fix redirects

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: SebastianM-C

docs/src/optimization_packages/mathoptinterface.md

@@ -58,7 +58,7 @@ sol = solve(prob, Ipopt.Optimizer(); option_name = option_value, ...)
 #### KNITRO.jl (MathOptInterface)
 
   - [`KNITRO.Optimizer`](https://github.com/jump-dev/KNITRO.jl)
-  - The full list of optimizer options can be found in the [KNITRO Documentation](https://www.artelys.com/docs/knitro//3_referenceManual/callableLibraryAPI.html)
+  - The full list of optimizer options can be found in the [KNITRO Documentation](https://www.artelys.com/app/docs/knitro/3_referenceManual/callableLibraryAPI.html)
 
 #### Juniper.jl (MathOptInterface)
 

docs/src/optimization_packages/nlopt.md

@@ -1,6 +1,6 @@
 # NLopt.jl
 
-[`NLopt`](https://github.com/JuliaOpt/NLopt.jl) is Julia package interfacing to the free/open-source [`NLopt library`](http://ab-initio.mit.edu/nlopt) which implements many optimization methods both global and local [`NLopt Documentation`](https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/).
+[`NLopt`](https://github.com/jump-dev/NLopt.jl) is Julia package interfacing to the free/open-source [`NLopt library`](http://ab-initio.mit.edu/nlopt/) which implements many optimization methods both global and local [`NLopt Documentation`](https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/).
 
 ## Installation: OptimizationNLopt.jl
 
@@ -139,7 +139,7 @@ sol = solve(prob, NLopt.LD_LBFGS())
 
 ### Without Constraint Equations
 
-The following algorithms in [`NLopt`](https://github.com/JuliaOpt/NLopt.jl) are performing global optimization on problems without
+The following algorithms in [`NLopt`](https://github.com/jump-dev/NLopt.jl) are performing global optimization on problems without
 constraint equations. However, lower and upper constraints set by `lb` and `ub` in the `OptimizationProblem` are required.
 
 `NLopt` global optimizers which fall into this category are:
@@ -192,7 +192,7 @@ sol = solve(prob, NLopt.G_MLSL_LDS(), local_method = NLopt.LD_LBFGS(), maxtime =
 
 ### With Constraint Equations
 
-The following algorithms in [`NLopt`](https://github.com/JuliaOpt/NLopt.jl) are performing global optimization on problems with
+The following algorithms in [`NLopt`](https://github.com/jump-dev/NLopt.jl) are performing global optimization on problems with
 constraint equations. However, lower and upper constraints set by `lb` and `ub` in the `OptimizationProblem` are required.
 
 !!! note "Constraints with NLopt"

docs/src/optimization_packages/pycma.md

@@ -2,7 +2,7 @@
 
 [`PyCMA`](https://github.com/CMA-ES/pycma) is a Python implementation of CMA-ES and a few related numerical optimization tools. `OptimizationPyCMA.jl` gives access to the CMA-ES optimizer through the unified `Optimization.jl` interface just like any native Julia optimizer.
 
-`OptimizationPyCMA.jl` relies on [`PythonCall`](https://github.com/cjdoris/PythonCall.jl). A minimal Python distribution containing PyCMA will be installed automatically on first use, so no manual Python set-up is required.
+`OptimizationPyCMA.jl` relies on [`PythonCall`](https://github.com/JuliaPy/PythonCall.jl). A minimal Python distribution containing PyCMA will be installed automatically on first use, so no manual Python set-up is required.
 
 ## Installation: OptimizationPyCMA.jl
 

docs/src/optimization_packages/scipy.md

@@ -4,7 +4,7 @@
 
 !!! note
 
-    `OptimizationSciPy.jl` relies on [`PythonCall`](https://github.com/cjdoris/PythonCall.jl).  A minimal Python distribution containing SciPy will be installed automatically on first use, so no manual Python set-up is required.
+    `OptimizationSciPy.jl` relies on [`PythonCall`](https://github.com/JuliaPy/PythonCall.jl).  A minimal Python distribution containing SciPy will be installed automatically on first use, so no manual Python set-up is required.
 
 ## Installation: OptimizationSciPy.jl
 

docs/src/tutorials/certification.md

@@ -1,6 +1,6 @@
 # Using SymbolicAnalysis.jl for convexity certificates
 
-In this tutorial, we will show how to use automatic convexity certification of the optimization problem using [SymbolicAnalysis.jl](https://github.com/Vaibhavdixit02/SymbolicAnalysis.jl).
+In this tutorial, we will show how to use automatic convexity certification of the optimization problem using [SymbolicAnalysis.jl](https://github.com/SciML/SymbolicAnalysis.jl).
 
 This works with the `structural_analysis` keyword argument to `OptimizationProblem`. This tells the package to try to trace through the objective and constraints with symbolic variables (for more details on this look at the [Symbolics documentation](https://symbolics.juliasymbolics.org/stable/manual/functions/#function_registration)). This relies on the Disciplined Programming approach hence neccessitates the use of "atoms" from the SymbolicAnalysis.jl package.