Differential Equations: Computing and Modeling (5th Edition), Edwards, Penney & Calvis
5th Edition
ISBN: 9780321816252
Author: C. Henry Edwards, David E. Penney, David Calvis
Publisher: PEARSON
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A particle of (mass= 4 g, charge%3 80 mC) moves in a region of space where the electric field is uniform and is given by E, =-2.5 N/C,
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We have learned the mid-point and trapezoidal rule for numercial intergration in the tutorials. Now you are asked to implement the Simpson rule, where we
approximate the integration of a non-linear curve using piecewise quadratic functions.
Assume f(x) is continuous over [a, b] . Let [a, b] be divided into N subintervals, each of length Ax, with endpoints at P = x0, x1, X2, ..., Xn,..., XN.
Each interval is Ax =
(b – a)/N.
The Simpon numerical integration rule is derived as:
N-2
Li f(x)dx =
* f(x0) + 4 (2n odd f(xn)) + 2 ( En=2,n even
N-1
f(x,) + f(xn)] .
Now complete the Python function InterageSimpson(N, a, b) below to implement this Simpson rule using the above equation.
The function to be intergrate is f (x) = 2x³ (Already defined, don't change it).
In [ ]: # Complete the function given the variables N,a,b and return the value as "TotalArea".
# Don't change the predefined content, only fill your code in the region "YOUR CODE"
from math import *
def InterageSimpson (N, a, b): # n is…
Chapter 2 Solutions
Differential Equations: Computing and Modeling (5th Edition), Edwards, Penney & Calvis
Ch. 2.1 - Prob. 1PCh. 2.1 - Prob. 2PCh. 2.1 - Prob. 3PCh. 2.1 - Prob. 4PCh. 2.1 - Prob. 5PCh. 2.1 - Prob. 6PCh. 2.1 - Prob. 7PCh. 2.1 - Prob. 8PCh. 2.1 - Prob. 9PCh. 2.1 - Prob. 10P
Ch. 2.1 - Prob. 11PCh. 2.1 - Prob. 12PCh. 2.1 - Prob. 13PCh. 2.1 - Prob. 14PCh. 2.1 - Prob. 15PCh. 2.1 - Prob. 16PCh. 2.1 - Prob. 17PCh. 2.1 - Prob. 18PCh. 2.1 - Prob. 19PCh. 2.1 - Prob. 20PCh. 2.1 - Prob. 21PCh. 2.1 - Suppose that at time t=0, half of a logistic...Ch. 2.1 - Prob. 23PCh. 2.1 - Prob. 24PCh. 2.1 - Prob. 25PCh. 2.1 - Prob. 26PCh. 2.1 - Prob. 27PCh. 2.1 - Prob. 28PCh. 2.1 - Prob. 29PCh. 2.1 - A tumor may be regarded as a population of...Ch. 2.1 - Prob. 31PCh. 2.1 - Prob. 32PCh. 2.1 - Prob. 33PCh. 2.1 - Prob. 34PCh. 2.1 - Prob. 35PCh. 2.1 - Prob. 36PCh. 2.1 - Prob. 37PCh. 2.1 - Fit the logistic equation to the actual U.S....Ch. 2.1 - Prob. 39PCh. 2.2 - Prob. 1PCh. 2.2 - Prob. 2PCh. 2.2 - Prob. 3PCh. 2.2 - Prob. 4PCh. 2.2 - Prob. 5PCh. 2.2 - Prob. 6PCh. 2.2 - Prob. 7PCh. 2.2 - Prob. 8PCh. 2.2 - Prob. 9PCh. 2.2 - Prob. 10PCh. 2.2 - Prob. 11PCh. 2.2 - Prob. 12PCh. 2.2 - Prob. 13PCh. 2.2 - Prob. 14PCh. 2.2 - Prob. 15PCh. 2.2 - Prob. 16PCh. 2.2 - Prob. 17PCh. 2.2 - Prob. 18PCh. 2.2 - Prob. 19PCh. 2.2 - Prob. 20PCh. 2.2 - Prob. 21PCh. 2.2 - Prob. 22PCh. 2.2 - Prob. 23PCh. 2.2 - Prob. 24PCh. 