Erick Palacios Moreno

Julia, CVXPy, Jupyter notebooks examples: http://www.juliaopt.org/Convex.jl/stable/examples/portfolio_optimization/portfolio_optimization2/ https://colab.research.google.com/github/cvxpy/cvxpy/blob/master/examples/notebooks/WWW/mTSP_en.ipynb https://www.cvxpy.org/examples/basic/linear_program.html https://www.cvxpy.org/examples/applications/MM.html?highlight=multiply https://github.com/cvxgrp/pymde/tree/main/examples https://github.com/cvxgrp/pymde https://github.com/cvxgrp/cvx_short_course/blob/master/exercises/13.20.ipynb https://github.com/cvxgrp/cvx_short_course/blob/master/exercises/Lasso.ipynb https://www.cvxpy.org/examples/applications/tv_inpainting.html https://nbviewer.jupyter.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/tv_inpainting.ipynb https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv https://stanford.edu/~boyd/papers/cvx_short_course.html https://nbviewer.jupyter.org/github/cvxgrp/cvx_short_course/blob/master/applications/portfolio_optimization.ipynb https://nbviewer.jupyter.org/github/cvxgrp/cvx_short_course/blob/master/intro/SVM.ipynb https://github.com/cvxgrp https://www.cvxpy.org/tutorial/dcp/index.html?highlight=norm https://www.cvxpy.org/examples/index.html#basic https://www.cvxpy.org/examples/basic/least_squares.html https://colab.research.google.com/github/cvxgrp/cvx_short_course/blob/master/applications/model_fitting.ipynb Kubernetes https://www.katacoda.com/courses/kubernetes/playground https://kubernetes.io/es/docs/tutorials/kubernetes-basics/create-cluster/cluster-interactive/ https://kubernetes.io/es/docs/tutorials/hello-minikube/

As jupyter book now uses `off` execution for reducing building time https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/blob/0525b254e9524034a6ac03ec3da8b30d2f9ba433/libro_optimizacion/temas/_config.yml#L21 notebooks need to be tested in some way. One possibility is via: https://github.com/treebeardtech/nbmake-action Another: https://github.com/nteract/papermill

Convex inequality and equality optimization problems were almost finished. See: [algorithms for cieco](https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/blob/master/temas/IV.optimizacion_convexa_y_machine_learning/algoritmos/Python/algorithms_for_cieco.py) and: [Feasible initial point](https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/blob/master/temas/IV.optimizacion_convexa_y_machine_learning/4.6.Metodo_de_BL_para_puntos_iniciales_factibles_Python.ipynb) But for the infeasible initial point, need to fix some errors (execute 2nd...

New algorithms for both Python and R were developed. Old Matlab implementation needs an update. Dirs in: https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/tree/master/temas/IV.optimizacion_convexa_y_machine_learning/algoritmos/Matlab_old/algoritmos

Change definition of arreglo_2d for handling of 2-d arrays

2

This definition of arreglo_2d needs that m is defined somewhere in the code: ``` //arreglo2d: typedef struct{ int m, n; #define renglones(arreglo) ((arreglo)->m) #define columnas(arreglo) ((arreglo)->n) double *arr; #define entradas(arreglo)...

Change assignments regarding linear system of eq and least squares

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Change exercises regarding module creation of: * `solve_linear_system_of_equations.py` in [1_ecuaciones_lineales.ipynb](https://github.com/ITAM-DS/Propedeutico/blob/main/Python/clases/3_algebra_lineal/1_ecuaciones_lineales.ipynb) * `utils.py` in [3_minimos_cuadrados.pynb](https://github.com/ITAM-DS/Propedeutico/blob/main/Python/clases/3_algebra_lineal/3_minimos_cuadrados.ipynb) as their implementations are almost equal. Maybe consider to implement forward, backward substitutions instead of using...

- [ ] Add note of common distributions part 2 (t, F, chi squared) - [ ] Add confidence intervals info in [3_elementos_de_inferencia](https://github.com/ITAM-DS/Propedeutico/blob/master/R/clases/3_estadistica/3_elementos_de_inferencia.ipynb) - [ ] Add more info in...