Create 2024-11-17-Comparing-solvers-in-excel.md

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+---
+layout: post
+title: Comparing Default Solvers in Excel
+subtitle: Simplex is more likely to be the best option.
+categories: operational-reasearch
+excerpt_image: "assets/images/posts/post-image/masiac art/404.jpg"
+
+tags: [book, return, fixed-income]
+---
+![banner](/assets/images/posts/post-image/masiac art/404.jpg)
+
+Images: kenny-eliason (unsplash.com)
+## **Simplex Method**
+
+- Used for linear optimization problems
+- Fastest and most reliable for linear models
+- Guaranteed to find the global optimum for linear problems
+- Only works when the Assume Linear Model box is checked[1](https://www.solver.com/standard-excel-solver-limitations-nonlinear-optimization)
+
+## **GRG (Generalized Reduced Gradient) Nonlinear Method**
+
+- Used for nonlinear optimization problems
+- Can handle smooth, continuous nonlinear functions
+- May find local optima rather than global optima
+- Slower than Simplex but faster than Evolutionary
+- Default method when Assume Linear Model is unchecked[1](https://www.solver.com/standard-excel-solver-limitations-nonlinear-optimization)
+- Works well for problems with continuous derivatives
+
+## **Evolutionary Method**
+
+- Used for non-smooth or discontinuous problems
+- Can handle discrete variables and non-smooth functions
+- Slowest of the three methods
+- May not find the global optimum, but can often find good solutions for complex problems
+- Useful when other methods fail due to discontinuities or discrete variables
+
+## **Key Considerations**
+
+- **Problem Type**: Linear problems should use Simplex. Nonlinear but smooth problems can use GRG Nonlinear. Complex or discontinuous problems may require Evolutionary.
+- **Solution Quality**: Simplex guarantees global optima for linear problems. GRG may find local optima. Evolutionary is less precise but can handle more complex problems.
+- **Computation Time**: Simplex is fastest, followed by GRG, with Evolutionary typically being the slowest.
+- **Starting Points**: GRG and Evolutionary methods can benefit from multiple starting points to increase the chance of finding the global optimum[1](https://www.solver.com/standard-excel-solver-limitations-nonlinear-optimization).
+- **Constraints**: All methods can handle constraints, but their effectiveness may vary depending on the problem structure.
+
+To get the best results, it's often recommended to:
+
+1. Use Simplex for linear problems
+2. Try GRG Nonlinear first for smooth nonlinear problems
+3. Use Evolutionary as a last resort for complex, discontinuous problems
+4. Experiment with different starting points, especially for GRG Nonlinear[1](https://www.solver.com/standard-excel-solver-limitations-nonlinear-optimization)
+5. Consider using the MultiStart option with GRG Nonlinear for global optimization[2](https://www.solver.com/excel-solver-change-options-grg-nonlinear-solving-method)
+
+By understanding the strengths and limitations of each method, you can choose the most appropriate solver for your specific optimization problem in Excel.
+
+### Additional Learning Resource to check out
+
+Some Solver resources
+[Frontier Solver](https://www.solver.com/solver-tutorial-using-solver) 
+They have a lot of template files with case studies to help learning
+
+[Microsoft Offical Documentation](https://support.microsoft.com/en-gb/office/load-the-solver-add-in-in-excel-612926fc-d53b-46b4-872c-e24772f078ca)
+
+[Microsoft Offical Tutorial](https://support.microsoft.com/en-gb/office/define-and-solve-a-problem-by-using-solver-5d1a388f-079d-43ac-a7eb-f63e45925040)
+
+[Open Solver](https://opensolver.org/)
+Alternative solver solution with advanced models.
+
+