# Algorithm Analysis

### Program Analysis

2.1. What value is returned by the following function? Express your answer as a function of ${\displaystyle n}$. Give the worst-case running time using the Big Oh notation.
  mystery(n)
r:=0
for i:=1 to n-1 do
for j:=i+1 to n do
for k:=1 to j do
r:=r+1
return(r)


2.2. What value is returned by the following function? Express your answer as a function of ${\displaystyle n}$. Give the worst-case running time using Big Oh notation.
   pesky(n)
r:=0
for i:=1 to n do
for j:=1 to i do
for k:=j to i+j do
r:=r+1
return(r)


2.3. What value is returned by the following function? Express your answer as a function of ${\displaystyle n}$. Give the worst-case running time using Big Oh notation.
   prestiferous(n)
r:=0
for i:=1 to n do
for j:=1 to i do
for k:=j to i+j do
for l:=1 to i+j-k do
r:=r+1
return(r)


2.4. What value is returned by the following function? Express your answer as a function of ${\displaystyle n}$. Give the worst-case running time using Big Oh notation.
  conundrum(${\displaystyle n}$)
${\displaystyle r:=0}$
for ${\displaystyle i:=1}$ to ${\displaystyle n}$ do
for ${\displaystyle j:=i+1}$ to ${\displaystyle n}$ do
for ${\displaystyle k:=i+j-1}$ to ${\displaystyle n}$ do
${\displaystyle r:=r+1}$
return(r)


2.5. Consider the following algorithm: (the print operation prints a single asterisk; the operation ${\displaystyle x=2x}$ doubles the value of the variable ${\displaystyle x}$).
   for ${\displaystyle k=1}$ to ${\displaystyle n}$
${\displaystyle x=k}$
while (${\displaystyle x):
print '*'
${\displaystyle x=2x}$

Let ${\displaystyle f(n)}$ be the complexity of this algorithm (or equivalently the number of times * is printed). Proivde correct bounds for ${\displaystyle O(f(n))}$, and ${\displaystyle /Theta(f(n))}$, ideally converging on ${\displaystyle \Theta (f(n))}$.

2.6. Suppose the following algorithm is used to evaluate the polynomial
${\displaystyle p(x)=a_{n}x^{n}+a_{n-1}x^{n-1}+\ldots +a_{1}x+a_{0}}$
   ${\displaystyle p:=a_{0};}$
${\displaystyle xpower:=1;}$
for ${\displaystyle i:=1}$ to ${\displaystyle n}$ do
${\displaystyle xpower:=x*xpower;}$
${\displaystyle p:=p+a_{i}*xpower}$

1. How many multiplications are done in the worst-case? How many additions?
2. How many multiplications are done on the average?
3. Can you improve this algorithm?

2.7. Prove that the following algorithm for computing the maximum value in an array ${\displaystyle A[1..n]}$ is correct.
  max(A)
${\displaystyle m:=A[1]}$
for ${\displaystyle i:=2}$ to n do
if ${\displaystyle A[i]>m}$ then ${\displaystyle m:=A[i]}$
return (m)


### Big Oh

2.8. True or False?
1. Is ${\displaystyle 2^{n+1}=O(2^{n})}$?
2. Is ${\displaystyle 2^{2n}=O(2^{n})}$?

2.9. For each of the following pairs of functions, either ${\displaystyle f(n)}$ is in ${\displaystyle O(g(n))}$, ${\displaystyle f(n)}$ is in ${\displaystyle \Omega (g(n))}$, or ${\displaystyle f(n)=\Theta (g(n))}$. Determine which relationship is correct and briefly explain why.
1. ${\displaystyle f(n)=\log n^{2}}$; ${\displaystyle g(n)=\log n}$ + ${\displaystyle 5}$
2. ${\displaystyle f(n)={\sqrt {n}}}$; ${\displaystyle g(n)=\log n^{2}}$
3. ${\displaystyle f(n)=\log ^{2}n}$; ${\displaystyle g(n)=\log n}$
4. ${\displaystyle f(n)=n}$; ${\displaystyle g(n)=\log ^{2}n}$
5. ${\displaystyle f(n)=n\log n+n}$; ${\displaystyle g(n)=\log n}$
6. ${\displaystyle f(n)=10}$; ${\displaystyle g(n)=\log 10}$
7. ${\displaystyle f(n)=2^{n}}$; ${\displaystyle g(n)=10n^{2}}$
8. ${\displaystyle f(n)=2^{n}}$; ${\displaystyle g(n)=3^{n}}$

2.10. For each of the following pairs of functions ${\displaystyle f(n)}$ and ${\displaystyle g(n)}$, determine whether ${\displaystyle f(n)=O(g(n))}$, ${\displaystyle g(n)=O(f(n))}$, or both.
1. ${\displaystyle f(n)=(n^{2}-n)/2}$, ${\displaystyle g(n)=6n}$
2. ${\displaystyle f(n)=n+2{\sqrt {n}}}$, ${\displaystyle g(n)=n^{2}}$
3. ${\displaystyle f(n)=n\log n}$, ${\displaystyle g(n)=n{\sqrt {n}}/2}$
4. ${\displaystyle f(n)=n+\log n}$, ${\displaystyle g(n)={\sqrt {n}}}$
5. ${\displaystyle f(n)=2(\log n)^{2}}$, ${\displaystyle g(n)=\log n+1}$
6. ${\displaystyle f(n)=4n\log n+n}$, ${\displaystyle g(n)=(n^{2}-n)/2}$

2.11. For each of the following functions, which of the following asymptotic bounds hold for ${\displaystyle f(n)=O(g(n)),\Theta (g(n)),\Omega (g(n))}$?

