Optimization. Please use Newton and bisection methods for your answer. The second picture is another example to help you with the codes. Please show the codes! Optimization: make the output as bigor as small as possible10.24 +1.4² +2.9z+1.5f(x) = sinsin (0.224 import mathimport numpy as npimport matplotlib.pyplot as pltmatplotlib inlinedef f(x):return 10*np.log(1/((pow (x, 2)+1))) *math.exp(-(pow(x, 2)+x))def plot_curve (numPoints, a,b):X = np.linspace(a, b, numPoints)Y = np.zeros (numPoints)for i in range (0, numPoints):f(x[i])Y[i]plt.figure(figsize=(8,16))=plt.plot(X,Y)

Step1/4 import math def f(x): a = (0.2*x)**4+(1.4*x)**2+2.9*x+1.5 return math.sin(1/a)…

Answered: f(x) = sin Optimization: make the…

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f(x) = sin Optimization: make the output as big or as small as possible 1 0.24 +1.42 +2.9z+1.5,

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

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Optimization. Please use Newton and bisection methods for your answer. The second picture is another example to help you with the codes. Please show the codes!

Optimization: make the output as big
or as small as possible
1
0.24 +1.4² +2.9z+1.5
f(x) = sin
sin (0.224

import math
import numpy as np
import matplotlib.pyplot as plt
matplotlib inline
def f(x):
return 10*np.log(1/((pow (x, 2)+1))) *math.exp(-(pow(x, 2)+x))
def plot_curve (numPoints, a,b):
X = np.linspace(a, b, numPoints)
Y = np.zeros (numPoints)
for i in range (0, numPoints):
f(x[i])
Y[i]
plt.figure(figsize=(8,16))
=
plt.plot(X,Y)

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