Using lpsolve from Scilab
Scilab?
Developed at INRIA, Scilab has been developed for system control and signal
processing applications. It is freely distributed in source code format.
Scilab is made of three distinct parts: an interpreter, libraries of
functions (Scilab procedures) and libraries of Fortran and C routines. These
routines (which, strictly speaking, do not belong to Scilab but are
interactively called by the interpreter) are of independent interest and most of
them are available through Netlib. A few of them have been slightly modified for
better compatibility with Scilab's interpreter. A key feature of the Scilab
syntax is its ability to handle matrices: basic matrix manipulations such as
concatenation, extraction or transpose are immediately performed as well as
basic operations such as addition or multiplication. Scilab also aims at
handling more complex objects than numerical matrices. For instance, control
people may want to manipulate rational or polynomial transfer matrices. This is
done in Scilab by manipulating lists and typed lists which allows a natural
symbolic representation of complicated mathematical objects such as transfer
functions, linear systems or graphs.
Polynomials, polynomials matrices and transfer matrices are also defined and
the syntax used for manipulating these matrices is identical to that used for
manipulating constant vectors and matrices.
Scilab provides a variety of powerful primitives for the analysis of
non-linear systems. Integration of explicit and implicit dynamic systems can be
accomplished numerically. The scicos toolbox allows the graphic
definition and simulation of complex interconnected hybrid systems.
Scilab has an open programming environment where the creation of functions
and libraries of functions is completely in the hands of the user.
Functions are recognized as data
objects in Scilab and, thus, can be manipulated or created as other data
objects. For example, functions can be defined inside Scilab and passed as input
or output arguments of other functions.
In addition Scilab supports a character string data type which, in
particular, allows the on-line creation of functions. Matrices of character
strings are also manipulated with the same syntax as ordinary matrices.
Finally, Scilab is easily interfaced with Fortran or C subprograms. This
allows use of standardized packages and libraries in the interpreted environment
of Scilab.
The general philosophy of Scilab is to provide the following sort of
computing environment:
- To have data types which are varied and flexible with
a syntax which is natural and easy to use.
- To provide a reasonable set of primitives which serve
as a basis for a wide variety of calculations.
- To have an open programming environment where new primitives are easily
added. A useful tool distributed with Scilab is intersci which is a tool for building interface
programs to add new primitives i.e. to add new modules of Fortran or C code
into Scilab.
- To support library development through ``toolboxes'' of functions devoted to
specific applications (linear control, signal processing, network analysis,
non-linear control, etc.)
We will not discuss the specifics of Scilab here but instead refer the reader to the
Scilab website and
documentation.
Scilab and lpsolve
lpsolve is callable from Scilab via an external interface. As such, it looks like lpsolve is fully integrated
with Scilab. Matrices can directly be transferred between Scilab 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
Scilab (especially for matrix support). So you have full control to the complete lpsolve functionality via the sclpsolve
Scilab driver. If you find that this involves too much work to solve an lp model then you can also work via higher-level
scripts that can make things a lot easier. See further in this article.
Quickstart
Compile and build sclpsolve:
----------------------------
1. Under Windows, the Microsoft Visual C/C++ compiler must be installed
and the environment variables must be active do that when a command prompt
is opened, the cl and nmake commands can be executed.
Under Unix/Linux, the standard c compiler is used.
2. Edit the file Path.incl and change pathnames as needed.
3. Start scilab
4. Check under scilab that the current directory is the lpsolve directory.
Use the scilab pwd command to show the current directory.
With the chdir command, you can change the current directory.
This current directory must be lp_solve_5.1/extra/scilab/lpsolve
example: chdir('/lp_solve/lp_solve_5.1/extra/scilab/lpsolve')
5. To compile and build sclpsolve, enter the following command in scilab:
-->exec builder.sce
This should be done once to build the sclpsolve driver and to produce
the file loader.sce.
Load the sclpsolve driver in the scilab memory space:
-----------------------------------------------------
1. Under Windows, make sure that the lpsolve51.dll file is somewhere in the path
Under Unix/Linux, make sure that the liblpsolve51.so shared library is in /usr/lib
or /lib so that Unix can find it.
2. A precompiled library is provided for Windows (sclpsolve.dll).
For Unix/Linux, it is required that the sclpsolve driver is first build.
That must be done only once. So if you haven't taken the steps yet
to build the sclpsolve driver, then do this first as described at
the beginning of this file.
3. Start scilab
4. Check under scilab that the current directory is the lpsolve directory.
Use the scilab pwd command to show the current directory.
With the chdir command, you can change the current directory.
This current directory must be lp_solve_5.1/extra/scilab/lpsolve
example: chdir('/lp_solve/lp_solve_5.1/extra/scilab/lpsolve')
5. Enter the following command in scilab:
-->exec loader.sce
Installation
To make this possible, a driver program is needed: sclpsolve (sclpsolve.dll under Windows, sclpsolve.a under Unix/Linux).
This driver must be put in a directory known to Scilab and Scilab can call the sclpsolve 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 sclpsolve driver doesn't have to
be recompiled when an update of lpsolve is provided.
For Windows, the lpsolve51.dll file must be somewhere in the path.
For Unix, the lpsolve shared library (liblpsolve51.so) must be in the /usr/lib or /lib directory.
So note the difference between the Scilab lpsolve driver that is called sclpsolve and the lpsolve library that implements the
API that is called lpsolve51.
There are also some Scilab script files (*.sce, *.sci) as a quick start.
The first thing that must be done, each time Scilab is restarted and you want to use lpsolve is load
the sclpsolve driver into the Scilab workspace. This can be done via the script loader.sce.
