from cvxopt import matrix

A = matrix(1, (1,4))

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# Author: monghim.ng

## CvxOpt Notes

## Finding Gradient by Inspecting First Order Expansion

## Git Notes

## PDB Note

## Movement

from cvxopt import matrix

A = matrix(1, (1,4))

covariance matrix and interpretation

matrix multiplication different view

matrix calculus:

scalar value function of scalar

vector, scalar value function of vector

matrix, vector, scalar value function of matrix

http://mplab.ucsd.edu/tutorials/MatrixRecipes.pdf

derivative, gradient, hessian, as related to calculus 1

evolution of projection onto a vector, onto a subspace, onto an affine space, onto hyperplane -> least square -> variants of least square

alternately -> minimum norm problem (how it is projecting 0 onto the subspace)

git diff old_commit new_commit [– files]

git log –oneline –graph –decorate –all

w(here) = print stack trace

d(own) = move down a frame

u(p) = move up a frame

b = list all breakpoints

b fileName:55 = set breakpoint in file fileName at line 55

b fileName:my_func

b 55, where a == 6

tbreak = temporary breakpoint = removed when first hit

c(lear) = clear all breakpoints

c fileName:lineNum = clear that line’s breakpoint

enable bnumber

disable bnumber

s(tep)

n(ext)

**j(ump) # = jump to that line**

unt(il) line#

r(eturn) = until current frame exits

c(ontinue) = until you hit a breakpoint

**pdb.set_trace() **= your code can break into the pdb