Clarify the support for new optimizer

Author: metab0t

docs/source/faq.md

@@ -35,3 +35,22 @@ model = gurobi.Model(env)
 In YALMIP, you can use the matrix form $Ax \leq b$ to add linear constraints, which is quite convenient.
 
 In PyOptInterface, you can use [`model.add_m_linear_constraints`](<project:#model.add_m_linear_constraints>) to add linear constraints in matrix form.
+
+## Will PyOptInterface support new optimizers in the future?
+
+In short, no, there are no plans to support new optimizers. Supporting a new optimizer is not a trivial task, as it requires a lot of work to implement, test and maintain the interface.
+
+Basically, a new optimizer should satisfy the following criteria to be considered for support:
+
+- Actively maintained
+- Good performance (open source or commercial)
+- Not difficult to acquire an academic license
+- Have well-defined C/C++ API
+
+Support for a new optimizer will only happen if one or more of the following conditions are met:
+
+- I am personally interested in the optimizer and plan to use it in my research, so I am willing to invest time in implementing it.
+- Someone steps up to implement and maintain the interface for the optimizer in PyOptInterface.
+- External funding or sponsorship become available to support the development and maintenance of the optimizer interface.
+
+Finally, we are always open to external contributions. If you have a specific optimizer in mind and plan to implement it, feel free to open an issue on our GitHub repository to discuss it.