker(f, A, b, [1; 1; 1], l, u, [], 1, 0);
octave:> solvestat = octlpsolve('solve', lp)
solvestat = 0
octave:> obj = octlpsolve('get_objective', lp)
obj = 2.6667
octave:> x = octlpsolve('get_variables', lp)
x =
0.66667
0.33333
0.66667
octave:> octlpsolve('delete_lp', lp)
Overview of API routines
Note again that the Octave command 'help octlpsolve.m' gives an overview of all functions that can be called via octlpsolve with their arguments and return values.

add_column, add_columnex
 return = octlpsolve('add_column', lp_handle, [column])
 return = octlpsolve('add_columnex', lp_handle, [column])
 Both have the same interface from add_column but act as add_columnex

add_constraint, add_constraintex
 return = octlpsolve('add_constraint', lp_handle, [row], constr_type, rh)
 return = octlpsolve('add_constraintex', lp_handle, [row], constr_type, rh)
 Both have the same interface from add_constraint but act as add_constraintex

add_SOS
 return = octlpsolve('add_SOS', lp_handle, name, sostype, priority, [sosvars], [weights])
 The count argument in the API documentation is not needed in Octave since the number of elements is derived from the size of the sosvars and weights matrices. These must have the same size.

column_in_lp
 return = octlpsolve('column_in_lp', lp_handle, [column])
 No special considerations.

default_basis
 octlpsolve('default_basis', lp_handle)
 No special considerations.

del_column
 return = octlpsolve('del_column', lp_handle, column)
 No special considerations.

del_constraint
 return = octlpsolve('del_constraint', lp_handle, del_row)
 No special considerations.

delete_lp
 octlpsolve('delete_lp', lp_handle)
 No special considerations.

free_lp
 octlpsolve('free_lp', lp_handle)
 lp_handle is not changed as in the lpsolve API since it is a read_only input parameter. So it acts the same as delete_lp.

get_anti_degen
 return = octlpsolve('get_anti_degen', lp_handle)
 No special considerations.

get_basis
 [bascolumn] = octlpsolve('get_basis', lp_handle {, nonbasic})
 The bascolumn argument in the API documentation is here the return value. The nonbasic argument is optional in Octave. If not provided, then 0 is used.

get_basiscrash
 return = octlpsolve('get_basiscrash', lp_handle)
 No special considerations.

get_bb_depthlimit
 return = octlpsolve('get_bb_depthlimit', lp_handle)
 No special considerations.

get_bb_floorfirst
 return = octlpsolve('get_bb_floorfirst', lp_handle)
 No special considerations.

get_bb_rule
 return = octlpsolve('get_bb_rule', lp_handle)
 No special considerations.

get_bounds_tighter
 return = octlpsolve('get_bounds_tighter', lp_handle)
 No special considerations.

get_break_at_value
 return = octlpsolve('get_break_at_value', lp_handle)
 No special considerations.

get_col_name
 name = octlpsolve('get_col_name', lp_handle, column)
 [names] = octlpsolve('get_col_name', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_column
 [column, return] = octlpsolve('get_column', lp_handle, col_nr)
 The column argument in the API documentation is here the first return value.
 The return code of the call is the second return value.

get_constr_type
 return = octlpsolve('get_constr_type', lp_handle, row)
 [constr_type] = octlpsolve('get_constr_type', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_constraints
 [constr, return] = octlpsolve('get_constraints', lp_handle)
 The constr argument in the API documentation is here the first return value.
 The return code of the call is the second return value.

get_dual_solution
 [duals, return] = octlpsolve('get_dual_solution', lp_handle)
 The duals argument in the API documentation is here the first return value.
 In the API, element 0 is not used and values start from element 1. In Octave, there is no unused element in the matrix.
 The return code of the call is the second return value.

get_epsb
 return = octlpsolve('get_epsb', lp_handle)
 No special considerations.

get_epsd
 return = octlpsolve('get_epsd', lp_handle)
 No special considerations.

get_epsel
 return = octlpsolve('get_epsel', lp_handle)
 No special considerations.

get_epsint
 return = octlpsolve('get_epsint', lp_handle)
 No special considerations.

