# ifndef CPPAD_EXAMPLE_ABS_NORMAL_SIMPLEX_METHOD_HPP
# define CPPAD_EXAMPLE_ABS_NORMAL_SIMPLEX_METHOD_HPP
// SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
// SPDX-FileCopyrightText: Bradley M. Bell <bradbell@seanet.com>
// SPDX-FileContributor: 2003-22 Bradley M. Bell
// ----------------------------------------------------------------------------
/*
{xrst_begin simplex_method}
{xrst_spell
   maxitr
   rl
   xout
}

abs_normal: Solve a Linear Program Using Simplex Method
#######################################################

Syntax
******

   *ok* = ``simplex_method`` ( *level* , *b* , *A* , *c* , *maxitr* , *xout* )

Prototype
*********
{xrst_literal
   // BEGIN PROTOTYPE
   // END PROTOTYPE
}

Source
******
This following is a link to the source code for this example:
:ref:`simplex_method.hpp-name` .

Problem
*******
We are given
:math:`A \in \B{R}^{m \times n}`,
:math:`b \in \B{R}^m`,
:math:`c \in \B{R}^n`.
This routine solves the problem

.. math::

   \begin{array}{rl}
   \R{minimize} &
   g^T x \; \R{w.r.t} \; x \in \B{R}_+^n
   \\
   \R{subject \; to} & A x + b \leq 0
   \end{array}

Vector
******
The type *Vector* is a
simple vector with elements of type ``double`` .

level
*****
This value is less than or equal two.
If *level*  == 0 ,
no tracing is printed.
If *level*  >= 1 ,
a trace :math:`x` and the corresponding objective :math:`z`
is printed at each iteration.
If *level*  == 2 ,
a trace of the simplex Tableau is printed at each iteration.

A
*
This is a :ref:`row-major<glossary@Row-major Representation>` representation
of the matrix :math:`A` in the problem.

b
*
This is the vector :math:`b` in the problem.

c
*
This is the vector :math:`c` in the problem.

maxitr
******
This is the maximum number of simplex 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 corresponding to the problem solution.

ok
**
If the return value *ok* is true, a solution has been found.
{xrst_toc_hidden
   example/abs_normal/simplex_method.cpp
   example/abs_normal/simplex_method.xrst
}
Example
*******
The file :ref:`simplex_method.cpp-name` contains an example and test of
``simplex_method`` .

{xrst_end simplex_method}
-----------------------------------------------------------------------------
*/
# include <RcppCommon.h>
# include <cmath>
# include <cppad/utility/error_handler.hpp>
# include "abs_print_mat.hpp"

// BEGIN C++
namespace CppAD { // BEGIN_CPPAD_NAMESPACE

// BEGIN PROTOTYPE
template <class Vector>
bool simplex_method(
   size_t        level   ,
   const Vector& A       ,
   const Vector& b       ,
   const Vector& c       ,
   size_t        maxitr  ,
   Vector&       xout    )
// END PROTOTYPE
{  // number of equations
   size_t ne  = b.size();
   // number of x variables
   size_t nx = c.size();
   CPPAD_ASSERT_UNKNOWN( size_t(A.size()) == ne * nx );
   CPPAD_ASSERT_UNKNOWN( level <= 2 );
   //
   if( level > 0 )
   {  Rcpp::Rcout << "start simplex_method\n";
      CppAD::abs_print_mat("A", ne, nx, A);
      CppAD::abs_print_mat("b", ne,  1, b);
      CppAD::abs_print_mat("c", nx, 1, c);
   }
   //
   // variables (columns) in the Tableau:
   // x: the original primary variables with size n
   // s: slack variables, one for each equation
   // a: auxillary variables, one for each negative right hand size
   // r: right hand size for equations
   //
   // Determine number of auxillary variables
   size_t na = 0;
   for(size_t i = 0; i < ne; i++)
   {  if( b[i] > 0.0 )
         ++na;
   }
   // size of columns in the Tableau
   size_t nc = nx + ne + na + 1;

