A sequence is an ordered list of numbers a 1 , a 2 , a 3 , … a_1, a_2, a_3, \ldots a 1 , a 2 , a 3 , … written { a n } n = 1 ∞ \{a_n\}_{n=1}^{\infty} { a n } n = 1 ∞ Or { a n } \{a_n\} { a n } . Each a n a_n a n is called a term of the sequence. A series is the sum of The terms of a sequence: ∑ n = 1 ∞ a n \displaystyle\sum_{n=1}^{\infty} a_n n = 1 ∑ ∞ a n .
A sequence converges to a limit L L L if lim n → ∞ a n = L \displaystyle\lim_{n \to \infty} a_n = L n → ∞ lim a n = L . Otherwise it diverges .
An arithmetic sequence has a constant common difference d d d between consecutive terms:
a n = a 1 + ( n − 1 ) d a_n = a_1 + (n - 1)d a n = a 1 + ( n − 1 ) d
Where a 1 a_1 a 1 is the first term and d = a n + 1 − a n d = a_{n+1} - a_n d = a n + 1 − a n for all n n n .
The sum of the first n n n terms is:
S n = n 2 ( a 1 + a n ) = n 2 [ 2 a 1 + ( n − 1 ) d ] S_n = \frac{n}{2}(a_1 + a_n) = \frac{n}{2}\bigl[2a_1 + (n-1)d\bigr] S n = 2 n ( a 1 + a n ) = 2 n [ 2 a 1 + ( n − 1 ) d ]
Proof. Write the sum forward and backward:
S n = a 1 + ( a 1 + d ) + ( a 1 + 2 d ) + ⋯ + a n S_n = a_1 + (a_1 + d) + (a_1 + 2d) + \cdots + a_n S n = a 1 + ( a 1 + d ) + ( a 1 + 2 d ) + ⋯ + a n S n = a n + ( a n − d ) + ( a n − 2 d ) + ⋯ + a 1 S_n = a_n + (a_n - d) + (a_n - 2d) + \cdots + a_1 S n = a n + ( a n − d ) + ( a n − 2 d ) + ⋯ + a 1
Adding: 2 S n = n ( a 1 + a n ) 2S_n = n(a_1 + a_n) 2 S n = n ( a 1 + a n ) Hence S n = n 2 ( a 1 + a n ) S_n = \dfrac{n}{2}(a_1 + a_n) S n = 2 n ( a 1 + a n ) .
Example. Find the sum of the first 50 positive odd numbers.
a 1 = 1 a_1 = 1 a 1 = 1 , d = 2 d = 2 d = 2 , a 50 = 1 + 49 ⋅ 2 = 99 a_{50} = 1 + 49 \cdot 2 = 99 a 50 = 1 + 49 ⋅ 2 = 99 .
S 50 = 50 2 ( 1 + 99 ) = 2500 S_{50} = \frac{50}{2}(1 + 99) = 2500 S 50 = 2 50 ( 1 + 99 ) = 2500
The arithmetic mean of a a a and b b b is a + b 2 \dfrac{a + b}{2} 2 a + b . In an arithmetic sequence, each term is The arithmetic mean of its neighbours.
A geometric sequence has a constant common ratio r r r between consecutive terms:
a n = a 1 ⋅ r n − 1 a_n = a_1 \cdot r^{n-1} a n = a 1 ⋅ r n − 1
Where a 1 a_1 a 1 is the first term and r = a n + 1 a n r = \dfrac{a_{n+1}}{a_n} r = a n a n + 1 for all n n n .
For r ≠ 1 r \ne 1 r = 1 :
S n = a 1 ( 1 − r n ) 1 − r = a 1 ( r n − 1 ) r − 1 S_n = \frac{a_1(1 - r^n)}{1 - r} = \frac{a_1(r^n - 1)}{r - 1} S n = 1 − r a 1 ( 1 − r n ) = r − 1 a 1 ( r n − 1 )
Proof. S n = a 1 + a 1 r + a 1 r 2 + ⋯ + a 1 r n − 1 S_n = a_1 + a_1 r + a_1 r^2 + \cdots + a_1 r^{n-1} S n = a 1 + a 1 r + a 1 r 2 + ⋯ + a 1 r n − 1 .
r S n = a 1 r + a 1 r 2 + ⋯ + a 1 r n − 1 + a 1 r n rS_n = a_1 r + a_1 r^2 + \cdots + a_1 r^{n-1} + a_1 r^n r S n = a 1 r + a 1 r 2 + ⋯ + a 1 r n − 1 + a 1 r n
Subtracting: S n − r S n = a 1 − a 1 r n S_n - rS_n = a_1 - a_1 r^n S n − r S n = a 1 − a 1 r n .
S n ( 1 − r ) = a 1 ( 1 − r n ) ⟹ S n = a 1 ( 1 − r n ) 1 − r S_n(1 - r) = a_1(1 - r^n) \implies S_n = \frac{a_1(1 - r^n)}{1 - r} S n ( 1 − r ) = a 1 ( 1 − r n ) ⟹ S n = 1 − r a 1 ( 1 − r n )
If ∣ r ∣ < 1 |r| \lt 1 ∣ r ∣ < 1 The infinite geometric series converges:
S ∞ = ∑ n = 1 ∞ a 1 r n − 1 = a 1 1 − r S_{\infty} = \sum_{n=1}^{\infty} a_1 r^{n-1} = \frac{a_1}{1 - r} S ∞ = ∑ n = 1 ∞ a 1 r n − 1 = 1 − r a 1
If ∣ r ∣ ≥ 1 |r| \ge 1 ∣ r ∣ ≥ 1 The series diverges.
Example. Express 0. 7 ‾ 0.\overline{7} 0. 7 as a fraction.
0. 7 ‾ = 7 10 + 7 100 + 7 1000 + ⋯ 0.\overline{7} = \frac{7}{10} + \frac{7}{100} + \frac{7}{1000} + \cdots 0. 7 = 10 7 + 100 7 + 1000 7 + ⋯
This is a geometric series with a 1 = 7 10 a_1 = \dfrac{7}{10} a 1 = 10 7 and r = 1 10 r = \dfrac{1}{10} r = 10 1 .
S ∞ = 7 / 10 1 − 1 / 10 = 7 / 10 9 / 10 = 7 9 S_{\infty} = \frac{7/10}{1 - 1/10} = \frac{7/10}{9/10} = \frac{7}{9} S ∞ = 1 − 1/10 7/10 = 9/10 7/10 = 9 7
Example. Evaluate ∑ n = 0 ∞ 3 4 n \displaystyle\sum_{n=0}^{\infty} \frac{3}{4^n} n = 0 ∑ ∞ 4 n 3 .
a 1 = 3 a_1 = 3 a 1 = 3 , r = 1 4 r = \dfrac{1}{4} r = 4 1 .