get_epsperturb
 return = octlpsolve('get_epsperturb', lp_handle)
 No special considerations.

get_epspivot
 return = octlpsolve('get_epspivot', lp_handle)
 No special considerations.

get_improve
 return = octlpsolve('get_improve', lp_handle)
 No special considerations.

get_infinite
 return = octlpsolve('get_infinite', lp_handle)
 No special considerations.

get_lowbo
 return = octlpsolve('get_lowbo', lp_handle, column)
 [return] = octlpsolve('get_lowbo', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_lp_index
 return = octlpsolve('get_lp_index', lp_handle, orig_index)
 No special considerations.

get_lp_name
 name = octlpsolve('get_lp_name', lp_handle)
 No special considerations.

get_mat
 value = octlpsolve('get_mat', lp_handle, row, col)
 [matrix, return] = octlpsolve('get_mat', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix in the first return value.
The return code of the call is the second return value.

get_max_level
 return = octlpsolve('get_max_level', lp_handle)
 No special considerations.

get_maxpivot
 return = octlpsolve('get_maxpivot', lp_handle)
 No special considerations.

get_mip_gap
 return = octlpsolve('get_mip_gap', lp_handle, absolute)
 No special considerations.

get_nameindex
 return = octlpsolve('get_nameindex', lp_handle, name, isrow)
 No special considerations.

get_Ncolumns
 return = octlpsolve('get_Ncolumns', lp_handle)
 No special considerations.

get_negrange
 return = octlpsolve('get_negrange', lp_handle)
 No special considerations.

get_nonzeros
 return = octlpsolve('get_nonzeros', lp_handle)
 No special considerations.

get_Norig_columns
 return = octlpsolve('get_Norig_columns', lp_handle)
 No special considerations.

get_Norig_rows
 return = octlpsolve('get_Norig_rows', lp_handle)
 No special considerations.

get_Nrows
 return = octlpsolve('get_Nrows', lp_handle)
 No special considerations.

get_obj_bound
 return = octlpsolve('get_obj_bound', lp_handle)
 No special considerations.

get_objective
 return = octlpsolve('get_objective', lp_handle)
 No special considerations.

get_orig_index
 return = octlpsolve('get_orig_index', lp_handle, lp_index)
 No special considerations.

get_origcol_name
 name = octlpsolve('get_origcol_name', lp_handle, column)
 [names] = octlpsolve('get_origcol_name', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_origrow_name
 name = octlpsolve('get_origrow_name', lp_handle, row)
 [names] = octlpsolve('get_origrow_name', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_pivoting
 return = octlpsolve('get_pivoting', lp_handle)
 No special considerations.

get_presolve
 return = octlpsolve('get_presolve', lp_handle)
 No special considerations.

get_primal_solution
 [pv, return] = octlpsolve('get_primal_solution', lp_handle)
 The pv argument in the API documentation is here the first return value.
 The return code of the call is the second return value.

get_print_sol
 return = octlpsolve('get_print_sol', lp_handle)
 No special considerations.

get_ptr_constraints

get_ptr_dualsolution

get_ptr_primal_solution

get_ptr_sensitivity_obj, get_ptr_sensitivity_objex

get_ptr_sensitivity_rhs

get_ptr_variables

get_rh
 return = octlpsolve('get_rh', lp_handle, row)
 [rh] = octlpsolve('get_rh', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix. Note that in this case, the value of row 0 is not returned.

get_rh_range
 return = octlpsolve('get_rh_range', lp_handle, row)
 [rh_ranges] = octlpsolve('get_rh_range', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_row
 [row, return] = octlpsolve('get_row', lp_handle, row_nr)
 The row argument in the API documentation is here the first return value.
 In the API, element 0 is not used and values start from element 1. In Octave, there is no unused element in the matrix.
 The return code of the call is the second return value.

get_row_name
 name = octlpsolve('get_row_name', lp_handle, row)
 [names] = octlpsolve('get_row_name', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_scalelimit
 return = octlpsolve('get_scalelimit', lp_handle)
 No special considerations.

get_scaling
 return = octlpsolve('get_scaling', lp_handle)
 No special considerations.