The following command must be used to load the driver:
exec loader.sce
It is assumed here that the current directory is the Scilab lpsolve directory (lp_solve_5.1/extra/scilab/lpsolve),
but this is not a requirement. You can also provide the full path to the script files. The current directory can be
shown via the pwd command in Scilab:
pwd
That is basically all you need to do. From now on, you can use the library. This until Scilab is restarted.
Then this command must be given again to reload the library.
To make things easier, you can edit the file scilab.star with your favourite editor (or notepad/vi) in the Scilab directory
and add above line at the end of this file.
That will automatically load the lpsolve driver in memory when Scilab is started.
So it will appear as if the sclpsolve command is then always available.
If you get an error simular to below, then probably the lpsolve library can not be found:
link failed for dll c:\lp_solve\lp_solve_5.1\extra\scilab\lpsolve\libs\sclpsolve.dll
addinter(liblpmex,'lpmex_gateway','sclpsolve')
!--error 236
link: the shared archive was not loaded
Under Windows, the lpsolve51.dll file must be in one of the directories specified by the PATH environment variable.
This path can be seen in Scilab via the command getenv("PATH"). It is common to place dlls in the WINDOWS\system32 folder.
Under Unix/Linux, the liblpsolve51.so file must be in the directory /usr/lib or /lib.
To test if everything is installed correctly, enter sclpsolve() in the Scilab command window.
If it gives the following, then everything is ok:
sclpsolve scilab Interface version 5.1.0.0
using lpsolve version 5.1.1.2
Usage: [ret1, ret2, ...] = sclpsolve('functionname', arg1, arg2, ...)
All this is developed and tested with Scilab versions 2.7 and 3.0 both under Windows and Linux (RedHat).
Solve an lp model from Scilab via sclpsolve
In the following text, --> before the Scilab commands is the Scilab prompt.
Only the text after --> must be entered.
To call an lpsolve function, the following syntax must be used:
-->[ret1, ret2, ...] = sclpsolve('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 Scilab specific functions.
Most of the lpsolve API routines use or return an lprec structure. To make things more robust in Scilab, this structure
is replaced by a handle. This is an incrementing number starting from 0 and the lprec structures are maintained
internally by the sclpsolve 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 Scilab 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 Scilab, nothing must
be dimensioned in advance. The sclpsolve driver takes care of dimensioning all return variables and they are
always returned as return value of the call to sclpsolve. Never as argument to the routine. This can be a single
value as for get_objective (although Scilab 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 Scilab:
-->help sclpsolve
An example
(Note that you can execute this example by entering command per command as shown below or by just entering exec example1.sce.
This will execute example1.sce.)
-->lp=sclpsolve('make_lp', 0, 4);
-->sclpsolve('set_verbose', lp, 3);
-->sclpsolve('set_obj_fn', lp, [1, 3, 6.24, 0.1]);
-->sclpsolve('add_constraint', lp, [0, 78.26, 0, 2.9], 2, 92.3);
-->sclpsolve('add_constraint', lp, [0.24, 0, 11.31, 0], 1, 14.8);
-->sclpsolve('add_constraint', lp, [12.68, 0, 0.08, 0.9], 2, 4);
-->sclpsolve('set_lowbo', lp, 1, 28.6);
-->sclpsolve('set_lowbo', lp, 4, 18);
-->sclpsolve('set_upbo', lp, 4, 48.98);
-->sclpsolve('set_col_name', lp, 1, 'COLONE');
-->sclpsolve('set_col_name', lp, 2, 'COLTWO');
-->sclpsolve('set_col_name', lp, 3, 'COLTHREE');
-->sclpsolve('set_col_name', lp, 4, 'COLFOUR');
-->sclpsolve('set_row_name', lp, 1, 'THISROW');
-->sclpsolve('set_row_name', lp, 2, 'THATROW');
-->sclpsolve('set_row_name', lp, 3, 'LASTROW');
-->sclpsolve('write_lp', lp, 'a.lp');
-->sclpsolve('get_mat', lp, 1, 2)
ans =
78.26
-->sclpsolve('solve', lp)
ans =
0.
-->sclpsolve('get_objective', lp)
ans =
31.782759
-->sclpsolve('get_variables', lp)
ans =
! 28.6 !
! 0. !
! 0. !
! 31.827586 !
-->sclpsolve('get_constraints', lp)
ans =
! 92.3 !
! 6.864 !
! 391.29283 !
-->sclpsolve('delete_lp', lp);
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:
-->obj=sclpsolve('get_objective', lp)
obj =
31.782759
Or without echoing on screen:
-->obj=sclpsolve('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:
-->x=sclpsolve('get_variables', lp);
-->b=sclpsolve('get_constraints', lp);
It is always possible to show the contents of a variable by just giving it as command:
-->x
x =
! 28.6 !
! 0. !
! 0. !
! 31.827586 !
Matrices
In Scilab, all numerical data is stored in matrices; even a scalar variable. Scilab also supports complex numbers
(a + b * %i with %i=SQRT(-1)). sclpsolve can only work with real numbers.
Scilab also supports sparse matrices. Sparse matrices are matrices where only the non-zero elements are provided
and stored. This results in both less storage and faster calculation if there are a sufficient number of zero values
in the matrix and there usually are. The sclpsolve driver supports both dense and sparse matrices and their use
is totally transparent to the user. Everywhere a matrix can be provided, it can be dense or sparse. However, Scilab
requires for interface programs that sparse matrixes are converted to MATLAB sparse matrices via the function mtlb_sparse(mat).
