diff --git a/internal/ceres/minimizer.h b/internal/ceres/minimizer.h
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2015 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// 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)
+
+#ifndef CERES_INTERNAL_MINIMIZER_H_
+#define CERES_INTERNAL_MINIMIZER_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+#include "ceres/internal/port.h"
+#include "ceres/iteration_callback.h"
+#include "ceres/solver.h"
+
+namespace ceres {
+namespace internal {
+
+class Evaluator;
+class SparseMatrix;
+class TrustRegionStrategy;
+class CoordinateDescentMinimizer;
+class LinearSolver;
+
+// Interface for non-linear least squares solvers.
+class Minimizer {
+ public:
+  // Options struct to control the behaviour of the Minimizer. Please
+  // see solver.h for detailed information about the meaning and
+  // default values of each of these parameters.
+  struct Options {
+    Options() {
+      Init(Solver::Options());
+    }
+
+    explicit Options(const Solver::Options& options) {
+      Init(options);
+    }
+
+    void Init(const Solver::Options& options) {
+      num_threads = options.num_threads;
+      max_num_iterations = options.max_num_iterations;
+      max_solver_time_in_seconds = options.max_solver_time_in_seconds;
+      max_step_solver_retries = 5;
+      gradient_tolerance = options.gradient_tolerance;
+      parameter_tolerance = options.parameter_tolerance;
+      function_tolerance = options.function_tolerance;
+      min_relative_decrease = options.min_relative_decrease;
+      eta = options.eta;
+      jacobi_scaling = options.jacobi_scaling;
+      use_nonmonotonic_steps = options.use_nonmonotonic_steps;
+      max_consecutive_nonmonotonic_steps =
+          options.max_consecutive_nonmonotonic_steps;
+      trust_region_problem_dump_directory =
+          options.trust_region_problem_dump_directory;
+      trust_region_minimizer_iterations_to_dump =
+          options.trust_region_minimizer_iterations_to_dump;
+      trust_region_problem_dump_format_type =
+          options.trust_region_problem_dump_format_type;
+      max_num_consecutive_invalid_steps =
+          options.max_num_consecutive_invalid_steps;
+      min_trust_region_radius = options.min_trust_region_radius;
+      line_search_direction_type = options.line_search_direction_type;
+      line_search_type = options.line_search_type;
+      nonlinear_conjugate_gradient_type =
+          options.nonlinear_conjugate_gradient_type;
+      max_lbfgs_rank = options.max_lbfgs_rank;
+      use_approximate_eigenvalue_bfgs_scaling =
+          options.use_approximate_eigenvalue_bfgs_scaling;
+      line_search_interpolation_type =
+          options.line_search_interpolation_type;
+      min_line_search_step_size = options.min_line_search_step_size;
+      line_search_sufficient_function_decrease =
+          options.line_search_sufficient_function_decrease;
+      max_line_search_step_contraction =
+          options.max_line_search_step_contraction;
+      min_line_search_step_contraction =
+          options.min_line_search_step_contraction;
+      max_num_line_search_step_size_iterations =
+          options.max_num_line_search_step_size_iterations;
+      max_num_line_search_direction_restarts =
+          options.max_num_line_search_direction_restarts;
+      line_search_sufficient_curvature_decrease =
+          options.line_search_sufficient_curvature_decrease;
+      max_line_search_step_expansion =
+          options.max_line_search_step_expansion;
+      inner_iteration_tolerance = options.inner_iteration_tolerance;
+      is_silent = (options.logging_type == SILENT);
+      is_constrained = false;
+      callbacks = options.callbacks;
+    }
+
+    int max_num_iterations;
+    double max_solver_time_in_seconds;
+    int num_threads;
+
+    // Number of times the linear solver should be retried in case of
+    // numerical failure. The retries are done by exponentially scaling up
+    // mu at each retry. This leads to stronger and stronger
+    // regularization making the linear least squares problem better
+    // conditioned at each retry.
+    int max_step_solver_retries;
+    double gradient_tolerance;
+    double parameter_tolerance;
+    double function_tolerance;
+    double min_relative_decrease;
+    double eta;
+    bool jacobi_scaling;
+    bool use_nonmonotonic_steps;
+    int max_consecutive_nonmonotonic_steps;
+    std::vector<int> trust_region_minimizer_iterations_to_dump;
+    DumpFormatType trust_region_problem_dump_format_type;
+    std::string trust_region_problem_dump_directory;
+    int max_num_consecutive_invalid_steps;
+    double min_trust_region_radius;
+    LineSearchDirectionType line_search_direction_type;
+    LineSearchType line_search_type;
+    NonlinearConjugateGradientType nonlinear_conjugate_gradient_type;
+    int max_lbfgs_rank;
+    bool use_approximate_eigenvalue_bfgs_scaling;
+    LineSearchInterpolationType line_search_interpolation_type;
+    double min_line_search_step_size;
+    double line_search_sufficient_function_decrease;
+    double max_line_search_step_contraction;
+    double min_line_search_step_contraction;
+    int max_num_line_search_step_size_iterations;
+    int max_num_line_search_direction_restarts;
+    double line_search_sufficient_curvature_decrease;
+    double max_line_search_step_expansion;
+    double inner_iteration_tolerance;
+
+    // If true, then all logging is disabled.
+    bool is_silent;
+
+    // Use a bounds constrained optimization algorithm.
+    bool is_constrained;
+
+    // List of callbacks that are executed by the Minimizer at the end
+    // of each iteration.
+    //
+    // The Options struct does not own these pointers.
+    std::vector<IterationCallback*> callbacks;
+
+    // Object responsible for evaluating the cost, residuals and
+    // Jacobian matrix.
+    std::shared_ptr<Evaluator> evaluator;
+
+    // Object responsible for actually computing the trust region
+    // step, and sizing the trust region radius.
+    std::shared_ptr<TrustRegionStrategy> trust_region_strategy;
+
+    // Object holding the Jacobian matrix. It is assumed that the
+    // sparsity structure of the matrix has already been initialized
+    // and will remain constant for the life time of the
+    // optimization.
+    std::shared_ptr<SparseMatrix> jacobian;
+
+    std::shared_ptr<CoordinateDescentMinimizer> inner_iteration_minimizer;
+  };
+
+  static Minimizer* Create(MinimizerType minimizer_type);
+  static bool RunCallbacks(const Options& options,
+                           const IterationSummary& iteration_summary,
+                           Solver::Summary* summary);
+
+  virtual ~Minimizer();
+  // Note: The minimizer is expected to update the state of the
+  // parameters array every iteration. This is required for the
+  // StateUpdatingCallback to work.
+  virtual void Minimize(const Options& options,
+                        double* parameters,
+                        Solver::Summary* summary) = 0;
+};
+
+}  // namespace internal
+}  // namespace ceres
+
+#endif  // CERES_INTERNAL_MINIMIZER_H_