get_sensitivity_obj, get_sensitivity_objex
 [objfrom, objtill, objfromvalue, objtillvalue, return] = octlpsolve('get_sensitivity_obj', lp_handle)
 [objfrom, objtill, objfromvalue, objtillvalue, return] = octlpsolve('get_sensitivity_objex', lp_handle)
 The objfrom, objtill, objfromvalue, objtillvalue arguments in the API documentation are here the return values. Note that Octave allows the return of fewer variables. For example if only objfrom and objtill are needed then the call can be [objfrom, objtill] = octlpsolve('get_sensitivity_obj', lp_handle). The unrequested values are even not calculated.
 Since the API routine doesn't calculate the objtillvalue value at this time, Octave always returns a zero vector for this.
 The return code of the call is the last value.
 get_sensitivity_obj and get_sensitivity_objex are both implemented, but have the same functionality.

get_sensitivity_rhs, get_sensitivity_rhsex
 [duals, dualsfrom, dualstill, return] = octlpsolve('get_sensitivity_rhs', lp_handle)
 [duals, dualsfrom, dualstill, return] = octlpsolve('get_sensitivity_rhsex', lp_handle)
 The duals, dualsfrom, dualstill
arguments in the API documentation are here the return values. Note that
Octave allows the return of fewer variables. For example if only duals is
needed then the call can be [duals] = octlpsolve('get_sensitivity_rhs', lp_handle). The unrequested values are even not calculated.
 The return code of the call is the last value.
 get_sensitivity_rhs and get_sensitivity_rhsex are both implemented, but have the same functionality.

get_simplextype
 return = octlpsolve('get_simplextype', lp_handle)
 No special considerations.

get_solutioncount
 return = octlpsolve('get_solutioncount', lp_handle)
 No special considerations.

get_solutionlimit
 return = octlpsolve('get_solutionlimit', lp_handle)
 No special considerations.

get_status
 return = octlpsolve('get_status', lp_handle)
 No special considerations.

get_statustext
 return = octlpsolve('get_statustext', lp_handle, statuscode)
 No special considerations.

get_timeout
 return = octlpsolve('get_timeout', lp_handle)
 No special considerations.

get_total_iter
 return = octlpsolve('get_total_iter', lp_handle)
 No special considerations.

get_total_nodes
 return = octlpsolve('get_total_nodes', lp_handle)
 No special considerations.

get_upbo
 return = octlpsolve('get_upbo', lp_handle, column)
 [upbo] = octlpsolve('get_upbo', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_var_branch
 return = octlpsolve('get_var_branch', lp_handle, column)
 [var_branch] = octlpsolve('get_var_branch', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_var_dualresult
 return = octlpsolve('get_var_dualresult', lp_handle, index)
 No special considerations.

get_var_primalresult
 return = octlpsolve('get_var_primalresult', lp_handle, index)
 No special considerations.

get_var_priority
 return = octlpsolve('get_var_priority', lp_handle, column)
 [var_priority] = octlpsolve('get_var_priority', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

get_variables
 [var, return] = octlpsolve('get_variables', lp_handle)
 The var argument in the API documentation is here the first return value.
 The return code of the call is the second return value.

get_verbose
 return = octlpsolve('get_verbose', lp_handle)
 No special considerations.

get_working_objective
 return = octlpsolve('get_working_objective', lp_handle)
 No special considerations.

has_BFP
 return = octlpsolve('has_BFP', lp_handle)
 No special considerations.

has_XLI
 return = octlpsolve('has_XLI', lp_handle)
 No special considerations.

is_add_rowmode
 return = octlpsolve('is_add_rowmode', lp_handle)
 No special considerations.

is_anti_degen
 return = octlpsolve('is_anti_degen', lp_handle, testmask)
 No special considerations.

is_binary
 return = octlpsolve('is_binary', lp_handle, column)
 [binary] = octlpsolve('is_binary', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

is_break_at_first
 return = octlpsolve('is_break_at_first', lp_handle)
 No special considerations.

is_constr_type
 return = octlpsolve('is_constr_type', lp_handle, row, mask)
 No special considerations.