S ∞ = 3 1 − 1 / 4 = 3 3 / 4 = 4 S_{\infty} = \frac{3}{1 - 1/4} = \frac{3}{3/4} = 4 S ∞ = 1 − 1/4 3 = 3/4 3 = 4
The geometric mean of positive numbers a a a and b b b is a b \sqrt{ab} ab . In a geometric sequence with Positive terms, each term is the geometric mean of its neighbours.
∑ k = m n a k = a m + a m + 1 + ⋯ + a n \sum_{k=m}^{n} a_k = a_m + a_{m+1} + \cdots + a_n ∑ k = m n a k = a m + a m + 1 + ⋯ + a n
∑ k = m n c ⋅ a k = c ∑ k = m n a k \sum_{k=m}^{n} c \cdot a_k = c\sum_{k=m}^{n} a_k ∑ k = m n c ⋅ a k = c ∑ k = m n a k
∑ k = m n ( a k + b k ) = ∑ k = m n a k + ∑ k = m n b k \sum_{k=m}^{n}(a_k + b_k) = \sum_{k=m}^{n} a_k + \sum_{k=m}^{n} b_k ∑ k = m n ( a k + b k ) = ∑ k = m n a k + ∑ k = m n b k
Sum Closed Form ∑ k = 1 n k \displaystyle\sum_{k=1}^{n} k k = 1 ∑ n k n ( n + 1 ) 2 \dfrac{n(n+1)}{2} 2 n ( n + 1 ) ∑ k = 1 n k 2 \displaystyle\sum_{k=1}^{n} k^2 k = 1 ∑ n k 2 n ( n + 1 ) ( 2 n + 1 ) 6 \dfrac{n(n+1)(2n+1)}{6} 6 n ( n + 1 ) ( 2 n + 1 ) ∑ k = 1 n k 3 \displaystyle\sum_{k=1}^{n} k^3 k = 1 ∑ n k 3 n 2 ( n + 1 ) 2 4 \dfrac{n^2(n+1)^2}{4} 4 n 2 ( n + 1 ) 2 ∑ k = 0 n r k \displaystyle\sum_{k=0}^{n} r^k k = 0 ∑ n r k 1 − r n + 1 1 − r , r ≠ 1 \dfrac{1 - r^{n+1}}{1 - r}, \quad r \ne 1 1 − r 1 − r n + 1 , r = 1
If lim n → ∞ a n ≠ 0 \displaystyle\lim_{n \to \infty} a_n \ne 0 n → ∞ lim a n = 0 Then ∑ a n \displaystyle\sum a_n ∑ a n diverges.
Caution. If lim n → ∞ a n = 0 \lim_{n \to \infty} a_n = 0 lim n → ∞ a n = 0 The series may or may not converge. The harmonic series ∑ 1 n \sum \dfrac{1}{n} ∑ n 1 diverges despite its terms tending to zero.
If 0 ≤ a n ≤ b n 0 \le a_n \le b_n 0 ≤ a n ≤ b n for all n n n :
If ∑ b n \sum b_n ∑ b n converges, then ∑ a n \sum a_n ∑ a n converges. If ∑ a n \sum a_n ∑ a n diverges, then ∑ b n \sum b_n ∑ b n diverges. For a series ∑ a n \sum a_n ∑ a n with a n ≠ 0 a_n \ne 0 a n = 0 :
L = lim n → ∞ ∣ a n + 1 a n ∣ L = \lim_{n \to \infty} \left|\frac{a_{n+1}}{a_n}\right| L = lim n → ∞ a n a n + 1
L < 1 L \lt 1 L < 1 : the series converges absolutely .L > 1 L \gt 1 L > 1 : the series diverges .L = 1 L = 1 L = 1 : the test is inconclusive .Example. Does ∑ n = 1 ∞ n 2 2 n \displaystyle\sum_{n=1}^{\infty} \frac{n^2}{2^n} n = 1 ∑ ∞ 2 n n 2 converge?
L = lim n → ∞ ( n + 1 ) 2 / 2 n + 1 n 2 / 2 n = lim n → ∞ ( n + 1 ) 2 2 n 2 = 1 2 < 1 L = \lim_{n \to \infty} \frac{(n+1)^2 / 2^{n+1}}{n^2 / 2^n} = \lim_{n \to \infty} \frac{(n+1)^2}{2n^2} = \frac{1}{2} \lt 1 L = lim n → ∞ n 2 / 2 n ( n + 1 ) 2 / 2 n + 1 = lim n → ∞ 2 n 2 ( n + 1 ) 2 = 2 1 < 1
The series converges.
If f f f is continuous, positive, and decreasing on [ 1 , ∞ ) [1, \infty) [ 1 , ∞ ) Then ∑ f ( n ) \sum f(n) ∑ f ( n ) converges if and Only if ∫ 1 ∞ f ( x ) d x \displaystyle\int_1^{\infty} f(x)\,dx ∫ 1 ∞ f ( x ) d x converges.
Example. The harmonic series diverges because:
∫ 1 ∞ 1 x d x = lim b → ∞ ln b = ∞ \int_1^{\infty} \frac{1}{x}\,dx = \lim_{b \to \infty} \ln b = \infty ∫ 1 ∞ x 1 d x = lim b → ∞ ln b = ∞
An alternating series ∑ ( − 1 ) n + 1 a n \sum (-1)^{n+1} a_n ∑ ( − 1 ) n + 1 a n (or ∑ ( − 1 ) n a n \sum (-1)^n a_n ∑ ( − 1 ) n a n ) converges if:
a n a_n a n is decreasing: a n + 1 ≤ a n a_{n+1} \le a_n a n + 1 ≤ a n for all n n n .lim n → ∞ a n = 0 \displaystyle\lim_{n \to \infty} a_n = 0 n → ∞ lim a n = 0 .Example. ∑ n = 1 ∞ ( − 1 ) n + 1 n \displaystyle\sum_{n=1}^{\infty} \frac{(-1)^{n+1}}{n} n = 1 ∑ ∞ n ( − 1 ) n + 1 converges (the alternating harmonic Series) since 1 n \dfrac{1}{n} n 1 decreases to 0 0 0 .
For n ∈ Z + n \in \mathbb{Z}^+ n ∈ Z + :
( a + b ) n = ∑ k = 0 n ( n k ) a n − k b k (a + b)^n = \sum_{k=0}^{n} \binom{n}{k} a^{n-k} b^k ( a + b ) n = ∑ k = 0 n ( k n ) a n − k b k
Where the binomial coefficient is:
( n k ) = n ! k ! ( n − k ) ! \binom{n}{k} = \frac{n!}{k!(n-k)!} ( k n ) = k ! ( n − k )! n !
