Minor update, img contourf.jpg

Author: kernc

contourf.jpg

@@ -512,7 +512,7 @@ <h3>Benchmark</h3>
                 of most common optimization algorithm implementations
                 on several popular global optimization functions, including a few multi-dimensional (2–10D),
                 <b><cite>SAMBO</cite> more often converges to correct global optimum,
-                    in fewest objective evaluations,
+                    in fewest total objective evaluations,
                     yielding smallest absolute error,
                     with runtime just as fast as that of the best</b>.
             </p>
@@ -529,8 +529,8 @@ <h3>Benchmark</h3>
                 <tbody>
                 <tr class="significant"><td>sambo.minimize(shgo)</td><td>92%</td><td>129</td><td>1</td><td>0.04</td></tr>
                 <tr class="significant"><td>sambo.minimize(sceua)</td><td>92%</td><td>548</td><td>1</td><td>0.24</td></tr>
-                <tr><td>direct</td><td>92%</td><td>1389</td><td>1</td><td>0.03</td></tr>
-                <tr><td>dual_annealing</td><td>92%</td><td>6459</td><td>1</td><td>0.84</td></tr>
+                <tr><td>direct †</td><td>92%</td><td>1389</td><td>1</td><td>0.03</td></tr>
+                <tr><td>dual_annealing †</td><td>92%</td><td>6459</td><td>1</td><td>0.84</td></tr>
                 <tr><td>differential_evolution</td><td>83%</td><td>13961</td><td>1</td><td>2.34</td></tr>
                 <tr class="significant"><td>sambo.minimize(smbo)</td><td>75%</td><td>476</td><td>2</td><td>44.68</td></tr>
                 <tr><td>hyperopt</td><td>75%</td><td>938</td><td>2</td><td>18.26</td></tr>
@@ -540,12 +540,12 @@ <h3>Benchmark</h3>
                 <tr><td>shgo</td><td>67%</td><td>243</td><td>11</td><td>0.11</td></tr>
                 <tr><td>SLSQP</td><td>67%</td><td>266</td><td>11</td><td>0.12</td></tr>
                 <tr><td>Nelder-Mead</td><td>67%</td><td>301</td><td>15</td><td>0.03</td></tr>
-                <tr><td>Powell</td><td>67%</td><td>324</td><td>16</td><td>0.02</td></tr>
+                <tr><td>Powell †</td><td>67%</td><td>324</td><td>16</td><td>0.02</td></tr>
                 <tr><td>COBYQA</td><td>58%</td><td>134</td><td>8</td><td>0.54</td></tr>
-                <tr><td>TNC</td><td>58%</td><td>232</td><td>16</td><td>0.04</td></tr>
+                <tr><td>TNC †</td><td>58%</td><td>232</td><td>16</td><td>0.04</td></tr>
                 <tr><td>trust-constr</td><td>58%</td><td>1052</td><td>8</td><td>2.08</td></tr>
                 <tr><td>basinhopping</td><td>58%</td><td>3383</td><td>21</td><td>1.15</td></tr>
-                <tr><td>CG</td><td>50%</td><td>414</td><td>20</td><td>0.02</td></tr>
+                <tr><td>CG †</td><td>50%</td><td>414</td><td>20</td><td>0.02</td></tr>
                 </tbody>
                 <tfoot style="font-size: small;">
                 <tr><th colspan="5">† Non-constrained method; constrained by patching the objective function s.t.<br>
@@ -574,7 +574,7 @@ <h3>Benchmark</h3>
                 <tr><th colspan="5">∗ The following implementations were considered:
                     <ul><li>too slow: Open-Box, AMPGO,</li>
                         <li>too complex: SMT, HyperBO, DEAP, PyMOO, OSQP, Optuna.</li></ul>
-                    &nbsp;&nbsp;&nbsp;&nbsp;To consider: jdb78/LIPO. Contributions welcome.</th></tr>
+                    &nbsp;&nbsp;&nbsp;&nbsp;Maybe to consider: jdb78/LIPO. Contributions welcome.</th></tr>
                 </tfoot>
             </table>
         </div>
@@ -596,6 +596,7 @@ <h3>Benchmark</h3>
         </script>
     </div>
 </section>
+<img src="contourf.jpg" loading="lazy" style="margin:3em 0 0 0; opacity: .8"/>
 
 <section id="citing">
     <h3>Citation</h3>