1 files changed, 108 insertions, 0 deletions
diff --git a/utils/lbfgs.cpp b/utils/lbfgs.cpp
new file mode 100644
index 00000000..bd26f048
--- /dev/null
+++ b/utils/lbfgs.cpp
@@ -0,0 +1,108 @@
+#include <vector>
+#include <iostream>
+#include <cmath>
+#include <stdio.h>
+#include "mathvec.h"
+#include "lbfgs.h"
+#include "maxent.h"
+
+using namespace std;
+
+const static int M = LBFGS_M;
+const static double LINE_SEARCH_ALPHA = 0.1;
+const static double LINE_SEARCH_BETA = 0.5;
+
+// stopping criteria
+int LBFGS_MAX_ITER = 300;
+const static double MIN_GRAD_NORM = 0.0001;
+
+double ME_Model::backtracking_line_search(const Vec& x0, const Vec& grad0,
+                                          const double f0, const Vec& dx,
+                                          Vec& x, Vec& grad1) {
+  double t = 1.0 / LINE_SEARCH_BETA;
+
+  double f;
+  do {
+    t *= LINE_SEARCH_BETA;
+    x = x0 + t * dx;
+    f = FunctionGradient(x.STLVec(), grad1.STLVec());
+    //        cout << "*";
+  } while (f > f0 + LINE_SEARCH_ALPHA * t * dot_product(dx, grad0));
+
+  return f;
+}
+
+//
+// Jorge Nocedal, "Updating Quasi-Newton Matrices With Limited Storage",
+// Mathematics of Computation, Vol. 35, No. 151, pp. 773-782, 1980.
+//
+Vec approximate_Hg(const int iter, const Vec& grad, const Vec s[],
+                   const Vec y[], const double z[]) {
+  int offset, bound;
+  if (iter <= M) {
+    offset = 0;
+    bound = iter;
+  } else {
+    offset = iter - M;
+    bound = M;
+  }
+
+  Vec q = grad;
+  double alpha[M], beta[M];
+  for (int i = bound - 1; i >= 0; i--) {
+    const int j = (i + offset) % M;
+    alpha[i] = z[j] * dot_product(s[j], q);
+    q += -alpha[i] * y[j];
+  }
+  if (iter > 0) {
+    const int j = (iter - 1) % M;
+    const double gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
+    //    static double gamma;
+    //    if (gamma == 0) gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
+    q *= gamma;
+  }
+  for (int i = 0; i <= bound - 1; i++) {
+    const int j = (i + offset) % M;
+    beta[i] = z[j] * dot_product(y[j], q);
+    q += s[j] * (alpha[i] - beta[i]);
+  }
+
+  return q;
+}
+
+vector<double> ME_Model::perform_LBFGS(const vector<double>& x0) {
+  const size_t dim = x0.size();
+  Vec x = x0;
+
+  Vec grad(dim), dx(dim);
+  double f = FunctionGradient(x.STLVec(), grad.STLVec());
+
+  Vec s[M], y[M];
+  double z[M];  // rho
+
+  for (int iter = 0; iter < LBFGS_MAX_ITER; iter++) {
+
+    fprintf(stderr, "%3d  obj(err) = %f (%6.4f)", iter + 1, -f, _train_error);
+    if (_nheldout > 0) {
+      const double heldout_logl = heldout_likelihood();
+      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", heldout_logl,
+              _heldout_error);
+    }
+    fprintf(stderr, "\n");
+
+    if (sqrt(dot_product(grad, grad)) < MIN_GRAD_NORM) break;
+
+    dx = -1 * approximate_Hg(iter, grad, s, y, z);
+
+    Vec x1(dim), grad1(dim);
+    f = backtracking_line_search(x, grad, f, dx, x1, grad1);
+
+    s[iter % M] = x1 - x;
+    y[iter % M] = grad1 - grad;
+    z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]);
+    x = x1;
+    grad = grad1;
+  }
+
+  return x.STLVec();
+}