For any n ∈ R n \in \mathbb{R} n ∈ R and ∣ x ∣ < 1 |x| \lt 1 ∣ x ∣ < 1 :
( 1 + x ) n = 1 + n x + n ( n − 1 ) 2 ! x 2 + n ( n − 1 ) ( n − 2 ) 3 ! x 3 + ⋯ (1 + x)^n = 1 + nx + \frac{n(n-1)}{2!}x^2 + \frac{n(n-1)(n-2)}{3!}x^3 + \cdots ( 1 + x ) n = 1 + n x + 2 ! n ( n − 1 ) x 2 + 3 ! n ( n − 1 ) ( n − 2 ) x 3 + ⋯
This is an infinite series that converges for ∣ x ∣ < 1 |x| \lt 1 ∣ x ∣ < 1 (and at the endpoints depending on n n n ).
Example. Expand ( 1 + x ) − 2 (1 + x)^{-2} ( 1 + x ) − 2 up to the term in x 3 x^3 x 3 .
( 1 + x ) − 2 = 1 + ( − 2 ) x + ( − 2 ) ( − 3 ) 2 x 2 + ( − 2 ) ( − 3 ) ( − 4 ) 6 x 3 + ⋯ (1 + x)^{-2} = 1 + (-2)x + \frac{(-2)(-3)}{2}x^2 + \frac{(-2)(-3)(-4)}{6}x^3 + \cdots ( 1 + x ) − 2 = 1 + ( − 2 ) x + 2 ( − 2 ) ( − 3 ) x 2 + 6 ( − 2 ) ( − 3 ) ( − 4 ) x 3 + ⋯
= 1 − 2 x + 3 x 2 − 4 x 3 + ⋯ = 1 - 2x + 3x^2 - 4x^3 + \cdots = 1 − 2 x + 3 x 2 − 4 x 3 + ⋯
Example. Find the coefficient of x 3 x^3 x 3 in the expansion of ( 2 − 3 x ) 1 / 2 (2 - 3x)^{1/2} ( 2 − 3 x ) 1/2 .
( 2 − 3 x ) 1 / 2 = 2 ( 1 − 3 x 2 ) 1 / 2 (2 - 3x)^{1/2} = \sqrt{2}\left(1 - \frac{3x}{2}\right)^{1/2} ( 2 − 3 x ) 1/2 = 2 ( 1 − 2 3 x ) 1/2
= 2 [ 1 + 1 2 ( − 3 x 2 ) + ( 1 / 2 ) ( − 1 / 2 ) 2 ( − 3 x 2 ) 2 + ( 1 / 2 ) ( − 1 / 2 ) ( − 3 / 2 ) 6 ( − 3 x 2 ) 3 + ⋯ ] = \sqrt{2}\left[1 + \frac{1}{2}\!\left(-\frac{3x}{2}\right) + \frac{(1/2)(-1/2)}{2}\!\left(-\frac{3x}{2}\right)^{\!2} + \frac{(1/2)(-1/2)(-3/2)}{6}\!\left(-\frac{3x}{2}\right)^{\!3} + \cdots\right] = 2 [ 1 + 2 1 ( − 2 3 x ) + 2 ( 1/2 ) ( − 1/2 ) ( − 2 3 x ) 2 + 6 ( 1/2 ) ( − 1/2 ) ( − 3/2 ) ( − 2 3 x ) 3 + ⋯ ]
The x 3 x^3 x 3 coefficient:
2 ⋅ − 3 / 8 6 ⋅ ( − 27 8 ) = 2 ⋅ 81 384 = 27 2 128 \sqrt{2} \cdot \frac{-3/8}{6} \cdot \left(-\frac{27}{8}\right) = \sqrt{2} \cdot \frac{81}{384} = \frac{27\sqrt{2}}{128} 2 ⋅ 6 − 3/8 ⋅ ( − 8 27 ) = 2 ⋅ 384 81 = 128 27 2
The Maclaurin series of a function f f f is its Taylor series expansion about x = 0 x = 0 x = 0 :
f ( x ) = ∑ n = 0 ∞ f ( n ) ( 0 ) n ! x n = f ( 0 ) + f " ( 0 ) x + f ′ ′ ( 0 ) 2 ! x 2 + f ′ ′ ′ ( 0 ) 3 ! x 3 + ⋯ f(x) = \sum_{n=0}^{\infty} \frac{f^{(n)}(0)}{n!}\,x^n = f(0) + f"(0)x + \frac{f''(0)}{2!}x^2 + \frac{f'''(0)}{3!}x^3 + \cdots f ( x ) = ∑ n = 0 ∞ n ! f ( n ) ( 0 ) x n = f ( 0 ) + f " ( 0 ) x + 2 ! f ′′ ( 0 ) x 2 + 3 ! f ′′′ ( 0 ) x 3 + ⋯
Function Maclaurin Series Radius of Convergence e x e^x e x ∑ n = 0 ∞ x n n ! \displaystyle\sum_{n=0}^{\infty} \frac{x^n}{n!} n = 0 ∑ ∞ n ! x n ∞ \infty ∞ sin x \sin x sin x ∑ n = 0 ∞ ( − 1 ) n x 2 n + 1 ( 2 n + 1 ) ! \displaystyle\sum_{n=0}^{\infty} \frac{(-1)^n x^{2n+1}}{(2n+1)!} n = 0 ∑ ∞ ( 2 n + 1 )! ( − 1 ) n x 2 n + 1 ∞ \infty ∞ cos x \cos x cos x ∑ n = 0 ∞ ( − 1 ) n x 2 n ( 2 n ) ! \displaystyle\sum_{n=0}^{\infty} \frac{(-1)^n x^{2n}}{(2n)!} n = 0 ∑ ∞ ( 2 n )! ( − 1 ) n x 2 n ∞ \infty ∞ ln ( 1 + x ) \ln(1 + x) ln ( 1 + x ) ∑ n = 1 ∞ ( − 1 ) n + 1 x n n \displaystyle\sum_{n=1}^{\infty} \frac{(-1)^{n+1} x^n}{n} n = 1 ∑ ∞ n ( − 1 ) n + 1 x n 1 1 1 ( 1 + x ) n (1 + x)^n ( 1 + x ) n ∑ k = 0 ∞ ( n k ) x k \displaystyle\sum_{k=0}^{\infty} \binom{n}{k}x^k k = 0 ∑ ∞ ( k n ) x k 1 1 1 1 1 − x \dfrac{1}{1 - x} 1 − x 1 ∑ n = 0 ∞ x n \displaystyle\sum_{n=0}^{\infty} x^n n = 0 ∑ ∞ x n 1 1 1 arctan x \arctan x arctan x ∑ n = 0 ∞ ( − 1 ) n x 2 n + 1 2 n + 1 \displaystyle\sum_{n=0}^{\infty} \frac{(-1)^n x^{2n+1}}{2n+1} n = 0 ∑ ∞ 2 n + 1 ( − 1 ) n x 2 n + 1 1 1 1
Example. Find the Maclaurin series of e x e^x e x .
