Peddi Anurag AnuragAnalog

## optimize_using_or.py
# Getting started:
# Python3 installation: python -m pip install --upgrade --user ortools
#
# Optimisation with the OR tool can be done in 5 steps:
# Creating a solver
# Creating a variable (x, y, … etc.)
# Creating linear constraints
# Creating an objective function
# Run the solver
#

## bisection.py
#  Bisection method which is also known as bolzano method is based on the repeated application of intermediate value property.
#  Let the function f(x) be continous between a and b. For definiteness, let f(a) be (-)ve and f(b) be (+)ve. Then the first approximation to the root is x1 = (a+b)/2.
#  If f(x1)=0, then x1 is a root of f(x) = 0, otherwise, the root lies between a
#  and x1 or x1 and baccording to f(x1) is (+)ve or (-)ve. Then we bisect the
#  interval as before and continue the process until the root is found to the desird accuracy.

#!/usr/bin/python3.6

def f(x):
    fx = 3*x**2-15*x+7

## bisection.c
/********* Bisection method which is also known as bolzano method is based on the repeated application of intermediate value property.
   Let the function f(x) be continous between a and b. For definiteness, let f(a) be (-)ve and f(b) be (+)ve. Then the first approximation to the root is x1 = (a+b)/2.
   If f(x1)=0, then x1 is a root of f(x) = 0, otherwise, the root lies between a
 and x1 or x1 and baccording to f(x1) is (+)ve or (-)ve. Then we bisect the inte
rval as before and continue the process until the root is found to the desird ac
curacy. *********/

/*************** PROGRAM STARTS HERE ***************/
#include <stdio.h>
#include <stdlib.h>

## chebysav.py
#!/usr/bin/python3.6

# Chebysav method is like approimating the given Transcedental Equation into a quadratic equation f(x) = 0, f(x) ~ a0 + a1x + a2x^2
#
# Let xk be an approximate root
# f'(x) = a1 + a2x
# f''(x) = 2a2  by subsituting the value xk in all the equations we get the values of f(xk), f'(xk), f''(xk)

# we get,
#      f(x) ~ fk + (x-xk)f'_k + (x-xk)^2*f''_k/2 ==> 0

## chebysav.c
/******* Chebysav method is like approimating the given Transcedental Equation into a quadratic equation f(x) = 0, f(x) ~ a0 + a1x + a2x^2

Let xk be an approximate root
f'(x) = a1 + a2x
f''(x) = 2a2  by subsituting the value xk in all the equations we get the values of f(xk), f'(xk), f''(xk)

we get,
      f(x) ~ fk + (x-xk)f'_k + (x-xk)^2*f''_k/2 ==> 0
      x_(k+1) = xk - fk/f'k - (fk^2 f''_k)/2((f'_k)^3) *********/

## regula-falsi.c
/* The bisection method guarantees that the iterative process will converge. It is,
however, slow. Thus, attempts have been made to speed up** the bisection
method retaining its guaranteed convergence. A method of doing this is called
the method of false position.
It is sometimes known as the method of linear interpolation.
This is the oldest method for finding the real roots of a numerical equation
and closely resembles the bisection method.
In this method, we choose two points x0 and x1 such that f(x0) and f(x1) are
of opposite signs. Since the graph of y = f(x) crosses the X-axis between these
two points, a root must lie in between these points.

## muller.c
/* In Muller, f(x) is approximated by a second degree curve in the vicinity of
a root. The roots of the quadratic are then assumed to be the approximations to
the roots of the equation f(x) = 0.
The method is iterative, converges almost quadratically, and can be used
to obtain complex roots. */

/*************** PROGRAM STARTS HERE ***************/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>

## free_resume.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                AnuragAnalog
                / free_resume.md
            
            
              Last active
              July 6, 2022 03:30
            
              
                A list of curated websites where you can build you resumes for free of cost
              
          
    Resume


If you find any nice and cool resume builder's or templates, feel free to add here

Free Resume Builders


https://novoresume.com/
https://flowcv.io/
https://www.canva.com/resumes/templates/


## newton_raphson.c
/* This method is generally used to improve the result obtained by one of the
previous methods. Let x0 be an approximate root of f(x) = 0 and let x1 = x0 + h be
the correct root so that f(x1) = 0.
Expanding f(x0 + h) by Taylor’s series, we get
          f(x0) + hf′(x0) + h2/2! f′′(x0) + ...... = 0
Since h is small, neglecting h2 and higher powers of h, we get
          f(x0) + hf′(x0) = 0 or h = – f(x0)/f'(x0)
A better approximation than x0 is therefore given by x1, where
          x1 = x0 - f(x0)/f'(x0)
Successive approximations are given by x2, x3, ....... , xn + 1, where