In the above example all matrices were dense. For example:
-->sclpsolve('add_constraint', lp, [0.24, 0, 11.31, 0], 1, 14.8);
In sparse matrix notation, this can be written:
-->sclpsolve('add_constraint', lp, mtlb_sparse(sparse([0.24, 0, 11.31, 0])), 1, 14.8);
Most of the time, variables are used to provide the data:
-->sclpsolve('add_constraint', lp, a1, 1, 14.8);
Where a1 is a dense matrix variable. A sparse matrix is then provided as follows:
-->sclpsolve('add_constraint', lp, mtlb_sparse(a1), 1, 14.8);
The sclpsolve driver sees all provided matrices as sparse matrices. sclpsolve also uses sparse matrices
internally and data can be provided sparse via the ex routines. For example add_constraintex. The sclpsolve
driver always uses the ex routines to provide the data to lpsolve. Even if you call from Scilab the routine
names that would require a dense matrix (for example add_constraint), the sclpsolve driver will always call the
sparse version of the routine (for example add_constraintex). This results in the most performing behaviour.
Note that if a dense matrix is provided, the dimension must exactly match the dimension that is expected by
sclpsolve. Matrices with too few or too much elements gives an 'invalid vector.' error. Sparse matrices can off
course provide less elements (the non provided elements are seen as zero). However if too many elements are
provided or an element with a too large index, again an 'invalid vector.' error is raised.
Most of the time, sclpsolve 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:
-->sclpsolve('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 Scilab 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 Scilab 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, Scilab 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.
It is noted however that this doesn't seem to work very well in Scilab. Scilab allows to return string sets, but when
a string set is provided to an interface program, the following error occurs:
!--error 9999
Invalid string matrix (at most one column!)
!--error 999
SIGSTP: aborting current computation
This is not an error generated by the sclpsolve driver, but from the Scilab parser. Hopefully, this problem
will be corrected in the future.
Maximum usage of matrices/sets with sclpsolve
Because Scilab 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 sclpsolve Scilab 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 sclpsolve Scilab driver however also allows specifying a vector to set the integer
status of all variables at once. The API call is: return = sclpsolve('set_int', lp_handle, column, must_be_int). The
matrix version of this call is: return = sclpsolve('set_int', lp_handle, [must_be_int]).
The API call to return the integer status of a variable is: return = sclpsolve('is_int', lp_handle, column). The
matrix version of this call is: [is_int] = sclpsolve('is_int', lp_handle)
Also note the get_mat and set_mat routines. In Scilab 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 exec example2.sce.
This will execute example2.sce.)
-->lp=sclpsolve('make_lp', 0, 4);
-->sclpsolve('set_verbose', lp, 3);
-->sclpsolve('set_obj_fn', lp, [1, 3, 6.24, 0.1]);
-->sclpsolve('add_constraint', lp, [0, 78.26, 0, 2.9], 2, 92.3);
-->sclpsolve('add_constraint', lp, [0.24, 0, 11.31, 0], 1, 14.8);
-->sclpsolve('add_constraint', lp, [12.68, 0, 0.08, 0.9], 2, 4);
-->sclpsolve('set_lowbo', lp, [28.6, 0, 0, 18]);
-->sclpsolve('set_upbo', lp, [%inf, %inf, %inf, 48.98]);
-->// sclpsolve('set_col_name', lp, {'COLONE', 'COLTWO', 'COLTHREE', 'COLFOUR'}); // gives a Scilab error with most releases :-(
-->// sclpsolve('set_row_name', lp, {'THISROW', 'THATROW', 'LASTROW'}); // gives a Scilab error with most releases :-(
-->sclpsolve('write_lp', lp, 'a.lp');
-->sclpsolve('get_mat', lp)
ans =
! 0. 78.26 0. 2.9 !
! .24 0. 11.31 0. !
! 12.68 0. .08 .9 !
-->sclpsolve('solve', lp)
ans =
0.
-->sclpsolve('get_objective', lp)
ans =
31.782759
-->sclpsolve('get_variables', lp)
ans =
! 28.6 !
! 0. !
! 0. !
! 31.827586 !
-->sclpsolve('get_constraints', lp)
ans =
! 92.3 !
! 6.864 !
! 391.29283 !
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:
-->A=sclpsolve('get_mat', lp);
-->X=sclpsolve('get_variables', lp);
-->B = A * X
B =
! 92.3 !
! 6.864 !
! 391.29283 !
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:
-->sclpsolve('get_constraints', lp)
ans =
! 92.3 !
! 6.864 !
! 391.29283 !
Exactly the same as the calculated B vector, as expected.
Also the value of the objective can be calculated in a same way:
-->C=sclpsolve('get_obj_fn', lp);
-->X=sclpsolve('get_variables', lp);
obj =
31.782759
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:
-->sclpsolve('get_objective', lp)
ans =
31.7828
Again exactly the same as the calculated obj value, as expected.
Script files
Scilab can execute a sequence of statements stored in diskfiles. Scilab has two kinds of these.
The first kinds are ASCII files where Scilab commands are written in the same way as in the command window.
These files normally have the extension .sce. These script files must be executed via the exec command. For example:
exec example1.sce
The second kinds are binary files. However the user enters the commands first in an ASCII file (normally with extention .sci)
and then these are translated to binary files via the Scilab genlib command. The .sci files also may only contain Scilab commands.
There are two advantages of using these. The first is that you don't have to use the exec command or provide the extension to execute them.
So it is as if you execute a regular Scilab command. The second advantage is that they are somewhat faster.
The lpsolve distribution contains some sample .sce files that must be executed via exec and also some .sci
high-level routines that can be executed without exec. They are already precompiled.
example1.sce
Contains the commands as shown in the first example of this article. Execute via exec example1.sce
example2.sce
Contains the commands as shown in the second example of this article. Execute via exec example2.sce
lp_solve.sci
This script uses the API to create a higher-level 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 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 lp-model:
max: -x1 + 2 x2;
C1: 2x1 + x2 < 5;
-4 x1 + 4 x2 <5;
int x2,x1;
The following command can be used:
-->[obj, x]=lp_solve([-1, 2], [2, 1; -4, 4], [5, 5], [-1, -1], [], [], [1, 2])
x =
! 1. !
