diff --git a/pyoptsparse/__init__.py b/pyoptsparse/__init__.py
index 1eff829b..20a2fdd9 100644
--- a/pyoptsparse/__init__.py
+++ b/pyoptsparse/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "2.14.4"
+__version__ = "2.14.5"
 
 from .pyOpt_history import History
 from .pyOpt_variable import Variable
diff --git a/pyoptsparse/pyIPOPT/pyIPOPT.py b/pyoptsparse/pyIPOPT/pyIPOPT.py
index 00879d97..adea0409 100644
--- a/pyoptsparse/pyIPOPT/pyIPOPT.py
+++ b/pyoptsparse/pyIPOPT/pyIPOPT.py
@@ -11,6 +11,7 @@
 import numpy as np
 
 # Local modules
+from ..pyOpt_error import pyOptSparseWarning
 from ..pyOpt_optimizer import Optimizer
 from ..pyOpt_solution import SolutionInform
 from ..pyOpt_utils import ICOL, INFINITY, IROW, convertToCOO, extractRows, import_module, scaleRows
@@ -281,12 +282,17 @@ def intermediate(self_cyipopt, alg_mod, iter_count, obj_value, inf_pr, inf_du, m
 
                         # Find pyoptsparse call counters for objective and constraints calls at current x.
                         # IPOPT calls objective and constraints separately, so we find two call counters and append iter_dict to both counters.
-                        call_counter_1 = self.hist._searchCallCounter(self.cache["x"])
-                        call_counter_2 = self.hist._searchCallCounter(self.cache["x"], last=call_counter_1 - 1)
+                        call_counter_1 = self.hist._searchCallCounter(self.optProb._mapXtoUser(self.cache["x"]))
+                        if call_counter_1 is None:
+                            call_counter_2 = None
+                        else:
+                            call_counter_2 = self.hist._searchCallCounter(self.optProb._mapXtoUser(self.cache["x"]), last=call_counter_1 - 1)
 
                         for call_counter in [call_counter_2, call_counter_1]:
                             if call_counter is not None:
                                 self.hist.write(call_counter, iterDict)
+                            else:
+                                pyOptSparseWarning("Failed to find a corresponding call counter at current x. Skipping writing to history file.")
 
                     if self.userRequestedTermination is True:
                         return False
diff --git a/tests/test_hs071.py b/tests/test_hs071.py
index 8ea94d1f..c98b94a6 100644
--- a/tests/test_hs071.py
+++ b/tests/test_hs071.py
@@ -117,24 +117,28 @@ def test_snopt_setDVFromHist(self):
         # since we restarted from the optimum
         self.assertEqual(second["xvars"].shape, (1, 4))
 
-    def test_slsqp_scaling_offset_optProb(self):
+    @parameterized.expand(["SLSQP", "IPOPT"])
+    def test_scaling_offset_optProb(self, optName):
         """
         Test that scaling and offset works as expected
         Also test optProb stored in the history file is correct
         """
-        self.optName = "SLSQP"
-        histFileName = "hs071_SLSQP_scaling_offset.hst"
+        self.optName = optName
+        histFileName = f"hs071_{optName}_scaling_offset.hst"
         objScale = 4.2
         xScale = [2, 3, 4, 5]
         conScale = [0.6, 1.7]
         offset = [1, -2, 40, 2.5]
         self.setup_optProb(objScale=objScale, xScale=xScale, conScale=conScale, offset=offset)
         sol = self.optimize(storeHistory=histFileName)
-        self.assert_solution_allclose(sol, self.tol["SLSQP"])
+        lambda_sign = -1.0 if optName == "IPOPT" else 1.0
+        self.assert_solution_allclose(sol, self.tol[optName], lambda_sign=lambda_sign)
         # now we retrieve the history file, and check the scale=True option is indeed
         # scaling things correctly
+        # IPOPT calls gradient and jacobian at first, so set callCounter = 2 for the first non-derivative call
+        callCounter = "2" if optName == "IPOPT" else "0"
         hist = History(histFileName, flag="r")
-        orig_values = hist.getValues(callCounters="0", scale=False)
+        orig_values = hist.getValues(callCounters=callCounter, scale=False)
         optProb = hist.getOptProb()
 
         # check that the scales are stored properly
@@ -147,13 +151,13 @@ def test_slsqp_scaling_offset_optProb(self):
             assert_allclose(objScale, optProb.objectives[obj].scale, atol=1e-12, rtol=1e-12)
 
         # verify the scale option in getValues
-        scaled_values = hist.getValues(callCounters="0", scale=True, stack=False)
+        scaled_values = hist.getValues(callCounters=callCounter, scale=True, stack=False)
         x = orig_values["xvars"][0]
         x_scaled = scaled_values["xvars"][0]
         assert_allclose(x_scaled, (x - offset) * xScale, atol=1e-12, rtol=1e-12)
 
         # now do the same but with stack=True
-        stacked_values = hist.getValues(callCounters="0", scale=True, stack=True)
+        stacked_values = hist.getValues(callCounters=callCounter, scale=True, stack=True)
         x_scaled = stacked_values["xuser"][0]
         assert_allclose(x_scaled, (x - offset) * xScale, atol=1e-12, rtol=1e-12)