connectivity constraint tutorial (#2364)

Co-authored-by: Mo Chen <[email protected]>
NanoComp · Jan 5, 2023 · 823d9aa · 823d9aa
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diff --git a/doc/docs/Python_Tutorials/Adjoint_Solver.md b/doc/docs/Python_Tutorials/Adjoint_Solver.md
@@ -8,7 +8,7 @@ Meep contains an adjoint-solver module for efficiently computing the gradient of
 
 This interface to this functionality is implemented entirely in Python using [autograd](https://github.com/HIPS/autograd) and [JAX](https://github.com/google/jax). The adjoint solver supports inverse design and [topology optimization](https://en.wikipedia.org/wiki/Topology_optimization) by providing the functionality to wrap an optimization library around the gradient computation.
 
-There are six Jupyter notebooks that demonstrate the main features of the adjoint solver.
+There are seven Jupyter notebooks that demonstrate the main features of the adjoint solver.
 
 - [Introduction](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/01-Introduction.ipynb)
 
@@ -22,4 +22,6 @@ There are six Jupyter notebooks that demonstrate the main features of the adjoin
 
 - [Near2Far Optimization with Epigraph Formulation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb)
 
+- [Connectivity Constraint](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/07-Connectivity-Constraint.ipynb)
+
 More documentation will be available soon.