Fix Python example optim g24 (#238)

* Fix optim_g24 with new API, keep it minimal * Cleanup
relf · Jan 24, 2025 · a0093b1 · a0093b1
1 parent 6d3c5dc
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diff --git a/doc/Egor_Tutorial.ipynb b/doc/Egor_Tutorial.ipynb
@@ -184,14 +184,7 @@
                 "                infill_strategy=egx.InfillStrategy.WB2,\n",
                 "                target=-5.5,\n",
                 "                trego=False,\n",
-                "                # outdir=\"./out\",\n",
-                "                # hot_start=True\n",
-                "            )   \n",
-                "\n",
-                "# Specify regression and/or correlation models used to build the surrogates of objective and constraints\n",
-                "#egor = egx.Egor(g24, xlimits_g24, n_cstr=n_cstr_g24, n_doe=10,\n",
-                "#                      regr_spec=egx.RegressionSpec.LINEAR,\n",
-                "#                      corr_spec=egx.CorrelationSpec.MATERN32 | egx.CorrelationSpec.MATERN52)  "
+                "            )   "
             ]
         },
         {

diff --git a/python/egobox/examples/optim_g24.py b/python/egobox/examples/optim_g24.py
@@ -50,22 +50,15 @@ def g24(point):
     return np.array([G24(p), G24_c1(p), G24_c2(p)]).T
 
 
+# Configure the optimizer. See help(egor) for options
 egor = egx.Egor(
-    g24,
     xspecs_g24,
     n_doe=10,
     n_cstr=n_cstr_g24,
-    cstr_tol=1e-3,
+    cstr_tol=[1e-3] * n_cstr_g24,
     infill_strategy=egx.InfillStrategy.WB2,
-    # expected=egx.ExpectedOptimum(val=-5.50, tol=1e-2),
-    # outdir="./out",
-    # hot_start=True
-)  # see help(egor) for options
+    target=-5.50,  # known reference objective value
+)
 
-# Restrict regression and correlation models used
-# egor = egx.Egor(g24, xlimits_g24, n_cstr=n_cstr_g24, n_doe=10,
-#                      regr_spec=egx.RegressionSpec.LINEAR,
-#                      corr_spec=egx.CorrelationSpec.MATERN32 | egx.CorrelationSpec.MATERN52)
-
-res = egor.minimize(max_iters=30)
+res = egor.minimize(g24, max_iters=30)
 print(f"Optimization f={res.y_opt} at {res.x_opt}")