Update informs list to be consistent with SNOPT 7.7 + updating docstrings for flake8 rst checks (#333)

* updated informs dictionary

* doc update

* removed old informs

* added test for time limit option

* fixed flake8 rst issues?

Author: marcomangano

doc/advancedFeatures.rst

@@ -62,6 +62,12 @@ Because the hot start process will store all the previous "restarted" iterations
 
 Time limit (for SNOPT only)
 ---------------------------
+
+.. note::
+
+   Since SNOPT 7.7, the user should rely on the ``Time Limit`` SNOPT option instead of the ``timeLimit`` argument of the ``Optimizer`` instance to set the maximum optimization time.
+
+
 The :ref:`optimizer` class in pyOptSparse has an attribute used to set the maximum allowable wall time for optimizations using SNOPT.
 The code will exit gracefully when such time limit is reached.
 This feature is particularly useful when running a time-constrained job, as in the case of most HPC systems.
@@ -79,5 +85,6 @@ Note that the attribute takes the maximum wall time *in seconds* as an integer n
    It will NOT interrupt an ongoing function or sensitivity evaluation.
    If your function evaluations are expensive, you should be more conservative when setting the ``timeLimit`` option for it to be effective.
 
+
 .. Clean Optimization Termination
 .. ------------------------------

examples/tp109.py

@@ -2,6 +2,7 @@
 Solves Schittkowski's TP109 constraint problem.
 
     min 	3.0*x1+1.*10**(-6)*x1**3+0.522074*10**(-6)*x2**3+2.0*x2
+
     s.t.    -(x4-x3+0.550) <= 0
             -(x3-x4+0.550) <= 0
             -(2.25*10**(+6)-x1**2-x8**2) <= 0
@@ -16,6 +17,7 @@
             -0.55 <= xi <= 0.55, i = 3,4
             196.0 <= xi <= 252.0, i = 5,6,7
             -400.0 <= xi <= 800.0, i = 8,9
+
     where   a = 50.176
             b = np.sin(0.25)
             c = np.cos(0.25)

pyoptsparse/pyOpt_optimization.py

@@ -1197,7 +1197,8 @@ def processObjtoDict(self, fobj_in: NumpyType, scaled: bool = True) -> Dict1DTyp
     def processContoVec(
         self, fcon_in: Dict1DType, scaled: bool = True, dtype: str = "d", natural: bool = False
     ) -> ndarray:
-        """
+        """A function that converts a dictionary of constraints into a vector
+
         Parameters
         ----------
         fcon_in : dict
@@ -1267,7 +1268,8 @@ def processContoVec(
     def processContoDict(
         self, fcon_in: ndarray, scaled: bool = True, dtype: str = "d", natural: bool = False, multipliers: bool = False
     ) -> Dict1DType:
-        """
+        """A function that converts an array of constraints into a dictionary
+
         Parameters
         ----------
         fcon_in : array

pyoptsparse/pyOpt_optimizer.py

@@ -941,7 +941,7 @@ def OPT(optName, *args, **kwargs):
        Either a string identifying the optimizer to create, e.g. "SNOPT", or
        an enum accessed via ``pyoptsparse.Optimizers``, e.g. ``Optimizers.SNOPT``.
 
-    *args, **kwargs : varies
+    \*args, \*\*kwargs : varies
        Passed to optimizer creation.
 
     Returns

pyoptsparse/pySNOPT/pySNOPT.py

@@ -116,45 +116,45 @@ def _getDefaultOptions() -> Dict[str, Any]:
 
