add example for constrained optimization

examples/optimization_applications/constrained_optimization.py

@@ -0,0 +1,38 @@
+import numpy as np
+
+from hyperactive import Hyperactive
+
+
+def convex_function(pos_new):
+    score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
+    return score
+
+
+search_space = {
+    "x1": list(np.arange(-100, 101, 0.1)),
+    "x2": list(np.arange(-100, 101, 0.1)),
+}
+
+
+def constraint_1(para):
+    # reject parameters where x1 and x2 are higher than 2.5 at the same time
+    return not (para["x1"] > 2.5 and para["x2"] > 2.5)
+
+
+# put one or more constraints inside a list
+constraints_list = [constraint_1]
+
+
+hyper = Hyperactive()
+# pass list of constraints
+hyper.add_search(
+    convex_function,
+    search_space,
+    n_iter=50,
+    constraints=constraints_list,
+)
+hyper.run()
+
+search_data = hyper.search_data(convex_function)
+
+print("\n search_data \n", search_data, "\n")