2.2 - Use the alternatives forms...Ch. 2.2 - Prob. 26PCh. 2.2 - Prob. 27PCh. 2.2 - Prob. 28PCh. 2.2 - Consider the two differentiable equation...Ch. 2.3 - The acceleration of a Maserati is proportional to...Ch. 2.3 - Prob. 2PCh. 2.3 - Prob. 3PCh. 2.3 - Prob. 4PCh. 2.3 - Prob. 5PCh. 2.3 - Prob. 6PCh. 2.3 - Prob. 7PCh. 2.3 - Prob. 8PCh. 2.3 - A motorboat weighs 32,000 lb and its motor...Ch. 2.3 - A woman bails out of an airplane at an altitude of...Ch. 2.3 - According to a newspaper account, a paratrooper...Ch. 2.3 - Prob. 12PCh. 2.3 - Prob. 13PCh. 2.3 - Prob. 14PCh. 2.3 - Prob. 15PCh. 2.3 - Prob. 16PCh. 2.3 - Prob. 17PCh. 2.3 - Prob. 18PCh. 2.3 - Prob. 19PCh. 2.3 - Prob. 20PCh. 2.3 - Prob. 21PCh. 2.3 - Suppose that =0.075 (in fps units, with g=32ft/s2...Ch. 2.3 - Prob. 23PCh. 2.3 - The mass of the sun is 329,320 times that of the...Ch. 2.3 - Prob. 25PCh. 2.3 - Suppose that you are stranded—your rocket engine...Ch. 2.3 - Prob. 27PCh. 2.3 - (a) Suppose that a body is dropped (0=0) from a...Ch. 2.3 - Prob. 29PCh. 2.3 - Prob. 30PCh. 2.4 - Prob. 1PCh. 2.4 - Prob. 2PCh. 2.4 - Prob. 3PCh. 2.4 - Prob. 4PCh. 2.4 - Prob. 5PCh. 2.4 - Prob. 6PCh. 2.4 - Prob. 7PCh. 2.4 - Prob. 8PCh. 2.4 - Prob. 9PCh. 2.4 - Prob. 10PCh. 2.4 - Prob. 11PCh. 2.4 - Prob. 12PCh. 2.4 - Prob. 13PCh. 2.4 - Prob. 14PCh. 2.4 - Prob. 15PCh. 2.4 - Prob. 16PCh. 2.4 - Prob. 17PCh. 2.4 - Prob. 18PCh. 2.4 - Prob. 19PCh. 2.4 - Prob. 20PCh. 2.4 - Prob. 21PCh. 2.4 - Prob. 22PCh. 2.4 - Prob. 23PCh. 2.4 - Prob. 24PCh. 2.4 - Prob. 25PCh. 2.4 - Prob. 26PCh. 2.4 - Prob. 27PCh. 2.4 - Prob. 28PCh. 2.4 - Prob. 29PCh. 2.4 - Prob. 30PCh. 2.4 - Prob. 31PCh. 2.5 - Prob. 1PCh. 2.5 - Prob. 2PCh. 2.5 - Prob. 3PCh. 2.5 - Prob. 4PCh. 2.5 - Prob. 5PCh. 2.5 - Prob. 6PCh. 2.5 - Prob. 7PCh. 2.5 - Prob. 8PCh. 2.5 - Prob. 9PCh. 2.5 - Prob. 10PCh. 2.5 - Prob. 11PCh. 2.5 - Prob. 12PCh. 2.5 - Prob. 13PCh. 2.5 - Prob. 14PCh. 2.5 - Prob. 15PCh. 2.5 - Prob. 16PCh. 2.5 - Prob. 17PCh. 2.5 - Prob. 18PCh. 2.5 - Prob. 19PCh. 2.5 - Prob. 20PCh. 2.5 - Prob. 21PCh. 2.5 - Prob. 22PCh. 2.5 - Prob. 23PCh. 2.5 - Prob. 24PCh. 2.5 - Prob. 25PCh. 2.5 - Prob. 26PCh. 2.5 - Prob. 27PCh. 2.5 - Prob. 28PCh. 2.5 - Prob. 29PCh. 2.5 - Prob. 30PCh. 2.6 - Prob. 1PCh. 2.6 - Prob. 2PCh. 2.6 - Prob. 3PCh. 2.6 - Prob. 4PCh. 2.6 - Prob. 5PCh. 2.6 - Prob. 6PCh. 2.6 - Prob. 7PCh. 2.6 - Prob. 8PCh. 2.6 - Prob. 9PCh. 2.6 - Prob. 10PCh. 2.6 - Prob. 11PCh. 2.6 - Prob. 12PCh. 2.6 - Prob. 13PCh. 2.6 - Prob. 14PCh. 2.6 - Prob. 15PCh. 2.6 - Prob. 16PCh. 2.6 - Prob. 17PCh. 2.6 - Prob. 18PCh. 2.6 - Prob. 19PCh. 2.6 - Prob. 20PCh. 2.6 - Prob. 21PCh. 2.6 - Prob. 22PCh. 2.6 - Prob. 23PCh. 2.6 - Prob. 24PCh. 2.6 - Prob. 25PCh. 2.6 - Prob. 26PCh. 2.6 - Prob. 27PCh. 2.6 - Prob. 28PCh. 2.6 - Prob. 29PCh. 2.6 - Prob. 30P
