diff --git a/examples/nist.cc b/examples/nist.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 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)
+//
+// NIST non-linear regression problems solved using Ceres.
+//
+// The data was obtained from
+// http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml, where more
+// background on these problems can also be found.
+//
+// Currently not all problems are solved successfully. Some of the
+// failures are due to convergence to a local minimum, and some fail
+// because of numerical issues.
+//
+// TODO(sameeragarwal): Fix numerical issues so that all the problems
+// converge and then look at convergence to the wrong solution issues.
+
+#include <iostream>
+#include <fstream>
+#include "ceres/ceres.h"
+#include "ceres/split.h"
+#include "gflags/gflags.h"
+#include "glog/logging.h"
+#include "Eigen/Core"
+
+DEFINE_string(nist_data_dir, "", "Directory containing the NIST non-linear"
+              "regression examples");
+
+using Eigen::Dynamic;
+using Eigen::RowMajor;
+typedef Eigen::Matrix<double, Dynamic, 1> Vector;
+typedef Eigen::Matrix<double, Dynamic, Dynamic, RowMajor> Matrix;
+
+bool GetAndSplitLine(std::ifstream& ifs, std::vector<std::string>* pieces) {
+  pieces->clear();
+  char buf[256];
+  ifs.getline(buf, 256);
+  ceres::SplitStringUsing(std::string(buf), " ", pieces);
+  return true;
+}
+
+void SkipLines(std::ifstream& ifs, int num_lines) {
+  char buf[256];
+  for (int i = 0; i < num_lines; ++i) {
+    ifs.getline(buf, 256);
+  }
+}
+
+class NISTProblem {
+ public:
+  explicit NISTProblem(const std::string& filename) {
+    std::ifstream ifs(filename.c_str(), std::ifstream::in);
+
+    std::vector<std::string> pieces;
+    SkipLines(ifs, 24);
+    GetAndSplitLine(ifs, &pieces);
+    const int kNumResponses = std::atoi(pieces[1].c_str());
+
+    GetAndSplitLine(ifs, &pieces);
+    const int kNumPredictors = std::atoi(pieces[0].c_str());
+
+    GetAndSplitLine(ifs, &pieces);
+    const int kNumObservations = std::atoi(pieces[0].c_str());
+
+    SkipLines(ifs, 4);
+    GetAndSplitLine(ifs, &pieces);
+    const int kNumParameters = std::atoi(pieces[0].c_str());
+    SkipLines(ifs, 8);
+
+    // Get the first line of initial and final parameter values to
+    // determine the number of tries.
+    GetAndSplitLine(ifs, &pieces);
+    const int kNumTries = pieces.size() - 4;
+
+    predictor_.resize(kNumObservations, kNumPredictors);
+    response_.resize(kNumObservations, kNumResponses);
+    initial_parameters_.resize(kNumTries, kNumParameters);
+    final_parameters_.resize(1, kNumParameters);
+
+    // Parse the line for parameter b1.
+    int parameter_id = 0;
+    for (int i = 0; i < kNumTries; ++i) {
+      initial_parameters_(i, parameter_id) = std::atof(pieces[i + 2].c_str());
+    }
+    final_parameters_(0, parameter_id) = std::atof(pieces[2 + kNumTries].c_str());
+
+    // Parse the remaining parameter lines.
+    for (int parameter_id = 1; parameter_id < kNumParameters; ++parameter_id) {
+     GetAndSplitLine(ifs, &pieces);
+     // b2, b3, ....
+     for (int i = 0; i < kNumTries; ++i) {
+       initial_parameters_(i, parameter_id) = std::atof(pieces[i + 2].c_str());
+     }
+     final_parameters_(0, parameter_id) = std::atof(pieces[2 + kNumTries].c_str());
+    }
+
+    // Read the observations.
+    SkipLines(ifs, 20 - kNumParameters);
+    for (int i = 0; i < kNumObservations; ++i) {
+      GetAndSplitLine(ifs, &pieces);
+      // Response.
+      for (int j = 0; j < kNumResponses; ++j) {
+        response_(i, j) =  std::atof(pieces[j].c_str());
+      }
+
+      // Predictor variables.
