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Use Exercise 48 to solve the recurrence relation\((n + 1){a_n} = (n + 3){a_{n - 1}} + n\) , for \(n \geqslant 1\), with \({a_0} = 1\)

Short Answer

Expert verified

Thus, the result isan=512n2+1312n+1

Step by step solution

01

Linear Homogeneous Recurrence Relations with Constant Coefficients definition

Suppose that\(\left\{ {{a_n}} \right\}\)satisfies the linear non-homogeneous recurrence relation

\({a_n} = {c_1}{a_{n - 1}} + {c_2}{a_{n - 2}} + \cdots + {c_k}{a_{n - k}} + F(n),\)Where\({c_1},{c_2}, \ldots ,{c_k}\)are real numbers, and

\(F(n) = \left( {{b_f}{n^t} + {b_{f - 1}}{n^{t - 1}} + \cdots + {b_1}n + {b_0}} \right){s^n},\)Where\({b_0},{b_1}, \ldots ,{b_t}\)and\(s\)are real numbers.

When\(s\)is not a root of the characteristic equation of the associated linear homogeneous recurrence relation, there is a particular solution of the form\(\left( {{p_t}{n^t} + {p_{t - 1}}{n^{t - 1}} + \cdots + {p_{1n}} + {p_0}} \right){s^n}\)

When\(s\)a root of this characteristic equation and its multiplicity is is\(m\), there is a particular solution of the form:\({n^m}\left( {{p_t}{n^t} + {p_{t - 1}}{n^{t - 1}} + \cdots + {p_1}n + {p_0}} \right){s^n}.\)

02

Use Recurrence Relations

The result from the previous exercise is\(f(n){a_n} = g(n){a_{n - 1}} + h(n)n \ge 1{a_0} = C\), then\({a_n} = \frac{{C + \sum\limits_{i = 1}^n Q (i) \cdot h(i)}}{{g(n + 1)Q(n + 1)}},\,\,Q(n) = \frac{{f(1) \cdot f(2) \cdot \ldots \cdot f(n - 1)}}{{g(1) \cdot g(2) \cdot \ldots \cdot g(n)}}\)

Solve:\((n + 1){a_n} = (n + 3){a_{n - 1}} + n{a_0} = 1\)

Since\(f(n){a_n} = g(n){a_{n - 1}} + h(n)\)and\({a_0} = C\)(in general):

\(f(n) = n + 1g(n) = n + 3h(n) = nC = 1\)

Let us first determine\(Q(n) = \frac{{f(1) \cdot f(2) \cdot \ldots \cdot f(n - 1)}}{{g(1) \cdot g(2) \cdot \ldots \cdot g(n)}}\)

\(\begin{array}{l}Q(n) = \frac{{f(1) \cdot f(2) \cdot \ldots \cdot f(n - 1)}}{{g(1) \cdot g(2) \cdot \ldots \cdot g(n)}}\\Q(n) = \frac{{2 \cdot 3 \cdot \ldots \cdot n}}{{4 \cdot 5 \cdot \ldots \cdot (n + 3)}}\\Q(n) = \frac{{2 \cdot 3}}{{(n + 1) \cdot (n + 2) \cdot (n + 3)}}\\Q(n) = \frac{6}{{(n + 1) \cdot (n + 2) \cdot (n + 3)}}\end{array}\)

Then we obtain:

\(\begin{array}{c}\sum\limits_{i = 1}^n Q (i)h(i) = \sum\limits_{i = 1}^n {\frac{{6i}}{{(i + 1) \cdot (i + 2) \cdot (i + 3)}}} \\ = \frac{{3n(n + 1)}}{{2(n + 2)(n + 3)}}\end{array}\)

03

Use the results from the previous exercise

Using the result of the previous exercise, we then obtain:

an=C+i=1nQ(i)·h(i)g(n+1)Q(n+1)an=1+3n(n+1)2(n+2)(n+3)6an=1+3n(n+1)2(n+2)(n+3)6(n+2)(n+3)an=(n+2)(n+3)+3n(n+1)26an=2(n+2)(n+3)+3n(n+1)12an=2n2+10n+12+3n2+3n12an=5n2+13n+1212an=512n2+1312n+1

Thus, the result isan=512n2+1312n+1

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Most popular questions from this chapter

Show that the algorithm from Exercise \({24}\) has worst-case time complexity \({O}\left( {{log n}} \right)\)in terms of the number of comparisons.

Find the coefficient of \({x^{10}}\) in the power series of each of these functions.

a) \({\left( {1 + {x^5} + {x^{10}} + {x^{15}} + \cdots } \right)^3}\)

b) \({\left( {{x^3} + {x^4} + {x^5} + {x^6} + {x^7} + \cdots } \right)^3}\)

c) \(\left( {{x^4} + {x^5} + {x^6}} \right)\left( {{x^3} + {x^4} + {x^5} + {x^6} + {x^7}} \right)(1 + x + \left. {{x^2} + {x^3} + {x^4} + \cdots } \right)\)

d) \(\left( {{x^2} + {x^4} + {x^6} + {x^8} + \cdots } \right)\left( {{x^3} + {x^6} + {x^9} + } \right. \cdots \left( {{x^4} + {x^8} + {x^{12}} + \cdots } \right)\)

e) \(\left( {1 + {x^2} + {x^4} + {x^6} + {x^8} + \cdots } \right)\left( {1 + {x^4} + {x^8} + {x^{12}} + } \right. \cdots )\left( {1 + {x^6} + {x^{12}} + {x^{18}} + \cdots } \right)\)

Find the sequence with each of these functions as its exponential generating functionf(x)=e3x-3e2x.

Use generating functions to find the number of ways to choose a dozen bagels from three varieties—egg, salty, and plain—if at least two bagels of each kind but no more than three salty bagels are chosen.

In this exercise we construct a dynamic programming algorithm for solving the problem of finding a subset S of items chosen from a set of n items where item i has a weight , which is a positive integer, so that the total weight of the items in S is a maximum but does no exceed a fixed weight limit W. Let M(j,w)denote the maximum total weight of the items in a subset of the first j items such that this total weight does not exceed w. This problem is known as the knapsack problem.

a) Show that ifwj>w, thenM(j,w)=M(j-1,w).
b) Show that if wjw, thenM(j,w)=max(M(j-1,w),wj+Mj-1,w-wj).
c) Use (a) and (b) to construct a dynamic programming algorithm for determining the maximum total weight of items so that this total weight does not exceed W. In your algorithm store the valuesM(j,w) as they are found.
d) Explain how you can use the values M(j,w)computed by the algorithm in part (c) to find a subset of items with maximum total weight not exceeding W.

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