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Update docs dependencies; re-run notebooks

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Alinson S. Xavier
2025-06-10 12:28:39 -05:00
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docs/guide/primal.ipynb
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@@ -15,7 +15,7 @@
"\n",
"Before presenting the primal components themselves, we briefly discuss the three ways a solution may be provided to the solver. Each approach has benefits and limitations, which we also discuss in this section. All primal components can be configured to use any of the following approaches.\n",
"\n",
"The first approach is to provide the solution to the solver as a **warm start**. This is implemented by the class [SetWarmStart](SetWarmStart). The main advantage is that this method maintains all optimality and feasibility guarantees of the MIP solver, while still providing significant performance benefits for various classes of problems. If the machine learning model is able to predict multiple solutions, it is also possible to set multiple warm starts. In this case, the solver evaluates each warm start, discards the infeasible ones, then proceeds with the one that has the best objective value. The main disadvantage of this approach, compared to the next two, is that it provides relatively modest speedups for most problem classes, and no speedup at all for many others, even when the machine learning predictions are 100% accurate.\n",
"The first approach is to provide the solution to the solver as a **warm start**. This is implemented by the class [SetWarmStart][SetWarmStart]. The main advantage is that this method maintains all optimality and feasibility guarantees of the MIP solver, while still providing significant performance benefits for various classes of problems. If the machine learning model is able to predict multiple solutions, it is also possible to set multiple warm starts. In this case, the solver evaluates each warm start, discards the infeasible ones, then proceeds with the one that has the best objective value. The main disadvantage of this approach, compared to the next two, is that it provides relatively modest speedups for most problem classes, and no speedup at all for many others, even when the machine learning predictions are 100% accurate.\n",
"\n",
"[SetWarmStart]: ../../api/components/#miplearn.components.primal.actions.SetWarmStart\n",
"\n",