note: code based on cvxpy package
my notes of optimization:

numerical optimization

convex optimization

Linear Search Methods:

Steepest Descent Method
Newton Method
Quasi-Newton Method
Damped-Newton Method
Conjugate Gradient Method
Matrix Util Method

Large-Scale Unconstrained Optimization:

Inexact Newton method

Calculating Derivatives:

Finite-Difference Derivative Approximations
Automatic Differentiation

StepLength:

{ Backtracking Line Search } Algorithm: BacktrackingLineSearch.py
{ Interpolation: Quadratic; Cubic} Algorithm: Interpolation.py
{ Zoom} Algorithm: Zoom.py
{ Wolfe Line Search-low dimension} Algorithm: WolfeLineSearch.py
{ Wolfe Line Search-high dimension} Algorithm: WolfeCondition.py

Steepest Descent:

{ Gradient Descent Method } Algorithm: GradientDescentMethod.py

Newton:

{ Newton Method } Algorithm: NewtonMethod.py
{ Cholesky with Added Multiple of the Identity } Algorithm: AddedMultipleOfTheIdentity.py

Quasi-Newton:

{ DFP Method } Algorithm: DFP.py
{ BFGS Method } Algorithm: BFGS.py

Damped-Newton:

{ Damped Newton Method } Algorithm: DampedNewtonMethod.py

Conjugate Gradient:

{ Conjugate Gradient Preliminary } Algorithm: CG_Preliminary.py
{ Conjugate Gradient } Algorithm: CG.py
{ Preconditioned Conjugate Gradient } Algorithm: Preconditioned_CG.py
{ Fletcher-Reeves methods } Algorithm: FR.py

MatrixUtil:

{ Cholesky Factorization: LDL^T} Algorithm: Cholesky_LDL.py

Inexact Newton method

{ Line Search Newton-CG } Algorithm: LineSearchNewton_CG.py
{ Limit memory-BFGS } Algorithm: L_BFGS.py

Finite-Difference:

{ Numerical Differentiation } Algorithm: NumericalDifferentiation.py

using CvxOpt or Cvxpy package demo:

{ CvxOpt Solve LP } Demo: CvxOptSolveLPDemo.py
{ Cvxpy Solve LP } Demo: CvxpySolveLPDemo.py
{ Cvxpy Solve NLP } Demo: CvxpySolveNLPDemo.py

Jorge Nocedal and Stephen J.Wright : Numerical optimization Second Edition

Stephen Boyd and Lieven vandenberghe: Convex optimization