diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 4aacb6cd..9a6115c5 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -59,8 +59,12 @@ if (Python_FOUND AND NUMPY_FOUND AND (SWIG_FOUND OR (EXISTS ${PROJECT_SOURCE_DIR
   set (PYINSTALLCHECK_ENVIRONMENT "LD_LIBRARY_PATH=${PROJECT_BINARY_DIR}/src/swig"
                                   "PYTHONPATH=${PROJECT_BINARY_DIR}/src/swig"
     )
-  add_test (NAME test_python COMMAND ${Python_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/t_python.py)
-  set_tests_properties (test_python PROPERTIES ENVIRONMENT "${PYINSTALLCHECK_ENVIRONMENT}")
+
+  foreach (algo_index 24 25 31 40)
+    add_test (NAME test_python${algo_index} COMMAND ${Python_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/t_python.py ${algo_index})
+    set_tests_properties (test_python${algo_index} PROPERTIES ENVIRONMENT "${PYINSTALLCHECK_ENVIRONMENT}")
+  endforeach()
+
 endif ()
 
 if (OCTAVE_FOUND)
diff --git a/test/t_python.py b/test/t_python.py
index 91d2560a..6a6cb548 100644
--- a/test/t_python.py
+++ b/test/t_python.py
@@ -2,6 +2,7 @@
 
 import nlopt
 import math as m
+import sys
 
 
 def myfunc(x, grad):
@@ -16,8 +17,10 @@ def myconstraint(x, grad, a, b):
         grad[1] = -1.0
     return (a*x[0] + b)**3 - x[1]
 
-opt = nlopt.opt(nlopt.LD_MMA, 2)
-opt.set_lower_bounds([-float('inf'), 0])
+algo = nlopt.LD_MMA if len(sys.argv) < 2 else int(sys.argv[1])
+opt = nlopt.opt(algo, 2)
+print(f"algo: {opt.get_algorithm_name()}")
+opt.set_lower_bounds([-float('inf'), 1e-6])
 opt.set_min_objective(myfunc)
 opt.add_inequality_constraint(lambda x, grad: myconstraint(x,grad, 2, 0), 1e-8)
 opt.add_inequality_constraint(lambda x, grad: myconstraint(x,grad, -1, 1), 1e-8)
@@ -25,8 +28,9 @@ def myconstraint(x, grad, a, b):
 x0 = [1.234, 5.678]
 x = opt.optimize(x0)
 minf = opt.last_optimum_value()
-print('optimum at ', x)
-print('minimum value = ', minf)
-print('result code = ', opt.last_optimize_result())
-print('nevals = ', opt.get_numevals())
-print('initial step =', opt.get_initial_step(x0))
+print(f"optimum at {x}")
+print(f"minimum value: {minf:.7g}")
+print(f"result code: {opt.last_optimize_result()}")
+print(f"nevals: {opt.get_numevals()}")
+print(f"initial step: {opt.get_initial_step(x0)}")
+assert m.fabs(minf - 0.5443310474) < 1e-3, "wrong optimum"