Documentation for adding new problems in contributing.md

docs/src/contributing.md

@@ -72,3 +72,40 @@ function function_name(; n::Int = default_nvar, type::Type{T} = Float64, kwargs.
   return nlp
 end
 ```
+
+## Validating new problems
+
+* Ensure all meta fields are accurate and complete.
+* For problem implementation in both ADNLP and PureJuMP, use the same initial point, variable bounds, constraint bounds and ensure objective and constraint values match within a relative tolerance.
+* The objective of implementations must be callable at the starting point and should not return NaN unless expected.
+* Problems modeled with `ADNLPModels` should support the `nls=true/false` keyword to allow both `ADNLPModel` and `ADNLSModel` instantiation from the same problem.
+* For least-squares problems, instantiate both `ADNLPModel` and `ADNLSModel` and ensure `residual!(nls, x, Fx)` is allocation-free with the objectives agree (or differ by a factor of 2 for LS).
+* For variable-size problems, verify that different values of `n` produce correct `nvar`, meta formulas predict actual values and instantiation works at various sizes.
+
+## Reviewer Checklist for New Problems
+
+**Meta**
+- [ ] Every new problem (ADNLP or PureJuMP) is registered in Meta, with all fields (origin, objtype, contype, bounds, best-known, etc.) filled correctly.
+- [ ] Meta formulas for variable sizes match actual model behavior.
+
+**Definition**
+- [ ] No extra or spurious exports are introduced.
+- [ ] Model name matches the file and function name.
+
+**Implementation**
+- [ ] Objective and constraint values agree within tolerance at test points.
+- [ ] Number of variables and constraints match.
+
+**Sanity**
+- [ ] Objective is callable at the starting point and does not return NaN (unless documented).
+- [ ] Model instantiates without error for all supported types (Float32, Float64).
+- [ ] For scalable problems, changing n updates nvar and all related meta fields.
+
+**Least-Squares & In-Place APIs**
+- [ ] If least-squares, ADNLP constructor supports `nls=true/false` for both ADNLPModel and ADNLSModel.
+- [ ] In-place nonlinear constraint evaluation (`nln!`) and least-squares residuals (`residual!`) are allocation-free.
+- [ ] For least-squares, objectives for NLP and NLS agree (or differ by a factor of 2, as appropriate).
+
+**Zero-Allocation**
+- [ ] All in-place APIs (constraints, residuals) are allocation-free (Julia ≥ 1.7).
+- [ ] No unnecessary allocations in tight loops or callbacks.