Updated informs (#138)

* updated informs for all optimizers, and also added tests * removed IPOPT copying in travis since it is compiled separately now * Delete opt_hist.hst The file is too old to be relevant, there is no need to provide a history file in the repo.
mdolab · Jul 10, 2020 · 772566c · 772566c
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commit 772566c
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Showing 8 changed files with 47 additions and 26 deletions.
diff --git a/.travis.yml b/.travis.yml
@@ -16,7 +16,6 @@ env:
     - DOCKER_MOUNT_DIR=/home/mdolabuser/travis/$REPO_NAME
     - SNOPT_DIR=$DOCKER_WORKING_DIR/pyoptsparse/pySNOPT/source
     - NLPQLP_DIR=$DOCKER_WORKING_DIR/pyoptsparse/pyNLPQLP/source
-    - IPOPT_DIR=$DOCKER_WORKING_DIR/pyoptsparse/pyIPOPT/Ipopt
   jobs:
     - DOCKER_TAG=py2-latest
     - DOCKER_TAG=py3-stable
@@ -43,7 +42,7 @@ before_install:
 install:
   # We back up the proprietary source codes first
   - if [ "$TRAVIS_PULL_REQUEST" == "false" ]; then
-      docker exec -it app /bin/bash -c "cp -r $SNOPT_DIR \$HOME/SNOPT && cp -r $NLPQLP_DIR \$HOME/NLPQLP && cp -r $IPOPT_DIR \$HOME/IPOPT";
+      docker exec -it app /bin/bash -c "cp -r $SNOPT_DIR \$HOME/SNOPT && cp -r $NLPQLP_DIR \$HOME/NLPQLP";
     fi
   # We thrown away the existing repo in Docker, and copy the new one in-place
   - docker exec -it app /bin/bash -c "rm -rf $DOCKER_WORKING_DIR && cp -r $DOCKER_MOUNT_DIR $DOCKER_WORKING_DIR"
@@ -52,7 +51,7 @@ install:
   - docker exec -it app /bin/bash -c ". \$HOME/.bashrc_mdolab && pip install testflo==1.3.5"
   # Copy back the proprietary codes
   - if [ "$TRAVIS_PULL_REQUEST" == "false" ]; then
-      docker exec -it app /bin/bash -c "cp -r \$HOME/NLPQLP/* $NLPQLP_DIR/ && cp -r \$HOME/SNOPT/* $SNOPT_DIR/ && cp -r \$HOME/IPOPT/ $IPOPT_DIR";
+      docker exec -it app /bin/bash -c "cp -r \$HOME/NLPQLP/* $NLPQLP_DIR/ && cp -r \$HOME/SNOPT/* $SNOPT_DIR/";
     fi
   # Build and install
   - docker exec -it app /bin/bash -c ". \$HOME/.bashrc_mdolab && cd $DOCKER_WORKING_DIR && pip install -e ."

diff --git a/postprocessing/opt_hist.hst b/postprocessing/opt_hist.hst
diff --git a/pyoptsparse/pyNLPQLP/pyNLPQLP.py b/pyoptsparse/pyNLPQLP/pyNLPQLP.py
@@ -76,6 +76,7 @@ def __init__(self, raiseError=True, *args, **kwargs):
             8: "The starting point violates a lower or upper bound.",
             9: "Wrong input parameter, i.e., MODE, LDL decomposition in D and C (in case of MODE=1), IPRINT, IOUT",
             10: "Internal inconsistency of the quadratic subproblem, division by zero.",
+            11: "More than MAXFUN successive non-evaluable function calls.",
             100: (
                 "The solution of the quadratic programming subproblem has been"
                 + " terminated with an error message and IFAIL is set to IFQL+100,"
@@ -236,7 +237,7 @@ def nlgrad(m, me, mmax, n, f, g, df, dg, x, active, wa):
 
             if os.path.isfile(go("iFile")):
                 os.remove(go("iFile"))
-            ifail = 0
+            ifail = np.array(0, dtype=int)
             # Run NLPQL
             t0 = time.time()
             # fmt: off
@@ -259,9 +260,10 @@ def nlgrad(m, me, mmax, n, f, g, df, dg, x, active, wa):
                 self.hist.close()
 
             # Store Results
+            inform = np.asscalar(ifail)
             sol_inform = {}
-            # sol_inform['value'] = inform
-            # sol_inform['text'] = self.informs[inform[0]]
+            sol_inform["value"] = inform
+            sol_inform["text"] = self.informs[inform]
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, ff, xs)

diff --git a/pyoptsparse/pyPSQP/pyPSQP.py b/pyoptsparse/pyPSQP/pyPSQP.py
@@ -224,7 +224,7 @@ def pdcon(n, k, x, dg):
                 return dg
 
             # Setup argument list values
-            iterm = 0
+            iterm = np.array(0, int)
             gmax = 0.0
             cmax = 0.0
             opt = self.getOption
@@ -255,9 +255,10 @@ def pdcon(n, k, x, dg):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
+            inform = np.asscalar(iterm)
             sol_inform = {}
-            # sol_inform['value'] = inform
-            # sol_inform['text'] = self.informs[inform[0]]
+            sol_inform["value"] = inform
+            sol_inform["text"] = self.informs[inform]
             if self.storeHistory:
                 self.metadata["endTime"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                 self.metadata["optTime"] = optTime

diff --git a/pyoptsparse/pySLSQP/pySLSQP.py b/pyoptsparse/pySLSQP/pySLSQP.py
@@ -201,7 +201,7 @@ def slgrad(m, me, la, n, f, g, df, dg, x):
                 if os.path.isfile(ifile):
                     os.remove(ifile)
 