! 2. !
obj =
3.
Note that you can also provide sparse matrices to this function without having to use mtlb_sparse.
The script is taking care of this.
lp_maker.sci
This script is analog to the lp_solve script and also uses the API to create a higher-level 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:
-->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 sparse 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 non-negative 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 lp-model:
max: -x1 + 2 x2;
C1: 2x1 + x2 < 5;
-4 x1 + 4 x2 <5;
int x2,x1;
The following command can be used:
-->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:
-->sclpsolve('solve', lp)
ans =
0.
-->sclpsolve('get_objective', lp)
ans =
3.
-->sclpsolve('get_variables', lp)
ans =
! 1. !
! 2. !
Don't forget to free the handle and its associated memory when you are done:
-->sclpsolve('delete_lp', lp);
Note that you can also provide sparse matrices to this function without having to use mtlb_sparse.
The script is taking care of this.
lpdemo.sce
Contains several examples to build and solve lp models. Execute via exec lpdemo.sce
ex.sce
Contains several examples to build and solve lp models.
Also solves the lp_examples from the lp_solve distribution. Execute via exec ex.sce
Overview of API routines
-
add_column, add_columnex
- return = sclpsolve('add_column', lp_handle,
[column])
- return = sclpsolve('add_columnex', lp_handle,
[column])
- Both have the same interface from add_column but act as add_columnex
-
add_constraint, add_constraintex
- return = sclpsolve('add_constraint', lp_handle,
[row], constr_type, rh)
- return = sclpsolve('add_constraintex', lp_handle,
[row], constr_type, rh)
- Both have the same interface from add_constraint but act as add_constraintex
-
add_SOS
- return = sclpsolve('add_SOS', lp_handle, name,
sostype, priority, [sosvars], [weights])
- The count argument in the API documentation is not needed in Scilab 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 = sclpsolve('column_in_lp', lp_handle,
[column])
- No special considerations.
-
default_basis
- sclpsolve('default_basis', lp_handle)
- No special considerations.
-
del_column
- return = sclpsolve('del_column', lp_handle, column)
- No special considerations.
-
del_constraint
- return = sclpsolve('del_constraint', lp_handle,
del_row)
- No special considerations.
-
delete_lp
- sclpsolve('delete_lp', lp_handle)
- No special considerations.
-
free_lp
- sclpsolve('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 = sclpsolve('get_anti_degen', lp_handle)
- No special considerations.
-
get_basis
- [bascolumn] = sclpsolve('get_basis', lp_handle {,
nonbasic})
- The bascolumn argument in the API documentation is here the return value. The nonbasic argument is optional in Scilab. If not provided, then 0 is used.
-
get_basiscrash
- return = sclpsolve('get_basiscrash', lp_handle)
- No special considerations.
-
get_bb_depthlimit
- return = sclpsolve('get_bb_depthlimit', lp_handle)
- No special considerations.
-
get_bb_floorfirst
- return = sclpsolve('get_bb_floorfirst', lp_handle)
- No special considerations.
-
get_bb_rule
- return = sclpsolve('get_bb_rule', lp_handle)
- No special considerations.
-
get_bounds_tighter
- return = sclpsolve('get_bounds_tighter', lp_handle)
- No special considerations.
-
get_break_at_value
- return = sclpsolve('get_break_at_value', lp_handle)
- No special considerations.
-
get_col_name
- name = sclpsolve('get_col_name', lp_handle, column)
- [names] = sclpsolve('get_col_name', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_column
- [column, return] = sclpsolve('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 = sclpsolve('get_constr_type', lp_handle,
row)
- [constr_type] = sclpsolve('get_constr_type',
lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_constraints
- [constr, return] = sclpsolve('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] = sclpsolve('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 Scilab, there is no unused element in the matrix.
- The return code of the call is the second return value.
-
get_epsb
- return = sclpsolve('get_epsb', lp_handle)
- No special considerations.
-
get_epsd
- return = sclpsolve('get_epsd', lp_handle)
- No special considerations.
-
get_epsel
- return = sclpsolve('get_epsel', lp_handle)
- No special considerations.
-
get_epsint
- return = sclpsolve('get_epsint', lp_handle)
- No special considerations.
-
get_epsperturb
- return = sclpsolve('get_epsperturb', lp_handle)
- No special considerations.
-
get_epspivot
- return = sclpsolve('get_epspivot', lp_handle)
- No special considerations.
-
get_improve
- return = sclpsolve('get_improve', lp_handle)
- No special considerations.
-
get_infinite
- return = sclpsolve('get_infinite', lp_handle)
- No special considerations.
-
get_lowbo
- return = sclpsolve('get_lowbo', lp_handle, column)
- [return] = sclpsolve('get_lowbo', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_lp_index
- return = sclpsolve('get_lp_index', lp_handle,
orig_index)
- No special considerations.
-
get_lp_name
- name = sclpsolve('get_lp_name', lp_handle)
- No special considerations.
-
get_mat
- value = sclpsolve('get_mat', lp_handle, row, col)
- [matrix, return] = sclpsolve('get_mat', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix in the first return value.
The return code of the call is the second return value.
-
get_max_level
- return = sclpsolve('get_max_level', lp_handle)
- No special considerations.
-
get_maxpivot
- return = sclpsolve('get_maxpivot', lp_handle)
- No special considerations.
-
get_mip_gap
- return = sclpsolve('get_mip_gap', lp_handle,
absolute)
- No special considerations.
-
get_nameindex
- return = sclpsolve('get_nameindex', lp_handle, name, isrow)
- No special considerations.
-
get_Ncolumns
- return = sclpsolve('get_Ncolumns', lp_handle)
- No special considerations.
