1 files changed, 0 insertions, 71 deletions
diff --git a/extern/libmv/patches/levenberg_marquardt.patch b/extern/libmv/patches/levenberg_marquardt.patch
deleted file mode 100644
index 49ef82d73d2..00000000000
--- a/extern/libmv/patches/levenberg_marquardt.patch
+++ /dev/null
@@ -1,71 +0,0 @@
-diff --git a/src/libmv/numeric/levenberg_marquardt.h b/src/libmv/numeric/levenberg_marquardt.h
-index 6a54f66..4473b72 100644
---- a/src/libmv/numeric/levenberg_marquardt.h
-+++ b/src/libmv/numeric/levenberg_marquardt.h
-@@ -33,6 +33,7 @@
- 
- #include "libmv/numeric/numeric.h"
- #include "libmv/numeric/function_derivative.h"
-+#include "libmv/logging/logging.h"
- 
- namespace libmv {
- 
-@@ -123,26 +124,40 @@ class LevenbergMarquardt {
-     Parameters dx, x_new;
-     int i;
-     for (i = 0; results.status == RUNNING && i < params.max_iterations; ++i) {
--      if (dx.norm() <= params.relative_step_threshold * x.norm()) {
-+      VLOG(1) << "iteration: " << i;
-+      VLOG(1) << "||f(x)||: " << f_(x).norm();
-+      VLOG(1) << "max(g): " << g.array().abs().maxCoeff();
-+      VLOG(1) << "u: " << u;
-+      VLOG(1) << "v: " << v;
-+
-+      AMatrixType A_augmented = A + u*AMatrixType::Identity(J.cols(), J.cols());
-+      Solver solver(A_augmented);
-+      dx = solver.solve(g);
-+      bool solved = (A_augmented * dx).isApprox(g);
-+      if (!solved) {
-+        LOG(ERROR) << "Failed to solve";
-+      }
-+      if (solved && dx.norm() <= params.relative_step_threshold * x.norm()) {
-         results.status = RELATIVE_STEP_SIZE_TOO_SMALL;
-         break;
--      }
--      x_new = x + dx;
--      // Rho is the ratio of the actual reduction in error to the reduction
--      // in error that would be obtained if the problem was linear.
--      // See [1] for details.
--      Scalar rho((error.squaredNorm() - f_(x_new).squaredNorm())
--                 / dx.dot(u*dx + g));
--      if (rho > 0) {
--        // Accept the Gauss-Newton step because the linear model fits well.
--        x = x_new;
--        results.status = Update(x, params, &J, &A, &error, &g);
--        Scalar tmp = Scalar(2*rho-1);
--        u = u*std::max(1/3., 1 - (tmp*tmp*tmp));
--        v = 2;
--        continue;
--      }
--
-+      } 
-+      if (solved) {
-+        x_new = x + dx;
-+        // Rho is the ratio of the actual reduction in error to the reduction
-+        // in error that would be obtained if the problem was linear.
-+        // See [1] for details.
-+        Scalar rho((error.squaredNorm() - f_(x_new).squaredNorm())
-+                   / dx.dot(u*dx + g));
-+        if (rho > 0) {
-+          // Accept the Gauss-Newton step because the linear model fits well.
-+          x = x_new;
-+          results.status = Update(x, params, &J, &A, &error, &g);
-+          Scalar tmp = Scalar(2*rho-1);
-+          u = u*std::max(1/3., 1 - (tmp*tmp*tmp));
-+          v = 2;
-+          continue;
-+        } 
-+      } 
-       // Reject the update because either the normal equations failed to solve
-       // or the local linear model was not good (rho < 0). Instead, increase u
-       // to move closer to gradient descent.