     @staticmethod
     def _getInforms() -> Dict[int, str]:
+        # INFO exit codes for SNOPT 7.7
         informs = {
             0: "finished successfully",
             1: "optimality conditions satisfied",
             2: "feasible point found",
             3: "requested accuracy could not be achieved",
-            4: "weak QP minimizer",
+            5: "elastic objective minimized",
+            6: "elastic infeasibilities minimized",
             10: "the problem appears to be infeasible",
             11: "infeasible linear constraints",
             12: "infeasible linear equalities",
             13: "nonlinear infeasibilities minimized",
             14: "infeasibilities minimized",
             15: "infeasible linear constraints in QP subproblem",
+            16: "infeasible nonelastic constraints",
             20: "the problem appears to be unbounded",
             21: "unbounded objective",
             22: "constraint violation limit reached",
             30: "resource limit error",
             31: "iteration limit reached",
             32: "major iteration limit reached",
             33: "the superbasics limit is too small",
+            34: "time limit reached",
             40: "terminated after numerical difficulties",
             41: "current point cannot be improved",
             42: "singular basis",
             43: "cannot satisfy the general constraints",
             44: "ill-conditioned null-space basis",
+            45: "unable to compute acceptable LU factors",
             50: "error in the user-supplied functions",
             51: "incorrect objective  derivatives",
             52: "incorrect constraint derivatives",
-            53: "the QP Hessian is indefinite",
-            54: "incorrect second derivatives",
-            55: "incorrect derivatives",
             56: "irregular or badly scaled problem functions",
             60: "undefined user-supplied functions",
             61: "undefined function at the first feasible point",
             62: "undefined function at the initial point",
             63: "unable to proceed into undefined region",
             70: "user requested termination",
             71: "terminated during function evaluation",
-            72: "terminated during constraint evaluation",
-            73: "terminated during objective evaluation",
             74: "terminated from monitor routine",
             80: "insufficient storage allocated",
             81: "work arrays must have at least 500 elements",
@@ -164,30 +164,11 @@ def _getInforms() -> Dict[int, str]:
             90: "input arguments out of range",
             91: "invalid input argument",
             92: "basis file dimensions do not match this problem",
-            93: "the QP Hessian is indefinite",
-            100: "finished successfully",
-            101: "SPECS file read",
-            102: "Jacobian structure estimated",
-            103: "MPS file read",
-            104: "memory requirements estimated",
-            105: "user-supplied derivatives appear to be correct",
-            106: "no derivatives were checked",
-            107: "some SPECS keywords were not recognized",
-            110: "errors while processing MPS data",
-            111: "no MPS file specified",
-            112: "problem-size estimates too small",
-            113: "fatal error in the MPS file",
-            120: "errors while estimating Jacobian structure",
-            121: "cannot find Jacobian structure at given point",
-            130: "fatal errors while reading the SP",
-            131: "no SPECS file (iSpecs le 0 or iSpecs gt 99)",
-            132: "End-of-file while looking for a BEGIN",
-            133: "End-of-file while reading SPECS file",
-            134: "ENDRUN found before any valid SPECS",
             140: "system error",
             141: "wrong no of basic variables",
             142: "error in basis package",
         }
+
         return informs
 
     def __call__(
@@ -256,7 +237,7 @@ def __call__(
             Specify the maximum amount of time for optimizer to run.
             Must be in seconds. This can be useful on queue systems when
             you want an optimization to cleanly finish before the
-            job runs out of time.
+            job runs out of time. From SNOPT 7.7, use the "Time limit" option instead.
 
         restartDict : dict
             A dictionary containing the necessary information for hot-starting SNOPT.

tests/test_hs071.py

@@ -173,6 +173,8 @@ def test_snopt_informs(self):
         self.setup_optProb()
         sol = self.optimize(optOptions={"Major iterations limit": 1})
         self.assert_inform_equal(sol, 32)
+        sol = self.optimize(optOptions={"Time Limit": 0.001})
+        self.assert_inform_equal(sol, 34)
 
     def test_slsqp_informs(self):
         self.optName = "SLSQP"

tests/test_tp109.py

@@ -2,6 +2,7 @@
 Test uses Schittkowski's TP109 constraint problem.
 
     min 	3.0*x1+1.*10**(-6)*x1**3+0.522074*10**(-6)*x2**3+2.0*x2
+
     s.t.    -(x4-x3+0.550) <= 0
             -(x3-x4+0.550) <= 0
             -(2.25*10**(+6)-x1**2-x8**2) <= 0
@@ -16,6 +17,7 @@
             -0.55 <= xi <= 0.55, i = 3,4
             196.0 <= xi <= 252.0, i = 5,6,7
             -400.0 <= xi <= 800.0, i = 8,9
+
     where   a = 50.176
             b = np.sin(0.25)
             c = np.cos(0.25)