-
get_negrange
- return = sclpsolve('get_negrange', lp_handle)
- No special considerations.
-
get_nonzeros
- return = sclpsolve('get_nonzeros', lp_handle)
- No special considerations.
-
get_Norig_columns
- return = sclpsolve('get_Norig_columns', lp_handle)
- No special considerations.
-
get_Norig_rows
- return = sclpsolve('get_Norig_rows', lp_handle)
- No special considerations.
-
get_Nrows
- return = sclpsolve('get_Nrows', lp_handle)
- No special considerations.
-
get_obj_bound
- return = sclpsolve('get_obj_bound', lp_handle)
- No special considerations.
-
get_objective
- return = sclpsolve('get_objective', lp_handle)
- No special considerations.
-
get_orig_index
- return = sclpsolve('get_orig_index', lp_handle,
lp_index)
- No special considerations.
-
get_origcol_name
- name = sclpsolve('get_origcol_name', lp_handle,
column)
- [names] = sclpsolve('get_origcol_name', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_origrow_name
- name = sclpsolve('get_origrow_name', lp_handle,
row)
- [names] = sclpsolve('get_origrow_name', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_pivoting
- return = sclpsolve('get_pivoting', lp_handle)
- No special considerations.
-
get_presolve
- return = sclpsolve('get_presolve', lp_handle)
- No special considerations.
-
get_primal_solution
- [pv, return] = sclpsolve('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 = sclpsolve('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 = sclpsolve('get_rh', lp_handle, row)
- [rh] = sclpsolve('get_rh', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix. Note that in this case, the value of row 0 is not returned.
-
get_rh_range
- return = sclpsolve('get_rh_range', lp_handle, row)
- [rh_ranges] = sclpsolve('get_rh_range', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_row
- [row, return] = sclpsolve('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 Scilab, there is no unused element in the matrix.
- The return code of the call is the second return value.
-
get_row_name
- name = sclpsolve('get_row_name', lp_handle, row)
- [names] = sclpsolve('get_row_name', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_scalelimit
- return = sclpsolve('get_scalelimit', lp_handle)
- No special considerations.
-
get_scaling
- return = sclpsolve('get_scaling', lp_handle)
- No special considerations.
-
get_sensitivity_obj, get_sensitivity_objex
- [objfrom, objtill, objfromvalue, objtillvalue,
return] = sclpsolve('get_sensitivity_obj', lp_handle)
- [objfrom, objtill, objfromvalue, objtillvalue,
return] = sclpsolve('get_sensitivity_objex', lp_handle)
- The objfrom, objtill, objfromvalue, objtillvalue arguments in the API documentation
are here the return values. Note that Scilab allows the return of fewer
variables. For example if only objfrom and objtill are needed then the
call can be [objfrom, objtill] = sclpsolve('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, Scilab 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] =
sclpsolve('get_sensitivity_rhs', lp_handle)
- [duals, dualsfrom, dualstill, return] =
sclpsolve('get_sensitivity_rhsex', lp_handle)
- The duals, dualsfrom, dualstill
arguments in the API documentation are here the return values. Note that
Scilab allows the return of fewer variables. For example if only duals is
needed then the call can be [duals] = sclpsolve('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 = sclpsolve('get_simplextype', lp_handle)
- No special considerations.
-
get_solutioncount
- return = sclpsolve('get_solutioncount', lp_handle)
- No special considerations.
-
get_solutionlimit
- return = sclpsolve('get_solutionlimit', lp_handle)
- No special considerations.
-
get_status
- return = sclpsolve('get_status', lp_handle)
- No special considerations.
-
get_statustext
- return = sclpsolve('get_statustext', lp_handle,
statuscode)
- No special considerations.
-
get_timeout
- return = sclpsolve('get_timeout', lp_handle)
- No special considerations.
-
get_total_iter
- return = sclpsolve('get_total_iter', lp_handle)
- No special considerations.
-
get_total_nodes
- return = sclpsolve('get_total_nodes', lp_handle)
- No special considerations.
-
get_upbo
- return = sclpsolve('get_upbo', lp_handle, column)
- [upbo] = sclpsolve('get_upbo', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_var_branch
- return = sclpsolve('get_var_branch', lp_handle,
column)
- [var_branch] = sclpsolve('get_var_branch',
lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_var_dualresult
- return = sclpsolve('get_var_dualresult', lp_handle,
index)
- No special considerations.
-
get_var_primalresult
- return = sclpsolve('get_var_primalresult',
lp_handle, index)
- No special considerations.
-
get_var_priority
- return = sclpsolve('get_var_priority', lp_handle,
column)
- [var_priority] = sclpsolve('get_var_priority',
lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
get_variables
- [var, return] = sclpsolve('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 = sclpsolve('get_verbose', lp_handle)
- No special considerations.
-
get_working_objective
- return = sclpsolve('get_working_objective',
lp_handle)
- No special considerations.
-
has_BFP
- return = sclpsolve('has_BFP', lp_handle)
- No special considerations.
-
has_XLI
- return = sclpsolve('has_XLI', lp_handle)
- No special considerations.
-
is_add_rowmode
- return = sclpsolve('is_add_rowmode', lp_handle)
- No special considerations.
-
is_anti_degen
- return = sclpsolve('is_anti_degen', lp_handle,
testmask)
- No special considerations.
-
is_binary
- return = sclpsolve('is_binary', lp_handle, column)
- [binary] = sclpsolve('is_binary', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
is_break_at_first
- return = sclpsolve('is_break_at_first', lp_handle)
- No special considerations.
-
is_constr_type
- return = sclpsolve('is_constr_type', lp_handle,
row, mask)
- No special considerations.
-
is_debug
- return = sclpsolve('is_debug', lp_handle)
- No special considerations.
