#ifndef __SRC_LIB_PRICING_STEEPESTEDGEPRICING_HPP__
#define __SRC_LIB_PRICING_STEEPESTEDGEPRICING_HPP__

#include "basis.hpp"
#include "pricing.hpp"
#include "runtime.hpp"

//

class SteepestEdgePricing : public Pricing {
 private:
  Runtime& runtime;
  Basis& basis;

  std::vector<double> weights;

  HighsInt chooseconstrainttodrop(const Vector& lambda) {
    auto activeconstraintidx = basis.getactive();
    auto constraintindexinbasisfactor = basis.getindexinfactor();

    HighsInt minidx = -1;
    double maxval = 0.0;
    for (HighsInt i = 0; i < activeconstraintidx.size(); i++) {
      HighsInt indexinbasis =
          constraintindexinbasisfactor[activeconstraintidx[i]];
      if (indexinbasis == -1) {
        printf("error\n");
      }
      assert(indexinbasis != -1);

      double val = lambda.value[indexinbasis] * lambda.value[indexinbasis] /
                   weights[indexinbasis];
      if (val > maxval && fabs(lambda.value[indexinbasis]) >
                              runtime.settings.lambda_zero_threshold) {
        if (basis.getstatus(activeconstraintidx[i]) ==
                BasisStatus::ActiveAtLower &&
            -lambda.value[indexinbasis] > 0) {
          minidx = activeconstraintidx[i];
          maxval = val;
        } else if (basis.getstatus(activeconstraintidx[i]) ==
                       BasisStatus::ActiveAtUpper &&
                   lambda.value[indexinbasis] > 0) {
          minidx = activeconstraintidx[i];
          maxval = val;
        } else {
          // TODO
        }
      }
    }

    return minidx;
  }

 public:
  SteepestEdgePricing(Runtime& rt, Basis& bas)
      : runtime(rt),
        basis(bas),
        weights(std::vector<double>(rt.instance.num_var, 1.0)){};

  HighsInt price(const Vector& x, const Vector& gradient) {
    Vector lambda = basis.ftran(gradient);
    HighsInt minidx = chooseconstrainttodrop(lambda);
    return minidx;
  }

  void update_weights(const Vector& aq, const Vector& ep, HighsInt p,
                      HighsInt q) {
    HighsInt rowindex_p = basis.getindexinfactor()[p];

    Vector v = basis.btran(aq);

    double weight_p = weights[rowindex_p];
    for (HighsInt i = 0; i < runtime.instance.num_var; i++) {
      if (i == rowindex_p) {
        weights[i] = weight_p / (aq.value[rowindex_p] * aq.value[rowindex_p]);
      } else {
        weights[i] = weights[i] -
                     2 * (aq.value[i] / aq.value[rowindex_p]) * (v.value[i]) +
                     (aq.value[i] * aq.value[i]) /
                         (aq.value[rowindex_p] * aq.value[rowindex_p]) *
                         weight_p;
      }
    }
  }
};

#endif