+      for (int j = 0; j < kNumPredictors; ++j) {
+        predictor_(i, j) =  std::atof(pieces[j + kNumResponses].c_str());
+      }
+    }
+  }
+
+  Matrix initial_parameters(int start) const { return initial_parameters_.row(start); }
+  Matrix final_parameters() const  { return final_parameters_; }
+  Matrix predictor()        const { return predictor_;         }
+  Matrix response()         const { return response_;          }
+  int predictor_size()      const { return predictor_.cols();  }
+  int num_observations()    const { return predictor_.rows();  }
+  int response_size()       const { return response_.cols();   }
+  int num_parameters()      const { return initial_parameters_.cols(); }
+  int num_starts()          const { return initial_parameters_.rows(); }
+
+ private:
+  Matrix predictor_;
+  Matrix response_;
+  Matrix initial_parameters_;
+  Matrix final_parameters_;
+};
+
+#define NIST_BEGIN(CostFunctionName) \
+  struct CostFunctionName { \
+    CostFunctionName(const double* const x, \
+                     const double* const y) \
+        : x_(*x), y_(*y) {} \
+    double x_; \
+    double y_; \
+    template <typename T> \
+    bool operator()(const T* const b, T* residual) const { \
+    const T y(y_); \
+    const T x(x_); \
+      residual[0] = y - (
+
+#define NIST_END ); return true; }};
+
+// y = b1 * (b2+x)**(-1/b3)  +  e
+NIST_BEGIN(Bennet5)
+  b[0] * pow(b[1] + x, T(-1.0) / b[2])
+NIST_END
+
+// y = b1*(1-exp[-b2*x])  +  e
+NIST_BEGIN(BoxBOD)
+  b[0] * (T(1.0) - exp(-b[1] * x))
+NIST_END
+
+// y = exp[-b1*x]/(b2+b3*x)  +  e
+NIST_BEGIN(Chwirut)
+  exp(-b[0] * x) / (b[1] + b[2] * x)
+NIST_END
+
+// y  = b1*x**b2  +  e
+NIST_BEGIN(DanWood)
+  b[0] * pow(x, b[1])
+NIST_END
+
+// y = b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 )
+//     + b6*exp( -(x-b7)**2 / b8**2 ) + e
+NIST_BEGIN(Gauss)
+  b[0] * exp(-b[1] * x) +
+  b[2] * exp(-pow((x - b[3])/b[4], 2)) +
+  b[5] * exp(-pow((x - b[6])/b[7],2))
+NIST_END
+
+// y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x)  +  e
+NIST_BEGIN(Lanczos)
+  b[0] * exp(-b[1] * x) + b[2] * exp(-b[3] * x) + b[4] * exp(-b[5] * x)
+NIST_END
+
+// y = (b1+b2*x+b3*x**2+b4*x**3) /
+//     (1+b5*x+b6*x**2+b7*x**3)  +  e
+NIST_BEGIN(Hahn1)
+  (b[0] + b[1] * x + b[2] * x * x + b[3] * x * x * x) /
+  (T(1.0) + b[4] * x + b[5] * x * x + b[6] * x * x * x)
+NIST_END
+
+// y = (b1 + b2*x + b3*x**2) /
+//    (1 + b4*x + b5*x**2)  +  e
+NIST_BEGIN(Kirby2)
+  (b[0] + b[1] * x + b[2] * x * x) /
+  (T(1.0) + b[3] * x + b[4] * x * x)
+NIST_END
+
+// y = b1*(x**2+x*b2) / (x**2+x*b3+b4)  +  e
+NIST_BEGIN(MGH09)
+  b[0] * (x * x + x * b[1]) / (x * x + x * b[2] + b[3])
+NIST_END
+
+// y = b1 * exp[b2/(x+b3)]  +  e
+NIST_BEGIN(MGH10)
+  b[0] * exp(b[1] / (x + b[2]))
+NIST_END
+
+// y = b1 + b2*exp[-x*b4] + b3*exp[-x*b5]
+NIST_BEGIN(MGH17)
+  b[0] + b[1] * exp(-x * b[3]) + b[2] * exp(-x * b[4])
+NIST_END
+
+// y = b1*(1-exp[-b2*x])  +  e
+NIST_BEGIN(Misra1a)
