test

docs/source/examples/07_PFR_MOO.ipynb

@@ -9,9 +9,13 @@
     "In this example, we will demonstrate how Bayesian Optimization can perform multi-objective optimization (MOO) and create a Pareto front. We will use the PFR model from [example 5](05_PFR_yield.ipynb). Two output variables will be generated from the model: yield (`y1`) and selectivity (`y2`). Unfortunately, these two variables cannot be maximized simultaneously. An increase in yield would lead to a decrease in selectivity, and vice versa. \n",
     "\n",
     "For this example, the scalarized objective can be written as,\n",
+    "\n",
     "$$ y = w_1 y_1 + w_2 y_2 $$\n",
+    "\n",
     "where the weights \n",
+    "\n",
     "$$ w_1, w_2 \\in [0, 1] $$\n",
+    "\n",
     "$$ w_1 + w_2 = 1 $$\n",
     "\n",
     "The details of this example is summarized in the table below:\n",