WIP FISTA

skglm/solvers/gram.py

@@ -31,11 +31,15 @@ def gram_lasso(X, y, alpha, max_iter, tol, w_init=None, weights=None, check_freq
     lipschitz = np.zeros(n_features, dtype=X.dtype)
     for j in range(n_features):
         lipschitz[j] = (X[:, j] ** 2).sum() / len(y)
-    w = w_init if w_init is not None else np.zeros(n_features)
+    w = w_init.copy() if w_init is not None else np.zeros(n_features)
+    z = w_init.copy() if w_init is not None else np.zeros(n_features)
+    beta_0 = beta_1 = 1
     weights = weights if weights is not None else np.ones(n_features)
     # CD
     for n_iter in range(max_iter):
-        cd_epoch(X, G, grads, w, alpha, lipschitz, weights)
+        beta_1 = (1 + np.sqrt(1 + 4 * beta_0 ** 2)) / 2
+        cd_epoch(X, G, grads, w, z, alpha, beta_1, beta_0, lipschitz, weights)
+        beta_0 = beta_1
         if n_iter % check_freq == 0:
             p_obj = primal(alpha, y, X, w, weights)
             if p_obj_prev - p_obj < tol:
@@ -58,7 +62,7 @@ def gram_group_lasso(X, y, alpha, groups, max_iter, tol, w_init=None, weights=No
     for g in range(n_groups):
         X_g = X[:, grp_indices[grp_ptr[g]:grp_ptr[g + 1]]]
         lipschitz[g] = norm(X_g, ord=2) ** 2 / len(y)
-    w = w_init if w_init is not None else np.zeros(n_features)
+    w = w_init.copy() if w_init is not None else np.zeros(n_features)
     weights = weights if weights is not None else np.ones(n_groups)
     # BCD
     for n_iter in range(max_iter):
@@ -74,15 +78,17 @@ def gram_group_lasso(X, y, alpha, groups, max_iter, tol, w_init=None, weights=No
 
 
 @njit
-def cd_epoch(X, G, grads, w, alpha, lipschitz, weights):
+def cd_epoch(X, G, grads, w, z, alpha, beta_1, beta_0, lipschitz, weights):
     n_features = X.shape[1]
     for j in range(n_features):
         if lipschitz[j] == 0. or weights[j] == np.inf:
             continue
         old_w_j = w[j]
-        w[j] = ST(w[j] + grads[j] / lipschitz[j], alpha / lipschitz[j] * weights[j])
-        if old_w_j != w[j]:
-            grads += G[j, :] * (old_w_j - w[j]) / len(X)
+        old_z_j = z[j]
+        w[j] = ST(z[j] + grads[j] / lipschitz[j], alpha / lipschitz[j] * weights[j])
+        z[j] = w[j] + ((beta_0 - 1) / beta_1) * (w[j] - old_w_j)
+        if old_z_j != z[j]:
+            grads += G[j, :] * (old_z_j - z[j]) / len(X)
 
 
 @njit