## newton_raphson.py
#!/usr/bin/python3.6

# This method is generally used to improve the result obtained by one of the
# previous methods. Let x0 be an approximate root of f(x) = 0 and let x1 = x0 + h be
# the correct root so that f(x1) = 0.
# Expanding f(x0 + h) by Taylor’s series, we get
#          f(x0) + hf′(x0) + h2/2! f′′(x0) + ...... = 0
# Since h is small, neglecting h2 and higher powers of h, we get
#          f(x0) + hf′(x0) = 0 or h = – f(x0)/f'(x0)
# A better approximation than x0 is therefore given by x1, where
	# Getting started:
	# Python3 installation: python -m pip install --upgrade --user ortools
	#
	# Optimisation with the OR tool can be done in 5 steps:
	# Creating a solver
	# Creating a variable (x, y, … etc.)
	# Creating linear constraints
	# Creating an objective function
	# Run the solver
	#
	# Bisection method which is also known as bolzano method is based on the repeated application of intermediate value property.
	# Let the function f(x) be continous between a and b. For definiteness, let f(a) be (-)ve and f(b) be (+)ve. Then the first approximation to the root is x1 = (a+b)/2.
	# If f(x1)=0, then x1 is a root of f(x) = 0, otherwise, the root lies between a
	# and x1 or x1 and baccording to f(x1) is (+)ve or (-)ve. Then we bisect the
	# interval as before and continue the process until the root is found to the desird accuracy.

	#!/usr/bin/python3.6

	def f(x):
	fx = 3x2-15x+7
	/********* Bisection method which is also known as bolzano method is based on the repeated application of intermediate value property.
	Let the function f(x) be continous between a and b. For definiteness, let f(a) be (-)ve and f(b) be (+)ve. Then the first approximation to the root is x1 = (a+b)/2.
	If f(x1)=0, then x1 is a root of f(x) = 0, otherwise, the root lies between a
	and x1 or x1 and baccording to f(x1) is (+)ve or (-)ve. Then we bisect the inte
	rval as before and continue the process until the root is found to the desird ac
	curacy. *********/

	/************* PROGRAM STARTS HERE *************/
	#include <stdio.h>
	#include <stdlib.h>
	#!/usr/bin/python3.6

	# Chebysav method is like approimating the given Transcedental Equation into a quadratic equation f(x) = 0, f(x) ~ a0 + a1x + a2x^2
	#
	# Let xk be an approximate root
	# f'(x) = a1 + a2x
	# f''(x) = 2a2 by subsituting the value xk in all the equations we get the values of f(xk), f'(xk), f''(xk)

	# we get,
	# f(x) ~ fk + (x-xk)f'_k + (x-xk)^2*f''_k/2 ==> 0
	/******* Chebysav method is like approimating the given Transcedental Equation into a quadratic equation f(x) = 0, f(x) ~ a0 + a1x + a2x^2

	Let xk be an approximate root
	f'(x) = a1 + a2x
	f''(x) = 2a2 by subsituting the value xk in all the equations we get the values of f(xk), f'(xk), f''(xk)

	we get,
	f(x) ~ fk + (x-xk)f'_k + (x-xk)^2*f''_k/2 ==> 0
	x_(k+1) = xk - fk/f'k - (fk^2 f''_k)/2((f'_k)^3) *********/
	/* The bisection method guarantees that the iterative process will converge. It is,
	however, slow. Thus, attempts have been made to speed up** the bisection
	method retaining its guaranteed convergence. A method of doing this is called
	the method of false position.
	It is sometimes known as the method of linear interpolation.
	This is the oldest method for finding the real roots of a numerical equation
	and closely resembles the bisection method.
	In this method, we choose two points x0 and x1 such that f(x0) and f(x1) are
	of opposite signs. Since the graph of y = f(x) crosses the X-axis between these
	two points, a root must lie in between these points.
	/* In Muller, f(x) is approximated by a second degree curve in the vicinity of
	a root. The roots of the quadratic are then assumed to be the approximations to
	the roots of the equation f(x) = 0.
	The method is iterative, converges almost quadratically, and can be used
	to obtain complex roots. */

	/************* PROGRAM STARTS HERE *************/
	#include <stdio.h>
	#include <stdlib.h>
	#include <math.h>
	/* This method is generally used to improve the result obtained by one of the
	previous methods. Let x0 be an approximate root of f(x) = 0 and let x1 = x0 + h be
	the correct root so that f(x1) = 0.
	Expanding f(x0 + h) by Taylor’s series, we get
	f(x0) + hf′(x0) + h2/2! f′′(x0) + ...... = 0
	Since h is small, neglecting h2 and higher powers of h, we get
	f(x0) + hf′(x0) = 0 or h = – f(x0)/f'(x0)
	A better approximation than x0 is therefore given by x1, where
	x1 = x0 - f(x0)/f'(x0)
	Successive approximations are given by x2, x3, ....... , xn + 1, where
	#!/usr/bin/python3.6

	# This method is generally used to improve the result obtained by one of the
	# previous methods. Let x0 be an approximate root of f(x) = 0 and let x1 = x0 + h be
	# the correct root so that f(x1) = 0.
	# Expanding f(x0 + h) by Taylor’s series, we get
	# f(x0) + hf′(x0) + h2/2! f′′(x0) + ...... = 0
	# Since h is small, neglecting h2 and higher powers of h, we get
	# f(x0) + hf′(x0) = 0 or h = – f(x0)/f'(x0)
	# A better approximation than x0 is therefore given by x1, where