Update examples to be more like those covered in tests

Refs: sambo-optimization/sambo#4

Author: kernc

index.html

@@ -366,8 +366,9 @@ <h4>Use case №1: Find global minimium of an objective/cost function</h4>
 from scipy.optimize import rosen
 
 result = sambo.minimize(
-    rosen, bounds=[(-2, 2)]*2, method='shgo',
-    constraints=lambda x: sum(x**2) &lt;= 2**len(x))
+    rosen, bounds=[(-2., 2.)] * 2,  # Mind the dots
+    constraints=lambda x: sum(x**2) &lt;= 2**len(x),
+    max_iter=50, method='shgo')
 
 plot_convergence(result)
 plot_objective(result)    # Partial dependence
@@ -427,17 +428,21 @@ <h4>Use case №2: Sequential surrogate model-based "Ask-and-Tell" optimization<
    ...  # Abstract long and laborious process
    return rosen(x)
 
+bounds = [(-2., 2.)] * 4  # 4D
 optimizer = Optimizer(
-    fun=None,  # Implies ask-and-tell interface
-    bounds=[(-2, 2)]*2,
+    fun=None,  # Implies Ask-And-Tell interface
+    bounds=bounds,
     estimator='gp',  # or bring your own
 )
 
-for i in range(100):
+n_iter = 50
+for i in range(n_iter):
     suggested_x = optimizer.ask(1)
     y = [evaluate(x) for x in suggested_x]
     optimizer.tell(y)
 
+best_x, best_fvals = optimizer.top_k()
+# Continue the optimization ...
 result: OptimizeResult = optimizer.run()
 </code></pre>
         <div class="snippet snippet-plot" data-plot="- SMBO estimators">