is_debug
 return = octlpsolve('is_debug', lp_handle)
 No special considerations.

is_feasible
 return = octlpsolve('is_feasible', lp_handle, [values] {, threshold})
 The threshold argument is optional.
When not provided, the value of get_epsint will be taken.

is_free
 return = octlpsolve('is_free', lp_handle, column)
 [free] = octlpsolve('is_free', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

is_infinite
 return = octlpsolve('is_infinite', lp_handle, value)
 No special considerations.

is_int
 return = octlpsolve('is_int', lp_handle, column)
 [int] = octlpsolve('is_int', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

is_integerscaling
 return = octlpsolve('is_integerscaling', lp_handle)
 No special considerations.

is_maxim
 return = octlpsolve('is_maxim', lp_handle)
 No special considerations.

is_nativeBFP
 return = octlpsolve('is_nativeBFP', lp_handle)
 No special considerations.

is_nativeXLI
 return = octlpsolve('is_nativeXLI', lp_handle)
 No special considerations.

is_negative
 return = octlpsolve('is_negative', lp_handle, column)
 [negative] = octlpsolve('is_negative', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

is_piv_mode
 return = octlpsolve('is_piv_mode', lp_handle, testmask)
 No special considerations.

is_piv_rule
 return = octlpsolve('is_piv_rule', lp_handle, rule)
 No special considerations.

is_presolve
 return = octlpsolve('is_presolve', lp_handle, testmask)
 No special considerations.

is_scalemode
 return = octlpsolve('is_scalemode', lp_handle, testmask)
 No special considerations.

is_scaletype
 return = octlpsolve('is_scaletype', lp_handle, scaletype)
 No special considerations.

is_semicont
 return = octlpsolve('is_semicont', lp_handle, column)
 [semicont] = octlpsolve('is_semicont', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

is_SOS_var
 return = octlpsolve('is_SOS_var', lp_handle, column)
 [SOS_var] = octlpsolve('is_SOS_var', lp_handle)
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into an Octave matrix.

is_trace
 return = octlpsolve('is_trace', lp_handle)
 No special considerations.

lp_solve_version
 versionstring = octlpsolve('lp_solve_version')
 The octlpsolve API routine returns the version information in 4 provided argument variables while the Octave version returns the information as a string in the format major.minor.release.build

make_lp
 lp_handle = octlpsolve('make_lp', rows, columns)
 lp_handle is not a pointer to an lprec structure as in the API, but an incrementing handle number starting from 0.

print_constraints
 octlpsolve('print_constraints', lp_handle {, columns})
 columns is optional. If not specified, then 1 is used.
 First call set_outputfile to specify where the
information is written to. In the API documentation it is written that by
default, the output goes to stdout, but under Octave (Windows) this means
that the output is not shown.
 The same information can also be obtained via octlpsolve('get_constraints', lp_handle). This shows the result on screen.

print_debugdump
 return = octlpsolve('print_debugdump', lp_handle, filename)
 No special considerations.

print_duals
 octlpsolve('print_duals', lp_handle)
 First call set_outputfile to specify where the
information is written to. In the API documentation it is written that by
default, the output goes to stdout, but under Octave (Windows) this means
that the output is not shown.
 The same information can be obtained via octlpsolve('get_dual_solution', lp_handle). This shows the result on screen.

print_lp
 octlpsolve('print_lp', lp_handle)
 First call set_outputfile to specify where the information is written to.
In the API documentation it is written that by default, the output goes to stdout, but under Octave (Windows) this means that the output is not shown.

print_objective
 octlpsolve('print_objective', lp_handle)
 First call set_outputfile to specify where the
information is written to. In the API documentation it is written that by
default, the output goes to stdout, but under Octave (Windows) this means
that the output is not shown.
 The same information can be obtained via octlpsolve('get_objective', lp_handle). This shows the result on screen.

print_scales
 octlpsolve('print_scales', lp_handle)
 First call set_outputfile to specify where the information is written to.
In the API documentation it is written that by default, the output goes to stdout, but under Octave (Windows) this means that the output is not shown.

