template <class Vector>
bool lp_box(
size_t level ,
const Vector& A ,
const Vector& b ,
const Vector& c ,
const Vector& d ,
size_t maxitr ,
Vector& xout )
Source
This following is a link to the source code for this example:
lp_box.hpp
.
Problem
We are given
@(@
A \in \B{R}^{m \times n}
@)@,
@(@
b \in \B{R}^m
@)@,
@(@
c \in \B{R}^n
@)@,
@(@
d \in \B{R}^n
@)@,
This routine solves the problem
@[@
\begin{array}{rl}
\R{minimize} &
c^T x \; \R{w.r.t} \; x \in \B{R}^n
\\
\R{subject \; to} & A x + b \leq 0 \; \R{and} \; - d \leq x \leq d
\end{array}
@]@
Vector
The type
Vector
is a
simple vector with elements of type double.
level
This value is less that or equal two.
If
level == 0
,
no tracing is printed.
If
level >= 1
,
a trace of the lp_box operations is printed.
If
level >= 2
,
the objective and primal variables @(@
x
@)@ are printed
at each simplex_method
iteration.
If
level == 3
,
the simplex tableau is printed at each simplex iteration.
A
This is a row-major
representation
of the matrix @(@
A
@)@ in the problem.
d
This is the vector @(@
d
@)@ in the problem.
If @(@
d_j
@)@ is infinity, there is no limit for the size of
@(@
x_j
@)@.
maxitr
This is the maximum number of newton iterations to try before giving up
on convergence.
xout
This argument has size is
n
and
the input value of its elements does no matter.
Upon return it is the primal variables
@(@
x
@)@ corresponding to the problem solution.
ok
If the return value
ok
is true, an optimal solution was found.
Example
The file lp_box.cpp
contains an example and test of
lp_box.
Input File: example/abs_normal/lp_box.hpp
