diff --git a/GPy/core/model.py b/GPy/core/model.py
index 3634aee5..c9795dca 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -10,6 +10,7 @@ from parameterised import Parameterised
 import multiprocessing as mp
 import numpy as np
 from GPy.core.domains import POSITIVE, REAL
+from numpy.linalg.linalg import LinAlgError
 # import numdifftools as ndt
 
 class Model(Parameterised):
@@ -227,20 +228,32 @@ class Model(Parameterised):
         """
         The objective function passed to the optimizer. It combines the likelihood and the priors.
         """
-        self._set_params_transformed(x)
+        try:
+            self._set_params_transformed(x)
+        except (LinAlgError, ZeroDivisionError) as e:
+            print "DEBUG: catching linalg, rejecting step" # TODO: delete
+            return np.inf
         return -self.log_likelihood() - self.log_prior()
 
     def objective_function_gradients(self, x):
         """
         Gets the gradients from the likelihood and the priors.
         """
-        self._set_params_transformed(x)
+        try:
+            self._set_params_transformed(x)
+        except (LinAlgError, ZeroDivisionError) as e:
+            print "DEBUG: catching linalg, rejecting step" # TODO: delete
+            # return np.ones_like(x)
         obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
         return obj_grads
 
     def objective_and_gradients(self, x):
-        self._set_params_transformed(x)
-        obj_f = -self.log_likelihood() - self.log_prior()
+        try:
+            self._set_params_transformed(x)
+            obj_f = -self.log_likelihood() - self.log_prior()
+        except (LinAlgError, ZeroDivisionError) as e:
+            print "DEBUG: catching linalg, rejecting step" # TODO: delete
+            obj_f = np.inf
         obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
         return obj_f, obj_grads
 
diff --git a/GPy/util/linalg.py b/GPy/util/linalg.py
index 931f5a03..3d1c1943 100644
--- a/GPy/util/linalg.py
+++ b/GPy/util/linalg.py
@@ -96,15 +96,17 @@ def jitchol(A, maxtries=5):
         return L
     else:
         diagA = np.diag(A)
-        if np.any(diagA < 0.):
-            raise linalg.LinAlgError, "not pd: negative diagonal elements"
+        if np.any(diagA <= 0.):
+            raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
         jitter = diagA.mean() * 1e-6
-        for i in range(1, maxtries + 1):
+        while maxtries > 0 and np.isfinite(jitter):
             print 'Warning: adding jitter of {:.10e}'.format(jitter)
             try:
                 return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
             except:
                 jitter *= 10
+            finally:
+                maxtries -= 1
         raise linalg.LinAlgError, "not positive definite, even with jitter."