print_solution
 octlpsolve('print_solution', lp_handle {, columns})
 columns is optional. If not specified, then 1 is used.
 First call set_outputfile to specify where the
information is written to. In the API documentation it is written that by
default, the output goes to stdout, but under Octave (Windows) this means
that the output is not shown.
 The same information can also be obtained via octlpsolve('get_variables', lp_handle). This shows the result on screen.

print_str
 octlpsolve('print_str', lp_handle, str)
 First call set_outputfile to specify where the information is written to.
In the API documentation it is written that by default, the output goes to stdout, but under Octave (Windows) this means that the output is not shown.

print_tableau
 octlpsolve('print_tableau', lp_handle)
 First call set_outputfile to specify where the information is written to.
In the API documentation it is written that by default, the output goes to stdout, but under Octave (Windows) this means that the output is not shown.

put_abortfunc

put_logfunc
 Not implemented.
 However, the octlpsolve driver sets a log function to redirect the output of lpsolve from stdout (which is not visible in Windows Octave) to the command window of Octave.
As such, all reported output can be seen in Octave. How much output is seen is controlled by the verbose level that can be defined by set_verbose or can be specified in the read_ routines.

put_msgfunc

read_basis
 [ret, info] = octlpsolve('read_basis', lp_handle, filename)
 No special considerations.

read_freemps, read_freeMPS
 lp_handle = octlpsolve('read_freemps', filename {, verbose})
 lp_handle = octlpsolve('read_freeMPS', filename {, verbose})
 In the lpsolve API, read_freemps needs a FILE
handle. In Octave it needs the filename and thus acts the same as
read_freeMPS.
 lp_handle is not a pointer to an lprec structure as
in the API, but an incrementing handle number starting from 0.
 verbose is optional. If not specified, then NORMAL is used.

read_lp, read_LP
 lp_handle = octlpsolve('read_lp', filename {, verbose {, lp_name}})
 lp_handle = octlpsolve('read_LP', filename {, verbose {, lp_name}})
 In the lpsolve API, read_lp needs a FILE handle. In
Octave it needs the filename and thus acts the same as read_LP.
 lp_handle is not a pointer to an lprec structure as
in the API, but an incrementing handle number starting from 0.
 verbose is optional. If not provided then NORMAL is used.
 lp_name is optional. If not provided then no name is given to the model ('').

read_mps, read_MPS
 lp_handle = octlpsolve('read_mps', filename {, verbose})
 lp_handle = octlpsolve('read_MPS', filename {, verbose})
 In the lpsolve API, read_mps needs a FILE handle.
In Octave it needs the filename and thus acts the same as read_MPS.
 lp_handle is not a pointer to an lprec structure as
in the API, but an incrementing handle number starting from 0.
 verbose is optional. If not specified, then NORMAL is used.

read_XLI
 lp_handle = octlpsolve('read_XLI', xliname, modelname {, dataname {, options {, verbose}}}
 lp_handle is not a pointer to an lprec structure as in the API, but an incrementing handle number starting from 0.
 dataname is optional. When not provided, '' (NULL) is taken. '' is taken as NULL.
 options is optional. When not provided, '' is taken.
 verbose is optional. If not specified, then NORMAL is used.

reset_basis

set_add_rowmode
 return = octlpsolve('set_add_rowmode', lp_handle, turnon)
 No special considerations.

set_anti_degen
 octlpsolve('set_anti_degen', lp_handle, anti_degen)
 No special considerations.

set_basis
 return = octlpsolve('set_basis', lp_handle, [bascolumn], nonbasic)
 In the API, element 0 of bascolumn is not used and values start from element 1. In Octave, there is no unused element in the matrix.

set_basiscrash
 octlpsolve('set_basiscrash', lp_handle, mode)
 No special considerations.

set_bb_depthlimit
 octlpsolve('set_bb_depthlimit', lp_handle, bb_maxlevel)
 No special considerations.

set_bb_floorfirst
 octlpsolve('set_bb_floorfirst', lp_handle, bb_floorfirst)
 No special considerations.

set_bb_rule
 octlpsolve('set_bb_rule', lp_handle, bb_rule)
 No special considerations.

set_BFP
 return = octlpsolve('set_BFP', lp_handle, filename)
 No special considerations.