-
is_feasible
- return = sclpsolve('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 = sclpsolve('is_free', lp_handle, column)
- [free] = sclpsolve('is_free', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
is_infinite
- return = sclpsolve('is_infinite', lp_handle, value)
- No special considerations.
-
is_int
- return = sclpsolve('is_int', lp_handle, column)
- [int] = sclpsolve('is_int', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
is_integerscaling
- return = sclpsolve('is_integerscaling', lp_handle)
- No special considerations.
-
is_maxim
- return = sclpsolve('is_maxim', lp_handle)
- No special considerations.
-
is_nativeBFP
- return = sclpsolve('is_nativeBFP', lp_handle)
- No special considerations.
-
is_nativeXLI
- return = sclpsolve('is_nativeXLI', lp_handle)
- No special considerations.
-
is_negative
- return = sclpsolve('is_negative', lp_handle,
column)
- [negative] = sclpsolve('is_negative', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
is_piv_mode
- return = sclpsolve('is_piv_mode', lp_handle,
testmask)
- No special considerations.
-
is_piv_rule
- return = sclpsolve('is_piv_rule', lp_handle, rule)
- No special considerations.
-
is_presolve
- return = sclpsolve('is_presolve', lp_handle,
testmask)
- No special considerations.
-
is_scalemode
- return = sclpsolve('is_scalemode', lp_handle,
testmask)
- No special considerations.
-
is_scaletype
- return = sclpsolve('is_scaletype', lp_handle,
scaletype)
- No special considerations.
-
is_semicont
- return = sclpsolve('is_semicont', lp_handle,
column)
- [semicont] = sclpsolve('is_semicont', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
is_SOS_var
- return = sclpsolve('is_SOS_var', lp_handle, column)
- [SOS_var] = sclpsolve('is_SOS_var', lp_handle)
- In Scilab, this routine has two formats. The first format is identical to the API.
The second format allows retrieving the values into a Scilab matrix.
-
is_trace
- return = sclpsolve('is_trace', lp_handle)
- No special considerations.
-
lp_solve_version
- versionstring = sclpsolve('lp_solve_version')
- The sclpsolve API routine returns the version information in 4 provided argument variables while the Scilab version returns the information as a string in the format major.minor.release.build
-
make_lp
- lp_handle = sclpsolve('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
- sclpsolve('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 Scilab (Windows) this means
that the output is not shown.
- The same information can also be obtained via sclpsolve('get_constraints', lp_handle). This shows the result on screen.
-
print_debugdump
- return = sclpsolve('print_debugdump', lp_handle,
filename)
- No special considerations.
-
print_duals
- sclpsolve('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 Scilab (Windows) this means
that the output is not shown.
- The same information can be obtained via sclpsolve('get_dual_solution', lp_handle). This shows the result on screen.
-
print_lp
- sclpsolve('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 Scilab (Windows) this means that the output is not shown.
-
print_objective
- sclpsolve('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 Scilab (Windows) this means
that the output is not shown.
- The same information can be obtained via sclpsolve('get_objective', lp_handle). This shows the result on screen.
-
print_scales
- sclpsolve('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 Scilab (Windows) this means that the output is not shown.
-
print_solution
- sclpsolve('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 Scilab (Windows) this means
that the output is not shown.
- The same information can also be obtained via sclpsolve('get_variables', lp_handle). This shows the result on screen.
-
print_str
- sclpsolve('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 Scilab (Windows) this means that the output is not shown.
-
print_tableau
- sclpsolve('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 Scilab (Windows) this means that the output is not shown.
-
put_abortfunc
-
put_logfunc
- Not implemented.
- However, the sclpsolve driver sets a log function to redirect the output of lpsolve from stdout (which is not visible in Windows MATLAB) to the command window of Scilab.
As such, all reported output can be seen in Scilab. 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_freemps, read_freeMPS
- lp_handle = sclpsolve('read_freemps', filename {,
verbose})
- lp_handle = sclpsolve('read_freeMPS', filename {,
verbose})
- In the lpsolve API, read_freemps needs a FILE
handle. In Scilab 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 = sclpsolve('read_lp', filename {,
verbose {, lp_name}})
- lp_handle = sclpsolve('read_LP', filename {,
verbose {, lp_name}})
- In the lpsolve API, read_lp needs a FILE handle. In
Scilab 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 = sclpsolve('read_mps', filename {,
verbose})
- lp_handle = sclpsolve('read_MPS', filename {,
verbose})
- In the lpsolve API, read_mps needs a FILE handle.
In Scilab 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 = sclpsolve('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 = sclpsolve('set_add_rowmode', lp_handle,
turnon)
- No special considerations.
-
set_anti_degen
- sclpsolve('set_anti_degen', lp_handle, anti_degen)
- No special considerations.
-
set_basis
- return = sclpsolve('set_basis', lp_handle,
[bascolumn], nonbasic)
- In the API, element 0 of bascolumn is not used and values start from element 1. In Scilab, there is no unused element in the matrix.
-
set_basiscrash
- sclpsolve('set_basiscrash', lp_handle, mode)
- No special considerations.
-
set_bb_depthlimit
- sclpsolve('set_bb_depthlimit', lp_handle,
bb_maxlevel)
- No special considerations.
-
set_bb_floorfirst
- sclpsolve('set_bb_floorfirst', lp_handle,
bb_floorfirst)
- No special considerations.
-
set_bb_rule
- sclpsolve('set_bb_rule', lp_handle, bb_rule)
- No special considerations.
-
set_BFP
- return = sclpsolve('set_BFP', lp_handle, filename)
- No special considerations.