f ( 0 ) = 1 , f ′ ( 0 ) = 1 , f ′ ′ ( 0 ) = 1 , … , f ( n ) ( 0 ) = 1 f(0) = 1, \quad f'(0) = 1, \quad f''(0) = 1, \quad \ldots, \quad f^{(n)}(0) = 1 f ( 0 ) = 1 , f ′ ( 0 ) = 1 , f ′′ ( 0 ) = 1 , … , f ( n ) ( 0 ) = 1
e x = 1 + x + x 2 2 ! + x 3 3 ! + ⋯ = ∑ n = 0 ∞ x n n ! e^x = 1 + x + \frac{x^2}{2!} + \frac{x^3}{3!} + \cdots = \sum_{n=0}^{\infty} \frac{x^n}{n!} e x = 1 + x + 2 ! x 2 + 3 ! x 3 + ⋯ = ∑ n = 0 ∞ n ! x n
Example. Find the Maclaurin series of sin x \sin x sin x .
f ( 0 ) = 0 , f ′ ( 0 ) = 1 , f ′ ′ ( 0 ) = 0 , f ′ ′ ′ ( 0 ) = − 1 , f ( 4 ) ( 0 ) = 0 , … f(0) = 0, \quad f'(0) = 1, \quad f''(0) = 0, \quad f'''(0) = -1, \quad f^{(4)}(0) = 0, \quad \ldots f ( 0 ) = 0 , f ′ ( 0 ) = 1 , f ′′ ( 0 ) = 0 , f ′′′ ( 0 ) = − 1 , f ( 4 ) ( 0 ) = 0 , …
sin x = x − x 3 3 ! + x 5 5 ! − x 7 7 ! + ⋯ = ∑ n = 0 ∞ ( − 1 ) n x 2 n + 1 ( 2 n + 1 ) ! \sin x = x - \frac{x^3}{3!} + \frac{x^5}{5!} - \frac{x^7}{7!} + \cdots = \sum_{n=0}^{\infty} \frac{(-1)^n x^{2n+1}}{(2n+1)!} sin x = x − 3 ! x 3 + 5 ! x 5 − 7 ! x 7 + ⋯ = ∑ n = 0 ∞ ( 2 n + 1 )! ( − 1 ) n x 2 n + 1
Example. Find the Maclaurin series of x 2 e x x^2 e^x x 2 e x .
Since e x = ∑ n = 0 ∞ x n n ! e^x = \displaystyle\sum_{n=0}^{\infty} \frac{x^n}{n!} e x = n = 0 ∑ ∞ n ! x n Multiplying by x 2 x^2 x 2 :
x 2 e x = ∑ n = 0 ∞ x n + 2 n ! = x 2 + x 3 + x 4 2 ! + x 5 3 ! + ⋯ x^2 e^x = \sum_{n=0}^{\infty} \frac{x^{n+2}}{n!} = x^2 + x^3 + \frac{x^4}{2!} + \frac{x^5}{3!} + \cdots x 2 e x = ∑ n = 0 ∞ n ! x n + 2 = x 2 + x 3 + 2 ! x 4 + 3 ! x 5 + ⋯
Example. Find the Maclaurin series of 1 1 + x 2 \dfrac{1}{1 + x^2} 1 + x 2 1 .
1 1 + x 2 = 1 − x 2 + x 4 − x 6 + ⋯ = ∑ n = 0 ∞ ( − 1 ) n x 2 n \frac{1}{1 + x^2} = 1 - x^2 + x^4 - x^6 + \cdots = \sum_{n=0}^{\infty} (-1)^n x^{2n} 1 + x 2 1 = 1 − x 2 + x 4 − x 6 + ⋯ = ∑ n = 0 ∞ ( − 1 ) n x 2 n
Integrating term by term gives the series for arctan x \arctan x arctan x :
arctan x = x − x 3 3 + x 5 5 − x 7 7 + ⋯ \arctan x = x - \frac{x^3}{3} + \frac{x^5}{5} - \frac{x^7}{7} + \cdots arctan x = x − 3 x 3 + 5 x 5 − 7 x 7 + ⋯
The Taylor series of f f f about x = a x = a x = a is:
f ( x ) = ∑ n = 0 ∞ f ( n ) ( a ) n ! ( x − a ) n f(x) = \sum_{n=0}^{\infty} \frac{f^{(n)}(a)}{n!}(x - a)^n f ( x ) = ∑ n = 0 ∞ n ! f ( n ) ( a ) ( x − a ) n
This reduces to the Maclaurin series when a = 0 a = 0 a = 0 .
The n n n -th degree Taylor polynomial of f f f about a a a is:
T n ( x ) = ∑ k = 0 n f ( k ) ( a ) k ! ( x − a ) k T_n(x) = \sum_{k=0}^{n} \frac{f^{(k)}(a)}{k!}(x - a)^k T n ( x ) = ∑ k = 0 n k ! f ( k ) ( a ) ( x − a ) k
The error in approximating f ( x ) f(x) f ( x ) by T n ( x ) T_n(x) T n ( x ) is bounded by:
∣ R n ( x ) ∣ = ∣ f ( x ) − T n ( x ) ∣ ≤ M ∣ x − a ∣ n + 1 ( n + 1 ) ! |R_n(x)| = \left|f(x) - T_n(x)\right| \le \frac{M|x - a|^{n+1}}{(n+1)!} ∣ R n ( x ) ∣ = ∣ f ( x ) − T n ( x ) ∣ ≤ ( n + 1 )! M ∣ x − a ∣ n + 1
Where M M M is an upper bound for ∣ f ( n + 1 ) ( t ) ∣ |f^{(n+1)}(t)| ∣ f ( n + 1 ) ( t ) ∣ for all t t t between a a a and x x x .
Example. Approximate e \sqrt{e} e using a 3rd degree Maclaurin polynomial and bound the error.
T 3 ( x ) = 1 + x + x 2 2 + x 3 6 T_3(x) = 1 + x + \dfrac{x^2}{2} + \dfrac{x^3}{6} T 3 ( x ) = 1 + x + 2 x 2 + 6 x 3 .
T 3 ( 0.5 ) = 1 + 0.5 + 0.125 + 0.020833 … = 1.64583 … T_3(0.5) = 1 + 0.5 + 0.125 + 0.020833\ldots = 1.64583\ldots T 3 ( 0.5 ) = 1 + 0.5 + 0.125 + 0.020833 … = 1.64583 …
True value: e 0.5 ≈ 1.64872 e^{0.5} \approx 1.64872 e 0.5 ≈ 1.64872 .