set_binary
 return = octlpsolve('set_binary', lp_handle, column, must_be_bin)
 return = octlpsolve('set_binary', lp_handle, [must_be_bin])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_bounds
 return = octlpsolve('set_bounds', lp_handle, column, lower, upper)
 return = octlpsolve('set_bounds', lp_handle, [lower], [upper])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_bounds_tighter
 octlpsolve('set_bounds_tighter', lp_handle, tighten)
 No special considerations.

set_break_at_first
 octlpsolve('set_break_at_first', lp_handle, break_at_first)
 No special considerations.

set_break_at_value
 octlpsolve('set_break_at_value', lp_handle, break_at_value)
 No special considerations.

set_col_name
 return = octlpsolve('set_col_name', lp_handle, column, name)
 return = octlpsolve('set_col_name', lp_handle, [names])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_column, set_columnex
 return = octlpsolve('set_column', lp_handle, col_no, [column])
 return = octlpsolve('set_columnex', lp_handle, col_no, [column])
 Both have the same interface from set_column but act as set_columnex

set_constr_type
 return = octlpsolve('set_constr_type', lp_handle, row, con_type)
 return = octlpsolve('set_constr_type', lp_handle, [con_type])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all rows.

set_debug
 octlpsolve('set_debug', lp_handle, debug)
 No special considerations.

set_epsb
 octlpsolve('set_epsb', lp_handle, epsb)
 No special considerations.

set_epsd
 octlpsolve('set_epsd', lp_handle, epsd)
 No special considerations.

set_epsel
 octlpsolve('set_epsel', lp_handle, epsel)
 No special considerations.

set_epsint
 octlpsolve('set_epsint', lp_handle, epsint)
 No special considerations.

set_epsperturb
 octlpsolve('set_epsperturb', lp_handle, epsperturb)
 No special considerations.

set_epspivot
 octlpsolve('set_epspivot', lp_handle, epspivot)
 No special considerations.

set_free
 return = octlpsolve('set_free', lp_handle, column)
 No special considerations.

set_improve
 octlpsolve('set_improve', lp_handle, improve)
 No special considerations.

set_infinite
 octlpsolve('set_infinite', lp_handle, infinite)
 No special considerations.

set_int
 return = octlpsolve('set_int', lp_handle, column, must_be_int)
 return = octlpsolve('set_int', lp_handle, [must_be_int])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_lowbo
 return = octlpsolve('set_lowbo', lp_handle, column, value)
 return = octlpsolve('set_lowbo', lp_handle, [values])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_lp_name
 return = octlpsolve('set_lp_name', lp_handle, name)
 No special considerations.

set_mat
 return = octlpsolve('set_mat', lp_handle, row, column, value)
 return = octlpsolve('set_mat', lp_handle, [matrix])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows to set the whole matrix (all rows/columns) at once.
This is the most performant way to provide the constraint matrix.
The matrix must be twodimentional.

set_maxim
 octlpsolve('set_maxim', lp_handle)
 No special considerations.

set_maxpivot
 octlpsolve('set_maxpivot', max_num_inv)
 No special considerations.

set_minim
 octlpsolve('set_minim', lp_handle)
 No special considerations.

set_mip_gap
 octlpsolve('set_mip_gap', lp_handle, absolute, mip_gap)
 No special considerations.

set_negrange
 octlpsolve('set_negrange', negrange)
 No special considerations.

set_obj
 return = octlpsolve('set_obj', lp_handle, column, value)
 return = octlpsolve('set_obj', lp_handle, [values])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables. It is then the same as set_obj_fn

set_obj_bound
 octlpsolve('set_obj_bound', lp_handle, obj_bound)
 No special considerations.

set_obj_fn, set_obj_fnex
 return = octlpsolve('set_obj_fn', lp_handle, [row])
 return = octlpsolve('set_obj_fnex', lp_handle, [row])
 Both have the same interface from set_obj_fn but act as set_obj_fnex
 In the API, element 0 is not used and values start from element 1. In Octave, there is no unused element in the matrix.