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- We have learned the mid-point and trapezoidal rule for numercial intergration in the tutorials. Now you are asked to implement the Simpson rule, where we approximate the integration of a non-linear curve using piecewise quadratic functions. Assume f(x) is continuous over [a, b] . Let [a, b] be divided into N subintervals, each of length Ax, with endpoints at P = x0, x1, x2,.. Xn,..., XN. Each interval is Ax = (b − a)/N. The equation for the Simpson numerical integration rule is derived as: f f(x) dx N-1 Ax [ƒ(x0) + 4 (Σ1,n odd f(xn)) ƒ(x₂)) + f(xx)]. N-2 + 2 (n=2,n even Now complete the Python function InterageSimpson (N, a, b) below to implement this Simpson rule using the above equation. The function to be intergrate is ƒ(x) = 2x³ (Already defined in the function, no need to change).arrow_forwardWe have learned the mid-point and trapezoidal rule for numercial intergration in the tutorials. Now you are asked to implement the Simpson rule, where we approximate the integration of a non-linear curve using piecewise quadratic functions. Assume f(x) is continuous over [a, b]. Let [a, b] be divided into N subintervals, each of length Ax, with endpoints at P = x0, x1,x2,..., X., XN. Each interval is Ax = (b − a)/N. The equation for the Simpson numerical integration rule is derived as: f f(x)dx ≈ [ƒ(x0) + 4 (EN-1,n odd S(x)) + 2 (Σ2²n even f(x)) + f(XN)]. Now complete the Python function InterageSimpson (N, a, b) below to implement this Simpson rule using the above equation. The function to be intergrate is f(x) = 2x³ (Already defined in the function, no need to change). *Complete the function given the variables N, a,b and return the value as "TotalArea"." "Don't change the predefined content' only fill your code in the region *YOUR CODE"" from math import * def InterageSimpson (N, a,…arrow_forwardYou are an investor who receives daily price quotes for a stock. The span of a stock's price on a given day is the number of consecutive days, from the given day going backwards, on which its price was less than or equal to its price on the day we are considering. Thus, the Stock Span Problem is as follows: Given a series of daily price quotes for a stock, find the span of the stock on each day of the series. Assume you are given seven daily stock quotes: 3, 10, 4, 7, 9, 6, and 8. Assume further that these stock quotes are stored in the array quotes. Show a step-by-step, manual desk-check execution of the algorithm below showing the values of all variables and arrays for each step in each cycle of each loop, as demonstrated in clase Algorithm: A Simple Stock Span Algorithm SimpleStockSpan (quotes) spans Input: quotes, an array with n stock price quotes Output: spans, an array with n stock price spans 1 spans CreateArray (n) 2 for i-0 to n do k+1 span_endFALSE while i-k 20 and not…arrow_forward
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