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@(@\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }@)@This is cppad-20221105 documentation. Here is a link to its current documentation .
abs_normal: Solve a Linear Program With Box Constraints

Syntax
ok = lp_box(
    
levelAbcdmaxitrxout
)


Prototype

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.

b
This is the vector @(@ b @)@ in the problem.

c
This is the vector @(@ c @)@ 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