For the error bound, ∣ f ( 4 ) ( t ) ∣ = e t ≤ e 0.5 |f^{(4)}(t)| = e^t \le e^{0.5} ∣ f ( 4 ) ( t ) ∣ = e t ≤ e 0.5 for 0 ≤ t ≤ 0.5 0 \le t \le 0.5 0 ≤ t ≤ 0.5 :
∣ R 3 ( 0.5 ) ∣ ≤ e 0.5 ⋅ ( 0.5 ) 4 24 ≈ 1.649 ⋅ 0.0625 24 ≈ 0.0043 |R_3(0.5)| \le \frac{e^{0.5} \cdot (0.5)^4}{24} \approx \frac{1.649 \cdot 0.0625}{24} \approx 0.0043 ∣ R 3 ( 0.5 ) ∣ ≤ 24 e 0.5 ⋅ ( 0.5 ) 4 ≈ 24 1.649 ⋅ 0.0625 ≈ 0.0043
Actual error: ∣ 1.64872 − 1.64583 ∣ ≈ 0.0029 |1.64872 - 1.64583| \approx 0.0029 ∣1.64872 − 1.64583∣ ≈ 0.0029 Which is within the bound.
Mathematical induction proves a statement P ( n ) P(n) P ( n ) for all integers n ≥ n 0 n \ge n_0 n ≥ n 0 :
Base case: Verify P ( n 0 ) P(n_0) P ( n 0 ) is true.Inductive hypothesis: Assume P ( k ) P(k) P ( k ) is true for some arbitrary k ≥ n 0 k \ge n_0 k ≥ n 0 .Inductive step: Using the hypothesis, prove P ( k + 1 ) P(k + 1) P ( k + 1 ) is true.Conclusion: By the principle of mathematical induction, P ( n ) P(n) P ( n ) is true for all n ≥ n 0 n \ge n_0 n ≥ n 0 .Example. Prove that ∑ k = 1 n k 2 = n ( n + 1 ) ( 2 n + 1 ) 6 \displaystyle\sum_{k=1}^{n} k^2 = \frac{n(n+1)(2n+1)}{6} k = 1 ∑ n k 2 = 6 n ( n + 1 ) ( 2 n + 1 ) for all n ∈ Z + n \in \mathbb{Z}^+ n ∈ Z + .
Base case (n = 1 n = 1 n = 1 ): 1 ⋅ 2 ⋅ 3 6 = 1 \dfrac{1 \cdot 2 \cdot 3}{6} = 1 6 1 ⋅ 2 ⋅ 3 = 1 . True.
Inductive hypothesis: Assume ∑ k = 1 j k 2 = j ( j + 1 ) ( 2 j + 1 ) 6 \displaystyle\sum_{k=1}^{j} k^2 = \frac{j(j+1)(2j+1)}{6} k = 1 ∑ j k 2 = 6 j ( j + 1 ) ( 2 j + 1 ) for some j ≥ 1 j \ge 1 j ≥ 1 .
Inductive step:
∑ k = 1 j + 1 k 2 = ∑ k = 1 j k 2 + ( j + 1 ) 2 = j ( j + 1 ) ( 2 j + 1 ) 6 + ( j + 1 ) 2 \sum_{k=1}^{j+1} k^2 = \sum_{k=1}^{j} k^2 + (j+1)^2 = \frac{j(j+1)(2j+1)}{6} + (j+1)^2 ∑ k = 1 j + 1 k 2 = ∑ k = 1 j k 2 + ( j + 1 ) 2 = 6 j ( j + 1 ) ( 2 j + 1 ) + ( j + 1 ) 2
= ( j + 1 ) 6 [ j ( 2 j + 1 ) + 6 ( j + 1 ) ] = ( j + 1 ) 6 [ 2 j 2 + 7 j + 6 ] = \frac{(j+1)}{6}\bigl[j(2j+1) + 6(j+1)\bigr] = \frac{(j+1)}{6}\bigl[2j^2 + 7j + 6\bigr] = 6 ( j + 1 ) [ j ( 2 j + 1 ) + 6 ( j + 1 ) ] = 6 ( j + 1 ) [ 2 j 2 + 7 j + 6 ]
= ( j + 1 ) ( j + 2 ) ( 2 j + 3 ) 6 = \frac{(j+1)(j+2)(2j+3)}{6} = 6 ( j + 1 ) ( j + 2 ) ( 2 j + 3 )
This is the formula with n = j + 1 n = j + 1 n = j + 1 . Hence P ( j + 1 ) P(j+1) P ( j + 1 ) is true. By induction, the result holds for All n ∈ Z + n \in \mathbb{Z}^+ n ∈ Z + .
Example. Prove that 3 n − 1 3^n - 1 3 n − 1 is divisible by 2 2 2 for all n ∈ N n \in \mathbb{N} n ∈ N .
Base case (n = 0 n = 0 n = 0 ): 3 0 − 1 = 0 3^0 - 1 = 0 3 0 − 1 = 0 Which is divisible by 2 2 2 . True.
Inductive hypothesis: 3 k − 1 = 2 m 3^k - 1 = 2m 3 k − 1 = 2 m for some m ∈ Z m \in \mathbb{Z} m ∈ Z .
Inductive step:
3 k + 1 − 1 = 3 ⋅ 3 k − 1 = 3 ( 2 m + 1 ) − 1 = 6 m + 3 − 1 = 6 m + 2 = 2 ( 3 m + 1 ) 3^{k+1} - 1 = 3 \cdot 3^k - 1 = 3(2m + 1) - 1 = 6m + 3 - 1 = 6m + 2 = 2(3m + 1) 3 k + 1 − 1 = 3 ⋅ 3 k − 1 = 3 ( 2 m + 1 ) − 1 = 6 m + 3 − 1 = 6 m + 2 = 2 ( 3 m + 1 )
This is divisible by 2 2 2 . By induction, the result holds.
Example. Prove that 2 n > n 2^n \gt n 2 n > n for all n ∈ Z + n \in \mathbb{Z}^+ n ∈ Z + .
Base case (n = 1 n = 1 n = 1 ): 2 1 = 2 > 1 2^1 = 2 \gt 1 2 1 = 2 > 1 . True.
Inductive hypothesis: 2 k > k 2^k \gt k 2 k > k for some k ≥ 1 k \ge 1 k ≥ 1 .