set_outputfile
 return = octlpsolve('set_outputfile', lp_handle, filename)
 In the API description it says that setting filename to NULL results in writing output back to stdout.
In Octave under Windows, output to stdout it not shown. However it results in closing the file.
Use '' to have the effect of NULL.

set_outputstream

set_pivoting
 octlpsolve('set_pivoting', lp_handle, pivoting)
 No special considerations.

set_preferdual
 octlpsolve('set_preferdual', lp_handle, dodual)
 No special considerations.

set_presolve
 octlpsolve('set_presolve', lp_handle, do_presolve)
 No special considerations.

set_print_sol
 octlpsolve('set_print_sol', lp_handle, print_sol)
 No special considerations.

set_rh
 return = octlpsolve('set_rh', lp_handle, row, value)
 return = octlpsolve('set_rh', lp_handle, [values])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all rows. Note that in this case, the value of row 0 is not specified in the matrix.

set_rh_range
 return = octlpsolve('set_rh_range', lp_handle, row, deltavalue)
 return = octlpsolve('set_rh_range', lp_handle, [deltavalues])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all rows.

set_rh_vec
 octlpsolve('set_rh_vec', lp_handle, [rh])
 In the API, element 0 is not used and values start from element 1. In Octave, there is no unused element in the matrix.

set_row, set_rowex
 return = octlpsolve('set_row', lp_handle, row_no, [row])
 return = octlpsolve('set_rowex', lp_handle, row_no, [row])
 Both have the same interface from set_row but act as set_rowex
 In the API, element 0 is not used and values start from element 1. In Octave, there is no unused element in the matrix.

set_row_name
 return = octlpsolve('set_row_name', lp_handle, row, name)
 return = octlpsolve('set_row_name', lp_handle, [names])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all rows.

set_scalelimit
 octlpsolve('set_scalelimit', lp_handle, scalelimit)
 No special considerations.

set_scaling
 octlpsolve('set_scaling', lp_handle, scalemode)
 No special considerations.

set_semicont
 return = octlpsolve('set_semicont', lp_handle, column, must_be_sc)
 return = octlpsolve('set_semicont', lp_handle, [must_be_sc])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_sense
 octlpsolve('set_sense', lp_handle, maximize)
 No special considerations.

set_simplextype
 octlpsolve('set_simplextype', lp_handle, simplextype)
 No special considerations.

set_solutionlimit
 octlpsolve('set_solutionlimit', lp_handle, simplextype)
 No special considerations.

set_timeout
 octlpsolve('set_timeout', lp_handle, sectimeout)
 No special considerations.

set_trace
 octlpsolve('set_trace', lp_handle, trace)
 No special considerations.

set_upbo
 return = octlpsolve('set_upbo', lp_handle, column, value)
 return = octlpsolve('set_upbo', lp_handle, [values])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_var_branch
 return = octlpsolve('set_var_branch', lp_handle, column, branch_mode)
 return = octlpsolve('set_var_branch', lp_handle, [branch_mode])
 In Octave, this routine has two formats. The first format is identical to the API.
The second format allows setting a matrix of all variables.

set_var_weights
 return = octlpsolve('set_var_weights', lp_handle, [weights])
 No special considerations.

set_verbose
 octlpsolve('set_verbose', lp_handle, verbose)
 No special considerations.

set_XLI
 return = octlpsolve('set_XLI', lp_handle, filename)
 No special considerations.

solve
 result = octlpsolve('solve', lp_handle)
 No special considerations.

str_add_column

str_add_constraint

str_set_obj_fn

str_set_rh_vec

time_elapsed
 return = octlpsolve('time_elapsed', lp_handle)
 No special considerations.

unscale
 octlpsolve('unscale', lp_handle)
 No special considerations.

write_basis
 octlpsolve('write_basis', lp_handle, filename)
 No special considerations.

write_freemps, write_freeMPS
 return = octlpsolve('write_freemps', lp_handle, filename)
 return = octlpsolve('write_freeMPS', lp_handle, filename)
 In the lpsolve API, write_freeMPS needs a FILE handle. In Octave it needs the filename and thus acts the same as write_freemps.

