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diff --git a/internal/ceres/loss_function.cc b/internal/ceres/loss_function.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+//
+// Purpose: See .h file.
+
+#include "ceres/loss_function.h"
+
+#include <cmath>
+#include <cstddef>
+
+namespace ceres {
+
+void TrivialLoss::Evaluate(double s, double rho[3]) const {
+  rho[0] = s;
+  rho[1] = 1;
+  rho[2] = 0;
+}
+
+void HuberLoss::Evaluate(double s, double rho[3]) const {
+  if (s > b_) {
+    // Outlier region.
+    // 'r' is always positive.
+    const double r = sqrt(s);
+    rho[0] = 2 * a_ * r - b_;
+    rho[1] = a_ / r;
+    rho[2] = - rho[1] / (2 * s);
+  } else {
+    // Inlier region.
+    rho[0] = s;
+    rho[1] = 1;
+    rho[2] = 0;
+  }
+}
+
+void SoftLOneLoss::Evaluate(double s, double rho[3]) const {
+  const double sum = 1 + s * c_;
+  const double tmp = sqrt(sum);
+  // 'sum' and 'tmp' are always positive, assuming that 's' is.
+  rho[0] = 2 * b_ * (tmp - 1);
+  rho[1] = 1 / tmp;
+  rho[2] = - (c_ * rho[1]) / (2 * sum);
+}
+
+void CauchyLoss::Evaluate(double s, double rho[3]) const {
+  const double sum = 1 + s * c_;
+  const double inv = 1 / sum;
+  // 'sum' and 'inv' are always positive, assuming that 's' is.
+  rho[0] = b_ * log(sum);
+  rho[1] = inv;
+  rho[2] = - c_ * (inv * inv);
+}
+
+void ArctanLoss::Evaluate(double s, double rho[3]) const {
+  const double sum = 1 + s * s * b_;
+  const double inv = 1 / sum;
+  // 'sum' and 'inv' are always positive.
+  rho[0] = a_ * atan2(s, a_);
+  rho[1] = inv;
+  rho[2] = -2 * s * b_ * (inv * inv);
+}
+
+TolerantLoss::TolerantLoss(double a, double b)
+    : a_(a),
+      b_(b),
+      c_(b * log(1.0 + exp(-a / b))) {
+  CHECK_GE(a, 0.0);
+  CHECK_GT(b, 0.0);
+}
+
+void TolerantLoss::Evaluate(double s, double rho[3]) const {
+  const double x = (s - a_) / b_;
+  // The basic equation is rho[0] = b ln(1 + e^x).  However, if e^x is too
+  // large, it will overflow.  Since numerically 1 + e^x == e^x when the
+  // x is greater than about ln(2^53) for doubles, beyond this threshold
+  // we substitute x for ln(1 + e^x) as a numerically equivalent approximation.
+  static const double kLog2Pow53 = 36.7;  // ln(MathLimits<double>::kEpsilon).
+  if (x > kLog2Pow53) {
+    rho[0] = s - a_ - c_;
+    rho[1] = 1.0;
+    rho[2] = 0.0;
+  } else {
+    const double e_x = exp(x);
+    rho[0] = b_ * log(1.0 + e_x) - c_;
+    rho[1] = e_x / (1.0 + e_x);
+    rho[2] = 0.5 / (b_ * (1.0 + cosh(x)));
+  }
+}
+
+ComposedLoss::ComposedLoss(const LossFunction* f, Ownership ownership_f,
+                           const LossFunction* g, Ownership ownership_g)
+    : f_(CHECK_NOTNULL(f)),
+      g_(CHECK_NOTNULL(g)),
+      ownership_f_(ownership_f),
+      ownership_g_(ownership_g) {
+}
+
+ComposedLoss::~ComposedLoss() {
+  if (ownership_f_ == DO_NOT_TAKE_OWNERSHIP) {
+    f_.release();
+  }
+  if (ownership_g_ == DO_NOT_TAKE_OWNERSHIP) {
+    g_.release();
+  }
+}
+
+void ComposedLoss::Evaluate(double s, double rho[3]) const {
+  double rho_f[3], rho_g[3];
+  g_->Evaluate(s, rho_g);
+  f_->Evaluate(rho_g[0], rho_f);
+  rho[0] = rho_f[0];
+  // f'(g(s)) * g'(s).
+  rho[1] = rho_f[1] * rho_g[1];
+  // f''(g(s)) * g'(s) * g'(s) + f'(g(s)) * g''(s).
+  rho[2] = rho_f[2] * rho_g[1] * rho_g[1] + rho_f[1] * rho_g[2];
+}
+
+void ScaledLoss::Evaluate(double s, double rho[3]) const {
+  if (rho_.get() == NULL) {
+    rho[0] = a_ * s;
+    rho[1] = a_;
+    rho[2] = 0.0;
+  } else {
+    rho_->Evaluate(s, rho);
+    rho[0] *= a_;
+    rho[1] *= a_;
+    rho[2] *= a_;
+  }
+}
+
+}  // namespace ceres