+  b[0] * (T(1.0) - exp(-b[1] * x))
+NIST_END
+
+// y = b1 * (1-(1+b2*x/2)**(-2))  +  e
+NIST_BEGIN(Misra1b)
+  b[0] * (T(1.0) - T(1.0)/ ((T(1.0) + b[1] * x / 2.0) * (T(1.0) + b[1] * x / 2.0)))
+NIST_END
+
+// y = b1 * (1-(1+2*b2*x)**(-.5))  +  e
+NIST_BEGIN(Misra1c)
+  b[0] * (T(1.0) - pow(T(1.0) + T(2.0) * b[1] * x, 0.5))
+NIST_END
+
+// y = b1*b2*x*((1+b2*x)**(-1))  +  e
+NIST_BEGIN(Misra1d)
+  b[0] * b[1] * x / (T(1.0) + b[1] * x)
+NIST_END
+
+const double kPi = 3.141592653589793238462643383279;
+// pi = 3.141592653589793238462643383279E0
+// y =  b1 - b2*x - arctan[b3/(x-b4)]/pi  +  e
+NIST_BEGIN(Roszman1)
+  b[0] - b[1] * x - atan2(b[2], (x - b[3]))/T(kPi)
+NIST_END
+
+// y = b1 / (1+exp[b2-b3*x])  +  e
+NIST_BEGIN(Rat42)
+  b[0] / (T(1.0) + exp(b[1] - b[2] * x))
+NIST_END
+
+// y = b1 / ((1+exp[b2-b3*x])**(1/b4))  +  e
+NIST_BEGIN(Rat43)
+  b[0] / pow(T(1.0) + exp(b[1] - b[2] * x), T(1.0) / b[4])
+NIST_END
+
+// y = (b1 + b2*x + b3*x**2 + b4*x**3) /
+//    (1 + b5*x + b6*x**2 + b7*x**3)  +  e
+NIST_BEGIN(Thurber)
+  (b[0] + b[1] * x + b[2] * x * x  + b[3] * x * x * x) /
+  (T(1.0) + b[4] * x + b[5] * x * x + b[6] * x * x * x)
+NIST_END
+
+// y = b1 + b2*cos( 2*pi*x/12 ) + b3*sin( 2*pi*x/12 )
+//        + b5*cos( 2*pi*x/b4 ) + b6*sin( 2*pi*x/b4 )
+//        + b8*cos( 2*pi*x/b7 ) + b9*sin( 2*pi*x/b7 )  + e
+NIST_BEGIN(ENSO)
+  b[0] + b[1] * cos(T(2.0 * kPi) * x / T(12.0)) +
+         b[2] * sin(T(2.0 * kPi) * x / T(12.0)) +
+         b[4] * cos(T(2.0 * kPi) * x / b[3]) +
+         b[5] * sin(T(2.0 * kPi) * x / b[3]) +
+         b[7] * cos(T(2.0 * kPi) * x / b[6]) +
+         b[8] * sin(T(2.0 * kPi) * x / b[6])
+NIST_END
+
+// y = (b1/b2) * exp[-0.5*((x-b3)/b2)**2]  +  e
+NIST_BEGIN(Eckerle4)
+  b[0] / b[1] * exp(T(-0.5) * pow((x - b[2])/b[1], 2))
+NIST_END
+
+struct Nelson {
+ public:
+  Nelson(const double* const x, const double* const y)
+      : x1_(x[0]), x2_(x[1]), y_(y[0]) {}
+
+  template <typename T>
+  bool operator()(const T* const b, T* residual) const {
+    // log[y] = b1 - b2*x1 * exp[-b3*x2]  +  e
+    residual[0] = T(log(y_)) - (b[0] - b[1] * T(x1_) * exp(-b[2] * T(x2_)));
+    return true;
+  }
+
+ private:
+  double x1_;
+  double x2_;
+  double y_;
+};
+
+template <typename Model, int num_residuals, int num_parameters>
+void RegressionDriver(const std::string& filename,
+                      const ceres::Solver::Options& options) {
+  NISTProblem nist_problem(FLAGS_nist_data_dir + filename);
+  CHECK_EQ(num_residuals, nist_problem.response_size());
+  CHECK_EQ(num_parameters, nist_problem.num_parameters());
+
+  Matrix predictor = nist_problem.predictor();
+  Matrix response = nist_problem.response();
+  Matrix final_parameters = nist_problem.final_parameters();
+  std::vector<ceres::Solver::Summary> summaries(nist_problem.num_starts() + 1);
+
+  // Each NIST problem comes with multiple starting points, so we
+  // construct the problem from scratch for each case and solve it.
