minor refactor: nest "cauchy" and "grad" options for handle_npd

torchmin/newton.py

@@ -316,17 +316,17 @@ def _minimize_newton_exact(
             nfail += 1
             if handle_npd == 'lu':
                 d = torch.linalg.solve(hess, g.neg())
-            elif handle_npd == 'grad':
+            elif handle_npd in ['grad', 'cauchy']:
                 d = g.neg()
-            elif handle_npd == 'cauchy':
-                # cauchy point for a trust radius of delta=1.
-                # equivalent to 'grad' method with scaled lr
-                gnorm = g.norm(p=2)
-                scale = 1 / gnorm
-                gHg = g.dot(hess.mv(g))
-                if gHg > 0:
-                    scale *= torch.clamp_max_(gnorm.pow(3) / gHg, max=1)
-                d = scale * g.neg()
+                if handle_npd == 'cauchy':
+                    # cauchy point for a trust radius of delta=1.
+                    # equivalent to 'grad' with a scaled lr
+                    gnorm = g.norm(p=2)
+                    scale = 1 / gnorm
+                    gHg = g.dot(hess.mv(g))
+                    if gHg > 0:
+                        scale *= torch.clamp_(gnorm.pow(3) / gHg, max=1)
+                    d *= scale
             elif handle_npd == 'eig':
                 # this setting is experimental! use with caution
                 # TODO: why use the factor 1.5 here? Seems to work best