Inductive step:
2 k + 1 = 2 ⋅ 2 k > 2 k ≥ k + 1 2^{k+1} = 2 \cdot 2^k \gt 2k \ge k + 1 2 k + 1 = 2 ⋅ 2 k > 2 k ≥ k + 1
(The last inequality holds since k ≥ 1 k \ge 1 k ≥ 1 .) Therefore 2 k + 1 > k + 1 2^{k+1} \gt k + 1 2 k + 1 > k + 1 . By induction, 2 n > n 2^n \gt n 2 n > n For all positive integers n n n .
Caution
The inductive step must genuinely use the inductive hypothesis. Proving P ( k + 1 ) P(k+1) P ( k + 1 ) independently Of P ( k ) P(k) P ( k ) is not a valid induction argument. Always make it explicit where the hypothesis is used.
Worked Example: Convergence of a Series by Ratio Test
Determine whether ∑ n = 1 ∞ n ! 10 n \displaystyle\sum_{n=1}^{\infty} \frac{n!}{10^n} n = 1 ∑ ∞ 1 0 n n ! converges or diverges.
Solution Apply the ratio test:
L = lim n → ∞ ( n + 1 ) ! / 10 n + 1 n ! / 10 n = lim n → ∞ ( n + 1 ) ! ⋅ 10 n n ! ⋅ 10 n + 1 = lim n → ∞ n + 1 10 = ∞ L = \lim_{n \to \infty} \frac{(n+1)! / 10^{n+1}}{n! / 10^n} = \lim_{n \to \infty} \frac{(n+1)! \cdot 10^n}{n! \cdot 10^{n+1}} = \lim_{n \to \infty} \frac{n+1}{10} = \infty L = lim n → ∞ n ! /1 0 n ( n + 1 )! /1 0 n + 1 = lim n → ∞ n ! ⋅ 1 0 n + 1 ( n + 1 )! ⋅ 1 0 n = lim n → ∞ 10 n + 1 = ∞
Since L > 1 L \gt 1 L > 1 The series diverges by the ratio test.
Worked Example: Maclaurin Series of a Composite Function
Find the Maclaurin series of f ( x ) = e − x 2 f(x) = e^{-x^2} f ( x ) = e − x 2 up to the term in x 6 x^6 x 6 And use it to approximate ∫ 0 0.5 e − x 2 d x \displaystyle\int_0^{0.5} e^{-x^2}\,dx ∫ 0 0.5 e − x 2 d x .
Solution Substitute − x 2 -x^2 − x 2 into the Maclaurin series for e u e^u e u :
e − x 2 = 1 + ( − x 2 ) + ( − x 2 ) 2 2 ! + ( − x 2 ) 3 3 ! + ⋯ = 1 − x 2 + x 4 2 − x 6 6 + ⋯ e^{-x^2} = 1 + (-x^2) + \frac{(-x^2)^2}{2!} + \frac{(-x^2)^3}{3!} + \cdots = 1 - x^2 + \frac{x^4}{2} - \frac{x^6}{6} + \cdots e − x 2 = 1 + ( − x 2 ) + 2 ! ( − x 2 ) 2 + 3 ! ( − x 2 ) 3 + ⋯ = 1 − x 2 + 2 x 4 − 6 x 6 + ⋯
Integrate term by term from 0 0 0 to 0.5 0.5 0.5 :
∫ 0 0.5 e − x 2 d x ≈ ∫ 0 0.5 ( 1 − x 2 + x 4 2 − x 6 6 ) d x \int_0^{0.5} e^{-x^2}\,dx \approx \int_0^{0.5} \left(1 - x^2 + \frac{x^4}{2} - \frac{x^6}{6}\right)dx ∫ 0 0.5 e − x 2 d x ≈ ∫ 0 0.5 ( 1 − x 2 + 2 x 4 − 6 x 6 ) d x
= [ x − x 3 3 + x 5 10 − x 7 42 ] 0 0.5 = \left[x - \frac{x^3}{3} + \frac{x^5}{10} - \frac{x^7}{42}\right]_0^{0.5} = [ x − 3 x 3 + 10 x 5 − 42 x 7 ] 0 0.5
= 0.5 − 0.125 3 + 0.03125 10 − 0.0078125 42 = 0.5 - \frac{0.125}{3} + \frac{0.03125}{10} - \frac{0.0078125}{42} = 0.5 − 3 0.125 + 10 0.03125 − 42 0.0078125
= 0.5 − 0.041667 + 0.003125 − 0.000186 ≈ 0.4613 = 0.5 - 0.041667 + 0.003125 - 0.000186 \approx 0.4613 = 0.5 − 0.041667 + 0.003125 − 0.000186 ≈ 0.4613
The actual value of the error function at 0.5 0.5 0.5 gives approximately 0.4613 0.4613 0.4613 Confirming the accuracy Of this approximation.
Worked Example: General Binomial Expansion to Find a Coefficient
Find the coefficient of x 4 x^4 x 4 in the expansion of ( 1 − 2 x ) − 3 (1 - 2x)^{-3} ( 1 − 2 x ) − 3 .
Solution ( 1 + x ) n = 1 + n x + n ( n − 1 ) 2 ! x 2 + n ( n − 1 ) ( n − 2 ) 3 ! x 3 + n ( n − 1 ) ( n − 2 ) ( n − 3 ) 4 ! x 4 + ⋯ (1 + x)^n = 1 + nx + \frac{n(n-1)}{2!}x^2 + \frac{n(n-1)(n-2)}{3!}x^3 + \frac{n(n-1)(n-2)(n-3)}{4!}x^4 + \cdots ( 1 + x ) n = 1 + n x + 2 ! n ( n − 1 ) x 2 + 3 ! n ( n − 1 ) ( n − 2 ) x 3 + 4 ! n ( n − 1 ) ( n − 2 ) ( n − 3 ) x 4 + ⋯
Here n = − 3 n = -3 n = − 3 and we substitute x → − 2 x x \to -2x x → − 2 x :
( 1 − 2 x ) − 3 = 1 + ( − 3 ) ( − 2 x ) + ( − 3 ) ( − 4 ) 2 ! ( − 2 x ) 2 + ( − 3 ) ( − 4 ) ( − 5 ) 3 ! ( − 2 x ) 3 + ( − 3 ) ( − 4 ) ( − 5 ) ( − 6 ) 4 ! ( − 2 x ) 4 + ⋯ (1 - 2x)^{-3} = 1 + (-3)(-2x) + \frac{(-3)(-4)}{2!}(-2x)^2 + \frac{(-3)(-4)(-5)}{3!}(-2x)^3 + \frac{(-3)(-4)(-5)(-6)}{4!}(-2x)^4 + \cdots ( 1 − 2 x ) − 3 = 1 + ( − 3 ) ( − 2 x ) + 2 ! ( − 3 ) ( − 4 ) ( − 2 x ) 2 + 3 ! ( − 3 ) ( − 4 ) ( − 5 ) ( − 2 x ) 3 + 4 ! ( − 3 ) ( − 4 ) ( − 5 ) ( − 6 ) ( − 2 x ) 4 + ⋯
The x 4 x^4 x 4 coefficient:
( − 3 ) ( − 4 ) ( − 5 ) ( − 6 ) 24 ⋅ ( − 2 ) 4 = 360 24 ⋅ 16 = 15 ⋅ 16 = 240 \frac{(-3)(-4)(-5)(-6)}{24} \cdot (-2)^4 = \frac{360}{24} \cdot 16 = 15 \cdot 16 = 240 24 ( − 3 ) ( − 4 ) ( − 5 ) ( − 6 ) ⋅ ( − 2 ) 4 = 24 360 ⋅ 16 = 15 ⋅ 16 = 240
So the coefficient of x 4 x^4 x 4 is 240 240 240 .