-
set_binary
- return = sclpsolve('set_binary', lp_handle, column,
must_be_bin)
- return = sclpsolve('set_binary', lp_handle,
[must_be_bin])
- In Scilab, 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 = sclpsolve('set_bounds', lp_handle, column,
lower, upper)
- return = sclpsolve('set_bounds', lp_handle,
[lower], [upper])
- In Scilab, 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
- sclpsolve('set_bounds_tighter', lp_handle, tighten)
- No special considerations.
-
set_break_at_first
- sclpsolve('set_break_at_first', lp_handle,
break_at_first)
- No special considerations.
-
set_break_at_value
- sclpsolve('set_break_at_value', lp_handle,
break_at_value)
- No special considerations.
-
set_col_name
- return = sclpsolve('set_col_name', lp_handle,
column, name)
- return = sclpsolve('set_col_name', lp_handle,
[names])
- In Scilab, 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 = sclpsolve('set_column', lp_handle, col_no,
[column])
- return = sclpsolve('set_columnex', lp_handle,
col_no, [column])
- Both have the same interface from set_column but act as set_columnex
-
set_constr_type
- return = sclpsolve('set_constr_type', lp_handle,
row, con_type)
- return = sclpsolve('set_constr_type', lp_handle,
[con_type])
- In Scilab, 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
- sclpsolve('set_debug', lp_handle, debug)
- No special considerations.
-
set_epsb
- sclpsolve('set_epsb', lp_handle, epsb)
- No special considerations.
-
set_epsd
- sclpsolve('set_epsd', lp_handle, epsd)
- No special considerations.
-
set_epsel
- sclpsolve('set_epsel', lp_handle, epsel)
- No special considerations.
-
set_epsint
- sclpsolve('set_epsint', lp_handle, epsint)
- No special considerations.
-
set_epsperturb
- sclpsolve('set_epsperturb', lp_handle, epsperturb)
- No special considerations.
-
set_epspivot
- sclpsolve('set_epspivot', lp_handle, epspivot)
- No special considerations.
-
set_free
- return = sclpsolve('set_free', lp_handle, column)
- No special considerations.
-
set_improve
- sclpsolve('set_improve', lp_handle, improve)
- No special considerations.
-
set_infinite
- sclpsolve('set_infinite', lp_handle, infinite)
- No special considerations.
-
set_int
- return = sclpsolve('set_int', lp_handle, column,
must_be_int)
- return = sclpsolve('set_int', lp_handle,
[must_be_int])
- In Scilab, 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 = sclpsolve('set_lowbo', lp_handle, column,
value)
- return = sclpsolve('set_lowbo', lp_handle,
[values])
- In Scilab, 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 = sclpsolve('set_lp_name', lp_handle, name)
- No special considerations.
-
set_mat
- return = sclpsolve('set_mat', lp_handle, row,
column, value)
- return = sclpsolve('set_mat', lp_handle, [matrix])
- In Scilab, 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. Consider using a Scilab sparse matrix for maximum performance and least memory usage.
The matrix must be two-dimentional.
-
set_maxim
- sclpsolve('set_maxim', lp_handle)
- No special considerations.
-
set_maxpivot
- sclpsolve('set_maxpivot', max_num_inv)
- No special considerations.
-
set_minim
- sclpsolve('set_minim', lp_handle)
- No special considerations.
-
set_mip_gap
- sclpsolve('set_mip_gap', lp_handle, absolute,
mip_gap)
- No special considerations.
-
set_negrange
- sclpsolve('set_negrange', negrange)
- No special considerations.
-
set_obj
- return = sclpsolve('set_obj', lp_handle, column,
value)
- return = sclpsolve('set_obj', lp_handle, [values])
- In Scilab, 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
- sclpsolve('set_obj_bound', lp_handle, obj_bound)
- No special considerations.
-
set_obj_fn, set_obj_fnex
- return = sclpsolve('set_obj_fn', lp_handle, [row])
- return = sclpsolve('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 Scilab, there is no unused element in the matrix.
-
set_outputfile
- return = sclpsolve('set_outputfile', lp_handle,
filename)
- In the API description it says that setting filename to NULL results in writing output back to stdout.
In Scilab 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
- sclpsolve('set_pivoting', lp_handle, pivoting)
- No special considerations.
-
set_preferdual
- sclpsolve('set_preferdual', lp_handle, dodual)
- No special considerations.
-
set_presolve
- sclpsolve('set_presolve', lp_handle, do_presolve)
- No special considerations.
-
set_print_sol
- sclpsolve('set_print_sol', lp_handle, print_sol)
- No special considerations.
-
set_rh
- return = sclpsolve('set_rh', lp_handle, row, value)
- return = sclpsolve('set_rh', lp_handle, [values])
- In Scilab, 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 = sclpsolve('set_rh_range', lp_handle, row,
deltavalue)
- return = sclpsolve('set_rh_range', lp_handle,
[deltavalues])
- In Scilab, 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
- sclpsolve('set_rh_vec', lp_handle, [rh])
- In the API, element 0 is not used and values start from element 1. In Scilab, there is no unused element in the matrix.
-
set_row, set_rowex
- return = sclpsolve('set_row', lp_handle, row_no,
[row])
- return = sclpsolve('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 Scilab, there is no unused element in the matrix.
-
set_row_name
- return = sclpsolve('set_row_name', lp_handle, row,
name)
- return = sclpsolve('set_row_name', lp_handle,
[names])
- In Scilab, 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
- sclpsolve('set_scalelimit', lp_handle, scalelimit)
- No special considerations.
-
set_scaling
- sclpsolve('set_scaling', lp_handle, scalemode)
- No special considerations.
-
set_semicont
- return = sclpsolve('set_semicont', lp_handle,
column, must_be_sc)
- return = sclpsolve('set_semicont', lp_handle,
[must_be_sc])
- In Scilab, 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
- sclpsolve('set_sense', lp_handle, maximize)
- No special considerations.