   // number of rows in Tableau, the equations plust two objectives
   size_t nr = ne + 2;

   // Initilize Tableau as zero
   Vector T(nr * nc);
   for(size_t i = 0; i < nr * nc; i++)
      T[i] = 0.0;

   // initialize basic variable flag as false
   CppAD::vector<size_t> basic(nc);
   for(size_t j = 0; j < nc; j++)
      basic[j] = false;

   // For i = 0 , ... , m-1, place the Equations
   // sum_j A_{i,j} * x_j + b_i <= 0 in Tableau
   na = 0; // use as index of next auxillary variable
   for(size_t i = 0; i < ne; i++)
   {  if( b[i] > 0.0)
      {  // convert to - sum_j A_{i,j} x_j - b_i >= 0
         for(size_t j = 0; j < nx; j++)
            T[i * nc + j] = - A[i * nx + j];
         // slack variable has negative coefficient
         T[i * nc + (nx + i)] = -1.0;
         // auxillary variable is basic for this constraint
         T[i * nc + (nx + ne + na)] = 1.0;
         basic[nx + ne + na]        = true;
         // right hand side
         T[i * nc + (nc - 1)] = b[i];
         //
         ++na;
      }
      else
      {  // sum_j A_{i,j} x_j + b_i <= 0
         for(size_t j = 0; j < nx; j++)
            T[i * nc + j] = A[i * nx + j];
         //  slack variable is also basic
         T[ i * nc + (nx + i) ]  = 1.0;
         basic[nx + i]           = true;
         // right hand side for equations
         T[ i * nc + (nc - 1) ] = - b[i];
      }
   }
   // na is back to its original value
   CPPAD_ASSERT_UNKNOWN( nc == nx + ne + na + 1 );
   //
   // place the equation objective equation in Tablueau
   // row ne corresponds to the equation z - sum_j c_j x_j = 0
   // column index for z is nx + ne + na
   for(size_t j = 0; j < nx; j++)
      T[ne * nc + j] = - c[j];
   //
   // row ne+1 corresponds to the equation w - a_0 - ... - a_{na-1} = 0
   // column index for w is nx + ne + na +1
   for(size_t j = 0; j < na; j++)
      T[(ne + 1) * nc + (nx + ne + j)] = -1.0;
   //
   // fix auxillary objective so coefficients in w
   // for auxillary variables are zero
   for(size_t k = 0; k < na; k++)
   {  size_t ja  = nx + ne + k;
      size_t ia  = ne;
      for(size_t i = 0; i < ne; i++)
      {  if( T[i * nc + ja] != 0.0 )
         {  CPPAD_ASSERT_UNKNOWN( T[i * nc + ja] == 1.0 );
            CPPAD_ASSERT_UNKNOWN( T[(ne + 1) * nc + ja] == -1.0 )
            CPPAD_ASSERT_UNKNOWN( ia == ne );
            ia = i;
         }
      }
      CPPAD_ASSERT_UNKNOWN( ia < ne );
      for(size_t j = 0; j < nc; j++)
         T[(ne + 1) * nc + j] += T[ia * nc + j];
      // The result in column ja is zero, avoid roundoff
      T[(ne + 1) * nc + ja] = 0.0;
   }
   //
   // index of current objective
   size_t iobj = ne;  // original objective z
   if( na > 0 )
      iobj = ne + 1; // auxillary objective w
   //
   // simplex interations
   for(size_t itr = 0; itr < maxitr; itr++)
   {  // current value for xout
      for(size_t j = 0; j < nx; j++)
      {  xout[j] = 0.0;
         if( basic[j] )
         {  // determine which row of column j is non-zero
            xout[j] = std::numeric_limits<double>::quiet_NaN();
            for(size_t i = 0; i < ne; i++)
            {  double T_ij = T[i * nc + j];
               CPPAD_ASSERT_UNKNOWN( T_ij == 0.0 || T_ij == 1.0 );
               if( T_ij == 1.0 )
               {  // corresponding value in right hand side
                  xout[j] = T[ i * nc + (nc-1) ];
               }
            }
         }
      }
      if( level > 1 )
         CppAD::abs_print_mat("T", nr, nc, T);
      if( level > 0 )
      {  CppAD::abs_print_mat("x", nx, 1, xout);