Worked Example: Induction Proof Involving Summation
Prove by induction that ∑ k = 1 n k ⋅ 2 k − 1 = ( n − 1 ) ⋅ 2 n + 1 \displaystyle\sum_{k=1}^{n} k \cdot 2^{k-1} = (n - 1) \cdot 2^n + 1 k = 1 ∑ n k ⋅ 2 k − 1 = ( n − 1 ) ⋅ 2 n + 1 for all n ∈ Z + n \in \mathbb{Z}^+ n ∈ Z + .
Solution Base case (n = 1 n = 1 n = 1 ): LHS = 1 ⋅ 2 0 = 1 = 1 \cdot 2^0 = 1 = 1 ⋅ 2 0 = 1 . RHS = ( 1 − 1 ) ⋅ 2 1 + 1 = 1 = (1 - 1) \cdot 2^1 + 1 = 1 = ( 1 − 1 ) ⋅ 2 1 + 1 = 1 . True.
Inductive hypothesis: Assume ∑ k = 1 j k ⋅ 2 k − 1 = ( j − 1 ) ⋅ 2 j + 1 \displaystyle\sum_{k=1}^{j} k \cdot 2^{k-1} = (j - 1) \cdot 2^j + 1 k = 1 ∑ j k ⋅ 2 k − 1 = ( j − 1 ) ⋅ 2 j + 1 for some j ≥ 1 j \ge 1 j ≥ 1 .
Inductive step:
∑ k = 1 j + 1 k ⋅ 2 k − 1 = ∑ k = 1 j k ⋅ 2 k − 1 + ( j + 1 ) ⋅ 2 j \sum_{k=1}^{j+1} k \cdot 2^{k-1} = \sum_{k=1}^{j} k \cdot 2^{k-1} + (j+1) \cdot 2^j ∑ k = 1 j + 1 k ⋅ 2 k − 1 = ∑ k = 1 j k ⋅ 2 k − 1 + ( j + 1 ) ⋅ 2 j
= ( j − 1 ) ⋅ 2 j + 1 + ( j + 1 ) ⋅ 2 j ( b y h y p o t h e s i s ) = (j - 1) \cdot 2^j + 1 + (j+1) \cdot 2^j \quad \mathrm{(by\ hypothesis)} = ( j − 1 ) ⋅ 2 j + 1 + ( j + 1 ) ⋅ 2 j ( by hypothesis )
= [ ( j − 1 ) + ( j + 1 ) ] 2 j + 1 = 2 j ⋅ 2 j + 1 = j ⋅ 2 j + 1 + 1 = \bigl[(j-1) + (j+1)\bigr] 2^j + 1 = 2j \cdot 2^j + 1 = j \cdot 2^{j+1} + 1 = [ ( j − 1 ) + ( j + 1 ) ] 2 j + 1 = 2 j ⋅ 2 j + 1 = j ⋅ 2 j + 1 + 1
= ( j + 1 − 1 ) ⋅ 2 j + 1 + 1 = (j + 1 - 1) \cdot 2^{j+1} + 1 = ( j + 1 − 1 ) ⋅ 2 j + 1 + 1
This is the formula with n = j + 1 n = j + 1 n = j + 1 . By induction, the result holds for all n ∈ Z + n \in \mathbb{Z}^+ n ∈ Z + .
Worked Example: Taylor Series Error Bound
Use a second degree Taylor polynomial of ln ( 1 + x ) \ln(1 + x) ln ( 1 + x ) about x = 0 x = 0 x = 0 to approximate ln ( 1.2 ) \ln(1.2) ln ( 1.2 ) . Bound The error.
Solution f ( x ) = ln ( 1 + x ) , f ′ ( x ) = 1 1 + x , f ′ ′ ( x ) = − 1 ( 1 + x ) 2 , f ′ ′ ′ ( x ) = 2 ( 1 + x ) 3 f(x) = \ln(1 + x), \quad f'(x) = \frac{1}{1+x}, \quad f''(x) = \frac{-1}{(1+x)^2}, \quad f'''(x) = \frac{2}{(1+x)^3} f ( x ) = ln ( 1 + x ) , f ′ ( x ) = 1 + x 1 , f ′′ ( x ) = ( 1 + x ) 2 − 1 , f ′′′ ( x ) = ( 1 + x ) 3 2
T 2 ( x ) = f ( 0 ) + f ′ ( 0 ) x + f ′ ′ ( 0 ) 2 x 2 = 0 + x − x 2 2 T_2(x) = f(0) + f'(0)x + \frac{f''(0)}{2}x^2 = 0 + x - \frac{x^2}{2} T 2 ( x ) = f ( 0 ) + f ′ ( 0 ) x + 2 f ′′ ( 0 ) x 2 = 0 + x − 2 x 2
T 2 ( 0.2 ) = 0.2 − 0.04 2 = 0.2 − 0.02 = 0.18 T_2(0.2) = 0.2 - \frac{0.04}{2} = 0.2 - 0.02 = 0.18 T 2 ( 0.2 ) = 0.2 − 2 0.04 = 0.2 − 0.02 = 0.18
True value: ln ( 1.2 ) ≈ 0.1823 \ln(1.2) \approx 0.1823 ln ( 1.2 ) ≈ 0.1823 .
For the error bound on [ 0 , 0.2 ] [0, 0.2] [ 0 , 0.2 ] : ∣ f ′ ′ ′ ( t ) ∣ = 2 ( 1 + t ) 3 ≤ 2 |f'''(t)| = \dfrac{2}{(1+t)^3} \le 2 ∣ f ′′′ ( t ) ∣ = ( 1 + t ) 3 2 ≤ 2 for 0 ≤ t ≤ 0.2 0 \le t \le 0.2 0 ≤ t ≤ 0.2 .
