Updated requirements.txt and README.md (#194)

* Update requirements.txt (numpy version)

* README updated (add MCO example)

* Update README.md

README.md

@@ -155,6 +155,49 @@ if __name__ == "__main__":
 
 ```
 
+Let's consider an example of using multicriteria optimization. We use optimization for two float objectives: precision and recall. The result of the process is a Pareto set chart.
+
+```python
+from examples.Machine_learning.SVC._2D.Problems import mco_breast_cancer
+
+from iOpt.solver import Solver
+from iOpt.solver_parametrs import SolverParameters
+from iOpt.output_system.listeners.console_outputers import ConsoleOutputListener
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+
+if __name__ == "__main__":
+
+    X, y = load_breast_cancer(return_X_y=True)
+    X_train, X_valid, y_train, y_valid = train_test_split(X, y)
+    problem = mco_breast_cancer.mco_breast_cancer(X, y, X_train, y_train)
+
+    params = SolverParameters(r=3.0, eps=0.01, iters_limit=200, number_of_lambdas=50,
+                              start_lambdas=[[0, 1]], is_scaling=False)
+
+    solver = Solver(problem=problem, parameters=params)
+
+    cfol = ConsoleOutputListener(mode='full')
+    solver.add_listener(cfol)
+
+    sol = solver.solve()
+
+    var = [trial.point.float_variables for trial in sol.best_trials]
+    val = [[-trial.function_values[i].value for i in range(2)] for trial in sol.best_trials]
+
+    print("size pareto set: ", len(var))
+    for fvar, fval in zip(var, val):
+        print(fvar, fval)
+
+    fv1 = [-trial.function_values[0].value for trial in sol.best_trials]
+    fv2 = [-trial.function_values[1].value for trial in sol.best_trials]
+    plt.plot(fv1, fv2, 'ro')
+    plt.show()
+
+```
+
+
 # **Project Structure**
 
 The latest stable release of iOpt is in the [main](https://github.com/UNN-ITMM-Software/iOpt/tree/main) branch. The repository includes the following directories:
@@ -170,4 +213,4 @@ A detailed description of the iOpt framework API is available at [Read the Docs]
 # **Supported by**
 
 The study is supported by the [Research Center Strong Artificial Intelligence in Industry](https://sai.itmo.ru/) 
-of [ITMO University](https://en.itmo.ru/) as part of the plan of the center's program: Framework of intelligent heuristic optimization methods.
+of [ITMO University](https://en.itmo.ru/) as part of the plan of the center's program: Framework of intelligent heuristic optimization methods.

requirements.txt

@@ -1,4 +1,4 @@
-numpy>=1.19
+numpy>=1.19,<2.0
 depq
 cycler
 kiwisolver