Several changes

1. Misc edit to ipopt_capi

2. Adding solver tests

Author: whart222

Diff for: lib/coek/coek/solvers/ipopt/ipopt_capi.cpp

@@ -30,8 +30,8 @@ namespace coek {
 
 int load_ipopt_library(const char* libname, std::string& error_message)
 {
-    char buf[256];
-    ipopt_handle = loadlib(libname, buf, 256);
+    char buf[1024];
+    ipopt_handle = loadlib(libname, buf, 1024);
     if (ipopt_handle == NULL) {
         error_message = buf;
         return 1;

Diff for: lib/coek/test/test_examples.cpp

@@ -288,6 +288,15 @@ TEST_CASE("ipopt_examples", "[smoke]")
         }
 #    endif
     }
+    else {
+        SECTION("rosenbr")
+        {
+            auto m = rosenbr();
+            coek::NLPModel nlp(m, adname);
+            auto res = solver.solve(nlp);
+            REQUIRE(res->termination_condition == coek::TerminationCondition::solver_not_available);
+        }
+    }
 #endif
 }
 
@@ -298,7 +307,6 @@ TEST_CASE("gurobi_examples", "[smoke]")
         SECTION("simplelp1")
         {
             auto m = simplelp1();
-
             solver.set_option("OutputFlag", 0);
             auto res = solver.solve(m);
             REQUIRE(coek::check_optimal_termination(res));
@@ -307,6 +315,13 @@ TEST_CASE("gurobi_examples", "[smoke]")
             REQUIRE(res->objective_bound == Approx(28750.0));
             check(m.get_variables(), simplelp1_soln);
         }
-        // SECTION("simplelp1_solve") { simplelp1_solve(); }
+    }
+    else {
+        SECTION("simplelp1")
+        {
+            auto m = simplelp1();
+            auto res = solver.solve(m);
+            REQUIRE(res->termination_condition == coek::TerminationCondition::solver_not_available);
+        }
     }
 }

Diff for: lib/poek/poek/tests/test_solver.py

@@ -3,10 +3,31 @@
 from poek import *
 
 
-def test_available():
+def test_available1():
     opt = solver("bad_coek_solvername")
     assert not opt.available
 
+def test_available2():
+    m = model()
+    x0 = m.add_variable(lower=0)
+    x1 = m.add_variable(lower=0)
+    m.add_objective(x0+x1)
+
+    opt = solver("bad_coek_solvername")
+    res = opt.solve(m)
+    assert res.termination_condition == "solver_not_available"
+
+def test_available3():
+    m = model()
+    x0 = m.add_variable(lower=0)
+    x1 = m.add_variable(lower=0)
+    m.add_objective(x0+x1)
+    nlp = nlp_model(m, "cppad")
+
+    opt = nlp_solver("bad_coek_solvername")
+    res = opt.solve(nlp)
+    assert res.termination_condition == "solver_not_available"
+
 
 def test_nlpsolver():
     m = model()
@@ -37,9 +58,13 @@ def test_rosenbr():
 
     nlp = nlp_model(m, "cppad")
     opt = nlp_solver("ipopt")
+    res = opt.solve(nlp)
+    assert res.solver_name == "ipopt"
     if opt.available:
-        res = opt.solve(nlp)
         assert check_optimal_termination(res)
-        assert res.solver_name == "ipopt"
         assert x0.value == pytest.approx(1.0)
         assert x1.value == pytest.approx(1.0)
+    else:
+        assert res.termination_condition == "solver_not_available"
+
+

Diff for: lib/pyomo_coek/pyomo_coek/test/level1/test_solver.py

@@ -0,0 +1,41 @@
+#
+# Test variable API
+#
+import pytest
+
+def test_available1(pyomo_module):
+    opt = pyomo_module.SolverFactory("bad_pyomo_solvername")
+    assert not opt.available(False)
+
+def test_available2(pyomo_module):
+    m = pyomo_module.ConcreteModel()
+    m.x0 = pyomo_module.Var(bounds=(0,None))
+    m.x1 = pyomo_module.Var(bounds=(0,None))
+    m.o = pyomo_module.Objective(expr=m.x0+m.x1)
+
+    opt = pyomo_module.SolverFactory("bad_pyomo_solvername")
+    with pytest.raises(RuntimeError) as e_info:
+        res = opt.solve(m)
+
+def test_gurobi(pyomo_module):
+    m = pyomo_module.ConcreteModel()
+    m.x0 = pyomo_module.Var(bounds=(0,None))
+    m.x1 = pyomo_module.Var(bounds=(0,None))
+    m.o = pyomo_module.Objective(expr=m.x0+m.x1)
+
+    opt = pyomo_module.SolverFactory("coek_gurobi")
+    if opt.available(False):
+        res = opt.solve(m)
+        pyo.check_optimal_termination(m)
+
+def test_ipopt(pyomo_module):
+    m = pyomo_module.ConcreteModel()
+    m.x0 = pyomo_module.Var(bounds=(0,None))
+    m.x1 = pyomo_module.Var(bounds=(0,None))
+    m.o = pyomo_module.Objective(expr=m.x0+m.x1)
+
+    opt = pyomo_module.SolverFactory("coek_ipopt")
+    if opt.available(False):
+        res = opt.solve(m)
+        pyo.check_optimal_termination(m)
+