analisis-numerico-computo-cientifico
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ITAM-DS
Análisis numérico y cómputo científico
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
Circuitscape borrows algorithms from electronic circuit theory to predict connectivity in heterogeneous landscapes. Landscapes are represented as conductive surfaces, with low resistances assigned to landscape features types that are most...
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
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)...