∣ R 2 ( 0.2 ) ∣ ≤ 2 ⋅ ( 0.2 ) 3 6 = 2 ⋅ 0.008 6 ≈ 0.00267 |R_2(0.2)| \le \frac{2 \cdot (0.2)^3}{6} = \frac{2 \cdot 0.008}{6} \approx 0.00267 ∣ R 2 ( 0.2 ) ∣ ≤ 6 2 ⋅ ( 0.2 ) 3 = 6 2 ⋅ 0.008 ≈ 0.00267
Actual error: ∣ 0.1823 − 0.18 ∣ = 0.0023 |0.1823 - 0.18| = 0.0023 ∣0.1823 − 0.18∣ = 0.0023 Which is within the bound.
A sequence is a list of numbers marching toward a destination. Some sequences march steadily, like soldiers in step (arithmetic), while others multiply their stride at each step (geometric). The question is whether they converge — whether they settle on a value or wander off forever. The geometric series formula is the key that unlocks repeating decimals and infinite sums: 0.777… is just a geometric series with ratio one-tenth. Maclaurin series are a microscope for functions — they reveal that every smooth function is secretly an infinite polynomial. The more terms you include, the closer you zoom in on the true shape of the function near zero.
Misidentifying the first term in sigma notation. ∑ k = 0 n \sum_{k=0}^{n} ∑ k = 0 n has n + 1 n + 1 n + 1 terms, while ∑ k = 1 n \sum_{k=1}^{n} ∑ k = 1 n has n n n terms. Confusing the starting index leads to off-by-one errors in sums.
Applying the infinite sum formula when ∣ r ∣ ≥ 1 |r| \ge 1 ∣ r ∣ ≥ 1 . The formula S ∞ = a 1 1 − r S_{\infty} = \dfrac{a_1}{1 - r} S ∞ = 1 − r a 1 is valid only when ∣ r ∣ < 1 |r| \lt 1 ∣ r ∣ < 1 . For ∣ r ∣ ≥ 1 |r| \ge 1 ∣ r ∣ ≥ 1 the series diverges and the formula is meaningless.
Computing the wrong term number. The n n n -th term of a geometric sequence is a 1 r n − 1 a_1 r^{n-1} a 1 r n − 1 Not a 1 r n a_1 r^n a 1 r n . Similarly, the n n n -th term of an arithmetic sequence is a 1 + ( n − 1 ) d a_1 + (n-1)d a 1 + ( n − 1 ) d Not a 1 + n d a_1 + nd a 1 + n d .
Using the ratio test when L = 1 L = 1 L = 1 . The ratio test is inconclusive when L = 1 L = 1 L = 1 . The series ∑ 1 n \sum \dfrac{1}{n} ∑ n 1 diverges and ∑ 1 n 2 \sum \dfrac{1}{n^2} ∑ n 2 1 converges, yet both give L = 1 L = 1 L = 1 .
Weak base case in induction. The base case must match the claim. If the statement starts at n = 1 n = 1 n = 1 Proving it for n = 0 n = 0 n = 0 is not sufficient unless the domain is specified to include 0 0 0 .
Neglecting the alternating sign in the general binomial expansion. When n n n is not a positive integer, the series is infinite and the sign of each coefficient depends on the value of n n n . Substituting x → a x x \to ax x → a x also changes the sign of odd powers when a < 0 a \lt 0 a < 0 .
Confusing the Lagrange remainder with the actual error. The remainder bound ∣ R n ( x ) ∣ ≤ M ∣ x − a ∣ n + 1 ( n + 1 ) ! |R_n(x)| \le \dfrac{M|x-a|^{n+1}}{(n+1)!} ∣ R n ( x ) ∣ ≤ ( n + 1 )! M ∣ x − a ∣ n + 1 gives an upper bound , not the exact error. The actual error may be much smaller.
Forgetting convergence conditions for Maclaurin series. Each standard Maclaurin series has a specific radius of convergence. The series for ln ( 1 + x ) \ln(1+x) ln ( 1 + x ) only converges for − 1 < x ≤ 1 -1 \lt x \le 1 − 1 < x ≤ 1 ; using it outside this interval gives incorrect results.
Find the sum of the infinite geometric series 8 − 4 + 2 − 1 + ⋯ 8 - 4 + 2 - 1 + \cdots 8 − 4 + 2 − 1 + ⋯ and express the repeating decimal 0. 27 ‾ 0.\overline{27} 0. 27 as a fraction in lowest terms.
Use the ratio test to determine the convergence of ∑ n = 1 ∞ 3 n n ! \displaystyle\sum_{n=1}^{\infty} \frac{3^n}{n!} n = 1 ∑ ∞ n ! 3 n .
Find the coefficient of x 5 x^5 x 5 in the expansion of ( 1 + 3 x ) − 2 (1 + 3x)^{-2} ( 1 + 3 x ) − 2 .
Prove by induction that ∑ k = 1 n 1 k ( k + 1 ) = n n + 1 \displaystyle\sum_{k=1}^{n} \frac{1}{k(k+1)} = \frac{n}{n+1} k = 1 ∑ n k ( k + 1 ) 1 = n + 1 n for all n ∈ Z + n \in \mathbb{Z}^+ n ∈ Z + .
Find the Maclaurin series of x 1 + x 2 \dfrac{x}{1 + x^2} 1 + x 2 x up to x 7 x^7 x 7 and state the radius of convergence.
Use a third degree Maclaurin polynomial of cos x \cos x cos x to approximate cos ( 0.3 ) \cos(0.3) cos ( 0.3 ) . Bound the error using the Lagrange remainder.
An arithmetic sequence has first term 5 5 5 and common difference 3 3 3 . A geometric sequence has first term 2 2 2 and common ratio 2 2 2 . Find the smallest n n n for which the n n n -th term of the geometric sequence exceeds the n n n -th term of the arithmetic sequence.
Determine whether ∑ n = 1 ∞ ( − 1 ) n n \displaystyle\sum_{n=1}^{\infty} \frac{(-1)^n}{\sqrt{n}} n = 1 ∑ ∞ n ( − 1 ) n converges absolutely, converges conditionally, or diverges.
Differentiation is needed to derive Maclaurin and Taylor series: see Differentiation Integration connects to term-by-term integration of power series: see Integration Differential equations use series expansions as solution methods: see Differential Equations Proof and reasoning techniques including induction: see Proof flowchart TD
A[6_Sequences And Series] --> B[Key Concepts]
A --> C[Core Principles]
A --> D[Practical Applications]
B --> E[Fundamental definitions]
C --> F[Design patterns]
D --> G[Real-world usage] This topic covers the mathematical techniques and concepts related to sequences and series, including key theorems, methods, and problem-solving approaches.
Key concepts include:
arithmetic and geometric sequences series and sigma notation recurrence relations convergence tests mathematical induction Regular practice with a variety of question types is essential to build fluency and confidence in applying these mathematical techniques.