         Rcpp::Rcout << "itr = " << itr;
         if( iobj > ne )
            Rcpp::Rcout << ", auxillary objective w = ";
         else
            Rcpp::Rcout << ", objective z = ";
         Rcpp::Rcout << T[iobj * nc + (nc - 1)] << "\n";
      }
      //
      // number of variables depends on objective
      size_t nv = nx + ne;   // (x, s)
      if( iobj == ne + 1 )
      {  // check if we have solved the auxillary problem
         bool done = true;
         for(size_t k = 0; k < na; k++)
            if( basic[nx + ne + k] )
               done = false;
         if( done )
         {  // switch to optimizing the original objective
            iobj = ne;
         }
         else
            nv = nx + ne + na; // (x, s, a)
      }
      //
      // determine variable with maximuim coefficient in objective row
      double cmax = 0.0;
      size_t jmax = nv;
      for(size_t j = 0; j < nv; j++)
      {  if( T[iobj * nc + j] > cmax )
         {  CPPAD_ASSERT_UNKNOWN( ! basic[j] );
            cmax = T[ iobj * nc + j];
            jmax = j;
         }
      }
      // check for solution
      if( jmax == nv )
      {  if( iobj == ne )
         {  if( level > 0 )
               Rcpp::Rcout << "end simplex_method\n";
            return true;
         }
         if( level > 0 )
            Rcpp::Rcout << "end_simples_method: no feasible solution\n";
         return false;
      }
      //
      // We will increase the j-th variable.
      // Determine which row will be the pivot row.
      double rmin = std::numeric_limits<double>::infinity();
      size_t imin = ne;
      for(size_t i = 0; i < ne; i++)
      {  if( T[i * nc + jmax] > 0.0 )
         {  double r = T[i * nc + (nc-1) ] / T[i * nc + jmax];
            if( r < rmin )
            {  rmin = r;
               imin = i;
            }
         }
      }
      if( imin == ne )
      {  // not auxillary objective
         CPPAD_ASSERT_UNKNOWN( iobj == ne );
         if( level > 0 ) Rcpp::Rcout
            << "end simplex_method: objective is unbounded below\n";
         return false;
      }
      double pivot = T[imin * nc + jmax];
      //
      // Which variable is changing from basic to non-basic.
      // Initilaize as not yet determined.
      size_t basic2not = nc;
      //
      // Divide row imin by pivot element
      for(size_t j = 0; j < nc; j++)
      {  if( basic[j] && T[imin * nc + j] == 1.0 )
         {  CPPAD_ASSERT_UNKNOWN( basic2not == nc );
            basic2not = j;
         }
         T[imin * nc + j] /= pivot;
      }
      // The result in column jmax is one, avoid roundoff
      T[imin * nc + jmax ] = 1.0;
      //
      // Check that we found the variable going from basic to non-basic
      CPPAD_ASSERT_UNKNOWN( basic2not < nv && basic2not != jmax );
      //
      // convert variable for column jmax to basic
      // and for column basic2not to non-basic
      for(size_t i = 0; i < nr; i++) if( i != imin )
      {  double r = T[i * nc + jmax ] / T[imin * nc + jmax];
         // row_i = row_i - r * row_imin
         for(size_t j = 0; j < nc; j++)
            T[i * nc + j] -= r * T[imin * nc + j];
         // The result in column jmax is zero, avoid roundoff
         T[i * nc + jmax] = 0.0;
      }
      // update flag for basic variables
      basic[ basic2not ] = false;
      basic[ jmax ]      = true;
   }
   if( level > 0 ) Rcpp::Rcout
      << "end simplex_method: maximum # iterations without solution\n";
   return false;
}
} // END_CPPAD_NAMESPACE
// END C++

# endif