write_lp, write_LP
 return = octlpsolve('write_lp', lp_handle, filename)
 return = octlpsolve('write_LP', lp_handle, filename)
 In the lpsolve API, write_LP needs a FILE handle. In Octave it needs the filename and thus acts the same as write_lp.

write_mps, write_MPS
 return = octlpsolve('write_mps', lp_handle, filename)
 return = octlpsolve('write_MPS', lp_handle, filename)
 In the lpsolve API, write_MPS needs a FILE handle.
In Octave it needs the filename and thus acts the same as write_mps.
 No special considerations.

write_XLI
 return = octlpsolve('write_XLI', lp_handle, filename {, options {, results}})
 No special considerations.
Extra Octave routines
These routines are not part of the lpsolve API, but are added for backwards compatibility.
Most of them exist in the lpsolve API with another name.
 [names] = octlpsolve('get_col_names', lp_handle)
 The same as get_col_name. Implemented for backwards compatibility.
 [constr_type] = octlpsolve('get_constr_types', lp_handle)
 The same as get_constr_type. Implemented for backwards compatibility.
 [int] = octlpsolve('get_int', lp_handle)
 The same as is_int. Implemented for backwards compatibility.
 return = octlpsolve('get_no_cols', lp_handle)
 The same as get_Ncolumns. Implemented for backwards compatibility.
 return = octlpsolve('get_no_rows', lp_handle)
 The same as get_Nrows. Implemented for backwards compatibility.
 name = octlpsolve('get_objective_name', lp_handle)
 The same as get_row_name with row=0. Implemented for backwards compatibility.
 [row_vec, return] = octlpsolve('get_obj_fn', lp_handle)
[row_vec, return] = octlpsolve('get_obj_fun', lp_handle)
 The same as get_row with row 0. Implemented for backwards compatibility.
 name = octlpsolve('get_problem_name', lp_handle)
 The same as get_lp_name. Implemented for backwards compatibility.
 [costs] = octlpsolve('get_reduced_costs', lp_handle)
 The same as get_dual_solution. Implemented for backwards compatibility.
 [names] = octlpsolve('get_row_names', lp_handle)
 The same as get_row_name. Implemented for backwards compatibility.
 [obj, x, duals, return] = octlpsolve('get_solution', lp_handle)
 Returns the value of the objective function, the
values of the variables and the duals. Implemented for backwards
compatibility.
 The return code of the call is the last value.
 value = octlpsolve('mat_elm', lp_handle)
 The same as get_mat. Implemented for backwards compatibility.
 [handle_vec] = octlpsolve('print_handle')
 Returns a vector with open handles.
Can be handy to see which handles aren't closed yet with delete_lp or free_lp.
 lp_handle = octlpsolve('read_lp_file', filename {, verbose {, lp_name}})
 The same as read_LP. Implemented for backwards compatibility.
Compile the octlpsolve driver
Windows
To compile the Octave driver under Windows, the cygwin environment is needed. The following document describes
what is needed for this: README.Windows. It may not
be needed to build the Octave sources to compile the binary file, but the proposed cygwin and octave packages
are needed to build a .oct file.
The build must be done from the cygwin prompt from the directory where the lpsolve octave files are located.
To make the compilation process easier, a script file can be used: ccc.win32
To make for release, just enter ./ccc.win32 and everything is build.
Unix/Linux
The build must be done from a shell prompt from the directory where the lpsolve octave files are located.
To make the compilation process easier, a script file can be used: ccc
To make for release, just enter sh ccc and everything is build.
All platforms
This compiles three source files: lpsolve.cpp, octave.cpp and hash.cpp
Then these are linked together to generate the octlpsolve.oct file.
The optional arguments to ccc are used for development. Source files can be provided
and then only these are compiled. For example hash.cpp should only be compiled
once while developing. So specifying lpsolve.cpp as first argument will only
compile this file and then link everything. This makes the build process a bit
faster. Also the option DDEMO can be added to add the demo command to test some
functionality of lpsolve. This is also only for debugging. Also the option
DDEBUG can be added. This to print some debug information while executing
octlpsolve. Should only be used for debugging purposes.
See also Using lpsolve from OMatrix,
Using lpsolve from MATLAB,
Using lpsolve from Scilab