fix formatting

docs/src/optimization.md

@@ -26,8 +26,7 @@ PolynomialAveraging
 [^DCAMHV2020]: Dhaka, A. K., Catalina, A., Andersen, M. R., Magnusson, M., Huggins, J., & Vehtari, A. (2020). Robust, accurate stochastic optimization for variational inference. Advances in Neural Information Processing Systems, 33, 10961-10973.
 [^KMJ2024]: Khaled, A., Mishchenko, K., & Jin, C. (2023). Dowg unleashed: An efficient universal parameter-free gradient descent method. Advances in Neural Information Processing Systems, 36, 6748-6769.
 [^IHC2023]: Ivgi, M., Hinder, O., & Carmon, Y. (2023). Dog is sgd's best friend: A parameter-free dynamic step size schedule. In International Conference on Machine Learning (pp. 14465-14499). PMLR.
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-## Operators 
+## Operators
 
 Depending on the variational family, variational objective, and optimization strategy, it might be necessary to modify the variational parameters after performing a gradient-based update.
 For this, an operator acting on the parameters can be supplied via the  `operator` keyword argument of `optimize`.