+  for (int start = 0; start < nist_problem.num_starts(); ++start) {
+    Matrix initial_parameters = nist_problem.initial_parameters(start);
+    ceres::Problem problem;
+
+    for (int i = 0; i < nist_problem.num_observations(); ++i) {
+      problem.AddResidualBlock(
+          new ceres::AutoDiffCostFunction<Model, num_residuals, num_parameters>(
+              new Model(predictor.data() + nist_problem.predictor_size() * i,
+                        response.data() + nist_problem.response_size() * i)),
+          NULL,
+          initial_parameters.data());
+    }
+
+    Solve(options, &problem, &summaries[start]);
+  }
+
+  // Ugly hack to get the objective function value at the certified
+  // optimal parameter values.
+  Matrix initial_parameters = nist_problem.final_parameters();
+  ceres::Problem problem;
+  for (int i = 0; i < nist_problem.num_observations(); ++i) {
+    problem.AddResidualBlock(
+        new ceres::AutoDiffCostFunction<Model, num_residuals, num_parameters>(
+            new Model(predictor.data() + nist_problem.predictor_size() * i,
+                      response.data() + nist_problem.response_size() * i)),
+        NULL,
+        initial_parameters.data());
+  }
+  Solve(options, &problem, &summaries.back());
+  double certified_cost = summaries.back().initial_cost;
+
+  std::cout << filename << std::endl;
+  for (int i = 0; i < nist_problem.num_starts(); ++i) {
+    std::cout << "start " << i + 1 << ": "
+              << " relative difference : "
+              << (summaries[i].final_cost - certified_cost) / certified_cost
+              << " termination: "
+              << ceres::SolverTerminationTypeToString(summaries[i].termination_type)
+              << std::endl;
+  }
+}
+
+int main(int argc, char** argv) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  google::InitGoogleLogging(argv[0]);
+
+  // TODO(sameeragarwal): Test more combinations of non-linear and
+  // linear solvers.
+  ceres::Solver::Options options;
+  options.linear_solver_type = ceres::DENSE_QR;
+  options.max_num_iterations = 2000;
+  options.function_tolerance *= 1e-10;
+  options.gradient_tolerance *= 1e-10;
+  options.parameter_tolerance *= 1e-10;
+
+  std::cout << "Lower Difficulty\n";
+  RegressionDriver<Misra1a,  1, 2>("Misra1a.dat",  options);
+  RegressionDriver<Chwirut,  1, 3>("Chwirut1.dat", options);
+  RegressionDriver<Chwirut,  1, 3>("Chwirut2.dat", options);
+  RegressionDriver<Lanczos,  1, 6>("Lanczos3.dat", options);
+  RegressionDriver<Gauss,    1, 8>("Gauss1.dat",   options);
+  RegressionDriver<Gauss,    1, 8>("Gauss2.dat",   options);
+  RegressionDriver<DanWood,  1, 2>("DanWood.dat",  options);
+  RegressionDriver<Misra1b,  1, 2>("Misra1b.dat",  options);
+
+  std::cout << "\nAverage Difficulty\n";
+  RegressionDriver<Kirby2,   1, 5>("Kirby2.dat",   options);
+  RegressionDriver<Hahn1,    1, 7>("Hahn1.dat",    options);
+  RegressionDriver<Nelson,   1, 3>("Nelson.dat",   options);
+  RegressionDriver<MGH17,    1, 5>("MGH17.dat",    options);
+  RegressionDriver<Lanczos,  1, 6>("Lanczos1.dat", options);
+  RegressionDriver<Lanczos,  1, 6>("Lanczos2.dat", options);
+  RegressionDriver<Gauss,    1, 8>("Gauss3.dat",   options);
+  RegressionDriver<Misra1c,  1, 2>("Misra1c.dat",  options);
+  RegressionDriver<Misra1d,  1, 2>("Misra1d.dat",  options);
+  RegressionDriver<Roszman1, 1, 4>("Roszman1.dat", options);
+  RegressionDriver<ENSO,     1, 9>("ENSO.dat",     options);
+
+  std::cout << "\nHigher Difficulty\n";
+  RegressionDriver<MGH09,    1, 4>("MGH09.dat",    options);
+  RegressionDriver<Thurber,  1, 7>("Thurber.dat",  options);
+  RegressionDriver<BoxBOD,   1, 2>("BoxBOD.dat",   options);
+  RegressionDriver<Rat42,    1, 3>("Rat42.dat",    options);
+  RegressionDriver<MGH10,    1, 3>("MGH10.dat",    options);
+  RegressionDriver<Eckerle4, 1, 3>("Eckerle4.dat", options);
+  RegressionDriver<Rat43,    1, 4>("Rat43.dat",    options);
+  RegressionDriver<Bennet5,  1, 3>("Bennett5.dat", options);
+
+  return 0;
+};