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) ((arreglo)->arr)
#define entrada(arreglo,i,j) ((arreglo)->arr[j*m+i]) //almacenamos column major
}arreglo_2d;
typedef arreglo_2d *arreglo_2d_T;

Snippet from:
https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/tree/master/C/BLAS/ejemplos
https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/tree/master/C/LAPACK/ejemplos
https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/tree/master/C/extensiones_a_C/MPI/openMPI/ejemplos/3_openMPI_y_BLAS

To avoid this define arreglo_2d as:

//arreglo2d:
typedef struct{
int m, n;
#define renglones(arreglo) ((arreglo)->m)
#define columnas(arreglo) ((arreglo)->n)
double *arr;
#define entradas(arreglo) ((arreglo)->arr)
#define entrada(arreglo,i,j) ((arreglo)->arr[j*renglones(arreglo)+i]) //almacenamos column major
}arreglo_2d;
typedef arreglo_2d *arreglo_2d_T;

So I have to run examples of BLAS, LAPACK and BLAS-OPENMPI to check everything is ok¡

Suggestions:

• Start mentioning this is the repo of the course X at ITAM by Erick Palacios

• Link to Temario should be larger and should be at the beginning.

• Explain how to select a branch with an image like this:

• Add Author to notes in dropbox

• Add "Índice de notas" as a title before the notes, otherwise the intuitive thing is to think that is a Temario and the difference ain't clear.

Debido a que el equipo 6 trabajará con el equipo 8 y el equipo 5 lo hará de forma separada, es necesario renombrar las carpetas ubicadas en:
https://github.com/ITAM-DS/analisis-numerico-computo-cientifico/tree/mno-2020-1/proyecto_final/proyectos/equipos

Your issue content here

The text states in the chapter Expresiones de los errores para las reglas compuestas del rectángulo, trapecio y Simpson that the Trapezoidal rule is $\bigO(h^2)$ when in reality is $\bigO(h^3)$

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 permeable to movement or best promote gene flow, and high resistances assigned to movement barriers. Effective resistances, current flow, and voltages calculated across the landscapes can then be related to ecological processes, such as individual movement and gene flow. More detail about the underlying model, its parameterization, and potential applications in ecology, evolution, and conservation planning can be found in McRae (2006) and McRae et al. (2008).

Circuitscape was originally designed to analyze connectivity across raster grids. With version 4.0, it can now analyze arbitrary networks (graphs) with any set of connections between nodes the user specifies.

It was written in python, but due to computational constrains they are developing a new version in julia. Could be a cool example.

https://circuitscape.org/

https://github.com/Circuitscape/Circuitscape.jl

Use

• for bash:

/usr/bin/time --verbose 

perf stat -e cycles,stalled-cycles-frontend,stalled-cycles-backend,instructions,\
cache-references,cache-misses,branches,branch-misses,task-clock,faults,\
minor-faults,cs,migrations -r 3

• for Python:

#cProfile:
python -m cProfile -s cumulative
python -m cProfile -o profile.stats
pip install runsnake
#lineprofiler:
pip install line_profiler
kernprof.py -l -v
#memoryprofiler:
pip install memory_profiler
pip install psutil
python -m memory_profiler
#magic commands:
%memit
%timeit
#heapy
pip install guppy

• for C: gdb, valgrind

• for R: lineprof, profvis

• for cuda: cuda-gdb

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

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

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

and:

Feasible initial point

But for the infeasible initial point, need to fix some errors (execute 2nd example using [-4,-4] as x_0):

Infeasible initial point

which is using: algoritmos/Python dir.

Examples of tests for this case of infeasible inital point are in:

Infeasible inital point 1st version

Infeasible inital point 2nd version

Need to check which one is going to be the one that I will use.... 1st or 2nd version....

Get some examples to test functionality of algorithms for ceco in particular for line: 55

Also check CO of S. Boyd & L. Vandenberghe for implementation tips when solving Newtons system: dir desc

And compute Newton's decrement as algorithms for uco

In line 48 of dir algorithms for unconstrained opt Newton's decrement is computed after Newton's direction is defined as:

dir_Newton = Hfeval\(-gfeval);

better if:

dir_Newton = Hfeval\(gfeval);
lambda_cuadrada = dir_Newton'*(Hfeval*dir_Newton);
dir_Newton = - dir_Newton

See:

4.2.Metodo_de_Newton.ipynb