-
set_simplextype
- sclpsolve('set_simplextype', lp_handle,
simplextype)
- No special considerations.
-
set_solutionlimit
- sclpsolve('set_solutionlimit', lp_handle,
simplextype)
- No special considerations.
-
set_timeout
- sclpsolve('set_timeout', lp_handle, sectimeout)
- No special considerations.
-
set_trace
- sclpsolve('set_trace', lp_handle, trace)
- No special considerations.
-
set_upbo
- return = sclpsolve('set_upbo', lp_handle, column,
value)
- return = sclpsolve('set_upbo', lp_handle, [values])
- In Scilab, 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 = sclpsolve('set_var_branch', lp_handle,
column, branch_mode)
- return = sclpsolve('set_var_branch', lp_handle,
[branch_mode])
- In Scilab, 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 = sclpsolve('set_var_weights', lp_handle,
[weights])
- No special considerations.
-
set_verbose
- sclpsolve('set_verbose', lp_handle, verbose)
- No special considerations.
-
set_XLI
- return = sclpsolve('set_XLI', lp_handle, filename)
- No special considerations.
-
solve
- result = sclpsolve('solve', lp_handle)
- No special considerations.
-
str_add_column
-
str_add_constraint
-
str_set_obj_fn
-
str_set_rh_vec
-
time_elapsed
- return = sclpsolve('time_elapsed', lp_handle)
- No special considerations.
-
unscale
- sclpsolve('unscale', lp_handle)
- No special considerations.
-
write_freemps, write_freeMPS
- return = sclpsolve('write_freemps', lp_handle,
filename)
- return = sclpsolve('write_freeMPS', lp_handle,
filename)
- In the lpsolve API, write_freeMPS needs a FILE handle. In Scilab it needs the filename and thus acts the same as write_freemps.
-
write_lp, write_LP
- return = sclpsolve('write_lp', lp_handle, filename)
- return = sclpsolve('write_LP', lp_handle, filename)
- In the lpsolve API, write_LP needs a FILE handle. In Scilab it needs the filename and thus acts the same as write_lp.
-
write_mps, write_MPS
- return = sclpsolve('write_mps', lp_handle,
filename)
- return = sclpsolve('write_MPS', lp_handle,
filename)
- In the lpsolve API, write_MPS needs a FILE handle.
In Scilab it needs the filename and thus acts the same as write_mps.
- No special considerations.
-
write_XLI
- return = sclpsolve('write_XLI', lp_handle, filename
{, options {, results}})
- No special considerations.
Extra Scilab 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] = sclpsolve('get_col_names', lp_handle)
- The same as get_col_name. Implemented for backwards compatibility.
- [constr_type] = sclpsolve('get_constr_types', lp_handle)
- The same as get_constr_type. Implemented for backwards compatibility.
- [int] = sclpsolve('get_int', lp_handle)
- The same as is_int. Implemented for backwards compatibility.
- return = sclpsolve('get_no_cols', lp_handle)
- The same as get_Ncolumns. Implemented for backwards compatibility.
- return = sclpsolve('get_no_rows', lp_handle)
- The same as get_Nrows. Implemented for backwards compatibility.
- name = sclpsolve('get_objective_name', lp_handle)
- The same as get_row_name with row=0. Implemented for backwards compatibility.
- [row_vec, return] = sclpsolve('get_obj_fn', lp_handle)
[row_vec, return] =
sclpsolve('get_obj_fun', lp_handle)
- The same as get_row with row 0. Implemented for backwards compatibility.
- name = sclpsolve('get_problem_name', lp_handle)
- The same as get_lp_name. Implemented for backwards compatibility.
- [costs] = sclpsolve('get_reduced_costs', lp_handle)
- The same as get_dual_solution. Implemented for backwards compatibility.
- [names] = sclpsolve('get_row_names', lp_handle)
- The same as get_row_name. Implemented for backwards compatibility.
- [obj, x, duals, return] = sclpsolve('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 = sclpsolve('mat_elm', lp_handle)
- The same as get_mat. Implemented for backwards compatibility.
- [handle_vec] = sclpsolve('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 = sclpsolve('read_lp_file', filename {, verbose {, lp_name}})
- The same as read_LP. Implemented for backwards compatibility.
Compile the sclpsolve driver
Windows, Unix/Linux
Under Windows, the sclpsolve Scilab driver is a dll: sclpsolve.dll
Under Unix/Linux, the sclpsolve Scilab driver is a static library sclpsolve.a
The library is an interface to the lpsolve51 library that contains the implementation of lp_solve.
Under windows this is a dll lpsolve51.dll and under Unix/Linux it is a shared library liblpsolve51.so
They are distributed with the lp_solve package. See at the beginning of this article where these files must be installed.
The sclpsolve Scilab driver is just
a wrapper between Scilab and lp_solve to translate the input/output to/from Scilab and the lp_solve library.
The sclpsolve Scilab driver is written in C. To compile this code, under Windows the Microsoft C compiler is needed and under
Unix the standard compiler is used.
This compiler must be called from Scilab. To make the compilation process easier, a script can be used:
builder.sce
Before the compilation is started, it may be necessary to edit the file Path.incl. In this file it is specified where scilab is installed
and were lp_solve is installed. Change the paths as needed.
To make everything, just enter exec builder.sce and everything is build.
This compiles the source files to the needed libraries, compiles the sci scripts and makes the manuals. It also
generates a new loader.sce file to load the driver into the Scilab workspace.
This build process is the same under Windows and Unix/Linux. Note that builder.sce generates Makefiles that
are then used to build the code. Don't edit the Makefiles since your changes will be lost when build.sce is
executed again.
See also Using lpsolve from MATLAB, Using lpsolve from O-Matrix