-            mode = 0
+            mode = np.array(0, int)
             mineq = m - meq + 2 * (n + 1)
             lsq = (n + 1) * ((n + 1) + 1) + meq * ((n + 1) + 1) + mineq * ((n + 1) + 1)
             lsi = ((n + 1) - meq + 1) * (mineq + 2) + 2 * mineq
@@ -257,9 +257,10 @@ def slgrad(m, me, la, n, f, g, df, dg, x):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
+            inform = np.asscalar(mode)
             sol_inform = {}
-            # sol_inform['value'] = inform
-            # sol_inform['text'] = self.informs[inform[0]]
+            sol_inform["value"] = inform
+            sol_inform["text"] = self.informs[inform]
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, ff, xs)

diff --git a/pyoptsparse/pySNOPT/pySNOPT.py b/pyoptsparse/pySNOPT/pySNOPT.py
@@ -510,9 +510,10 @@ def __call__(
                 snopt.closeunit(self.options["iSumm"][1])
 
             # Store Results
+            inform = np.asscalar(inform)
             sol_inform = {}
             sol_inform["value"] = inform
-            sol_inform["text"] = self.informs[inform[0]]
+            sol_inform["text"] = self.informs[inform]
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, ff, xs[:nvar], multipliers=pi)

diff --git a/test/test_hs071.py b/test/test_hs071.py
@@ -85,14 +85,6 @@ def optimize(
                 assert_allclose(sol.lambdaStar["con"], self.lambdaStar, atol=tol, rtol=tol)
         return sol
 
-    def test_snopt(self):
-        test_name = "hs071_SNOPT"
-        optOptions = {
-            "Print file": "{}.out".format(test_name),
-            "Summary file": "{}_summary.out".format(test_name),
-        }
-        self.optimize("snopt", 1e-6, optOptions=optOptions)
-
     def test_slsqp_setDV(self):
         """
         Test that setDV works as expected, even with scaling/offset
@@ -218,13 +210,34 @@ def test_slsqp_scaling_offset_optProb(self):
         con_scaled = scaled_values["con"][0]
         assert_allclose(con_scaled, con_orig * conScale, atol=1e-12, rtol=1e-12)
 
+    def test_snopt(self):
+        test_name = "hs071_SNOPT"
+        optOptions = {
+            "Print file": "{}.out".format(test_name),
+            "Summary file": "{}_summary.out".format(test_name),
+        }
+        sol = self.optimize("snopt", 1e-6, optOptions=optOptions)
+        self.assertEqual(sol.optInform["value"], 1)
+        optOptions["Major iterations limit"] = 1
+        sol = self.optimize("snopt", 1e-6, optOptions=optOptions, check_solution=False)
+        self.assertEqual(sol.optInform["value"], 32)
+
     def test_slsqp(self):
         optOptions = {"IFILE": "hs071_SLSQP.out"}
-        self.optimize("slsqp", 1e-6, optOptions=optOptions)
+        sol = self.optimize("slsqp", 1e-6, optOptions=optOptions)
+        self.assertEqual(sol.optInform["value"], 0)
+        # now we set max iteration to 1 and verify that we get a different inform
+        optOptions["MAXIT"] = 1
+        sol = self.optimize("slsqp", 1e-6, optOptions=optOptions, check_solution=False)
+        self.assertEqual(sol.optInform["value"], 9)
 
     def test_nlpqlp(self):
         optOptions = {"iFile": "hs071_NLPQLP.out"}
-        self.optimize("nlpqlp", 1e-6, optOptions=optOptions)
+        sol = self.optimize("nlpqlp", 1e-6, optOptions=optOptions)
+        self.assertEqual(sol.optInform["value"], 0)
+        optOptions["maxIt"] = 1
+        sol = self.optimize("nlpqlp", 1e-6, optOptions=optOptions, check_solution=False)
+        self.assertEqual(sol.optInform["value"], 1)
 
     def test_ipopt(self):
         optOptions = {"print_level": 5, "output_file": "hs071_IPOPT.out"}
@@ -253,7 +266,11 @@ def test_conmin(self):
 
     def test_psqp(self):
         optOptions = {"IFILE": "hs071_PSQP.out"}
-        self.optimize("psqp", 1e-6, optOptions=optOptions)
+        sol = self.optimize("psqp", 1e-6, optOptions=optOptions)
+        self.assertEqual(sol.optInform["value"], 4)
+        optOptions["MIT"] = 1
+        sol = self.optimize("psqp", 1e-6, optOptions=optOptions, check_solution=False)
+        self.assertEqual(sol.optInform["value"], 11)
 
     def test_paropt(self):
         optOptions = {"output_file": "hs071_ParOpt.out"}

diff --git a/test/test_snopt_user_termination.py b/test/test_snopt_user_termination.py
@@ -88,7 +88,7 @@ def test_obj(self):
         self.assertEqual(termcomp.obj_count, 3)
 
         # Exit code for user requested termination.
-        self.assertEqual(sol.optInform["value"][0], 71)
+        self.assertEqual(sol.optInform["value"], 71)
 
     def test_sens(self):
         termcomp = TerminateComp(max_sens=3)
@@ -117,7 +117,7 @@ def test_sens(self):
         self.assertEqual(termcomp.sens_count, 4)
 
         # Exit code for user requested termination.
-        self.assertEqual(sol.optInform["value"][0], 71)
+        self.assertEqual(sol.optInform["value"], 71)
 
 
 if __name__ == "__main__":