diff --git a/src/ui.py b/src/ui.py
index b0f08a8..2cc5b04 100644
--- a/src/ui.py
+++ b/src/ui.py
@@ -153,25 +153,53 @@ class OptimizationUI(QWidget):
         This is a call-back for pressing the run button.
         """
         settings = self.current_settings
-        print("-" * 80)
         print(self.function_dropdown.currentText())
-        print(settings)
 
         values: list[float] = []
+        x = None
         for x in min_search(**settings):
             y = settings["function"](x)
             values.append(y)
-            print(f"{x} -> {y}")
 
-        self.plot_results(values)
+        if x is None:
+            raise RuntimeError("Number of iterations can't be 0")
 
-    def plot_results(self, values: list[float]) -> None:
+        self.plot_results(values, x)
+
+    def plot_results(self, values: list[float], optimal_x: INPUT_VECTOR) -> None:
         """Plot the decreasing values using Matplotlib."""
-        _ = plt.figure(figsize=(8, 5))
-        _ = plt.plot(values, marker="o", linestyle="-", color="b", label="Function Value")
-        _ = plt.xlabel("Iteration")
-        _ = plt.ylabel("Function Value")
-        _ = plt.title("Optimization Progress")
-        _ = plt.legend()
-        plt.grid(True)
+        func_name = self.function_dropdown.currentText()
+
+        _fig, ax = plt.subplots(figsize=(8, 5))
+        _ = ax.plot(values, marker="o", linestyle="-", color="b", label="Function Value")
+        _ = ax.set_xlabel("Iteration")
+        _ = ax.set_ylabel("Minimum Value")
+        _ = ax.set_title(f"Optimization Progress ({func_name})")
+        _ = ax.legend()
+        ax.grid(True)
+
+        # Convert optimal_x to a readable string
+        max_display_dim = 5  # Truncate if X has too many components (a lot of dimensions)
+        if optimal_x.size > max_display_dim:
+            optimal_x_str = np.array2string(optimal_x[:max_display_dim], precision=4, separator=", ")
+            optimal_x_str = f"{optimal_x_str[:-1]}, ...]"  # Add ellipsis at the end
+        else:
+            optimal_x_str = np.array2string(optimal_x, precision=4, separator=", ")
+
+        min_value = values[-1]
+
+        table_data = [
+            ["Min Value", f"{min_value:.6f}"],
+            ["Optimal X", optimal_x_str],
+        ]
+
+        # Add table
+        _ = plt.table(
+            cellText=table_data,
+            cellLoc="center",
+            loc="bottom",
+            bbox=[0.1, -0.4, 0.8, 0.2],  # Position table below plot
+        )
+
+        plt.subplots_adjust(bottom=0.35)  # Adjust layout to make room for the table
         plt.show()