2.12. Prove that ${\displaystyle n^{3}-3n^{2}-n+1=\Theta (n^{3})}$.

2.13. Prove that ${\displaystyle n^{2}=O(2^{n})}$.

2.14. Prove or disprove: ${\displaystyle \Theta (n^{2})=\Theta (n^{2}+1)}$.

2.15. Suppose you have algorithms with the five running times listed below. (Assume these are the exact running times.) How much slower do each of these inputs get when you (a) double the input size, or (b) increase the input size by one?
(a) ${\displaystyle n^{2}}$ (b) ${\displaystyle n^{3}}$ (c) ${\displaystyle 100n^{2}}$ (d) ${\displaystyle nlogn}$ (e) ${\displaystyle 2^{n}}$

2.16. Suppose you have algorithms with the six running times listed below. (Assume these are the exact number of operations performed as a function of input size ${\displaystyle n}$.)Suppose you have a computer that can perform ${\displaystyle 10^{1}0}$ operations per second. For each algorithm, what is the largest input size n that you can complete within an hour?
(a) ${\displaystyle n^{2}}$ (b) ${\displaystyle n^{3}}$ (c) ${\displaystyle 100n^{2}}$ (d) ${\displaystyle nlogn}$ (e) ${\displaystyle 2^{n}}$ (f) ${\displaystyle 2^{2^{n}}}$

2.17. For each of the following pairs of functions ${\displaystyle f(n)}$ and ${\displaystyle g(n)}$, give an appropriate positive constant ${\displaystyle c}$ such that ${\displaystyle f(n)\leq c\cdot g(n)}$ for all ${\displaystyle n>1}$.
1. ${\displaystyle f(n)=n^{2}+n+1}$, ${\displaystyle g(n)=2n^{3}}$
2. ${\displaystyle f(n)=n{\sqrt {n}}+n^{2}}$, ${\displaystyle g(n)=n^{2}}$
3. ${\displaystyle f(n)=n^{2}-n+1}$, ${\displaystyle g(n)=n^{2}/2}$

2.18. Prove that if ${\displaystyle f_{1}(n)=O(g_{1}(n))}$ and ${\displaystyle f_{2}(n)=O(g_{2}(n))}$, then ${\displaystyle f_{1}(n)+f_{2}(n)=O(g_{1}(n)+g_{2}(n))}$.

2.19. Prove that if ${\displaystyle f_{1}(N)=\Omega (g_{1}(n))}$ and ${\displaystyle f_{2}(n)=\Omega (g_{2}(n)}$, then ${\displaystyle f_{1}(n)+f_{2}(n)=\Omega (g_{1}(n)+g_{2}(n))}$.

2.20. Prove that if ${\displaystyle f_{1}(n)=O(g_{1}(n))}$ and ${\displaystyle f_{2}(n)=O(g_{2}(n))}$, then ${\displaystyle f_{1}(n)\cdot f_{2}(n)=O(g_{1}(n)\cdot g_{2}(n))}$

2.21. Prove for all ${\displaystyle k\geq 1}$ and all sets of constants ${\displaystyle \{a_{k},a_{k-1},\ldots ,a_{1},a_{0}\}\in R}$, ${\displaystyle a_{k}n^{k}+a_{k-1}n^{k-1}+....+a_{1}n+a_{0}=O(n^{k})}$

2.22. Show that for any real constants ${\displaystyle a}$ and ${\displaystyle b}$, ${\displaystyle b>0}$
${\displaystyle (n+a)^{b}=\Omega (n^{b})}$

2.23. List the functions below from the lowest to the highest order. If any two or more are of the same order, indicate which.

${\displaystyle {\begin{array}{llll}n&2^{n}&n\lg n&\ln n\\n-n^{3}+7n^{5}&\lg n&{\sqrt {n}}&e^{n}\\n^{2}+\lg n&n^{2}&2^{n-1}&\lg \lg n\\n^{3}&(\lg n)^{2}&n!&n^{1+\varepsilon }where0<\varepsilon <1\\\end{array}}}$

2.24

2.25

2.26. List the functions below from the lowest to the highest order. If any two or more are of the same order, indicate which.

${\displaystyle {\begin{array}{lll}{\sqrt {n}}&n&2^{n}\\n\log n&n-n^{3}+7n^{5}&n^{2}+\log n\\n^{2}&n^{3}&\log n\\n^{\frac {1}{3}}+\log n&(\log n)^{2}&n!\\\ln n&{\frac {n}{\log n}}&\log \log n\\({1}/{3})^{n}&({3}/{2})^{n}&6\\\end{array}}}$

2.27. Find two functions ${\displaystyle f(n)}$ and ${\displaystyle g(n)}$ that satisfy the following relationship. If no such ${\displaystyle f}$ and ${\displaystyle g}$ exist, write None.
1. ${\displaystyle f(n)=o(g(n))}$ and ${\displaystyle f(n)\neq \Theta (g(n))}$
2. ${\displaystyle f(n)=\Theta (g(n))}$ and ${\displaystyle f(n)=o(g(n))}$
3. ${\displaystyle f(n)=\Theta (g(n))}$ and ${\displaystyle f(n)\neq O(g(n))}$
4. ${\displaystyle f(n)=\Omega (g(n))}$ and ${\displaystyle f(n)\neq O(g(n))}$

2.28. True or False?
1. ${\displaystyle 2n^{2}+1=O(n^{2})}$
2. ${\displaystyle {\sqrt {n}}=O(\log n)}$
3. ${\displaystyle \log n=O({\sqrt {n}})}$
4. ${\displaystyle n^{2}(1+{\sqrt {n}})=O(n^{2}\log n)}$
5. ${\displaystyle 3n^{2}+{\sqrt {n}}=O(n^{2})}$
6. ${\displaystyle {\sqrt {n}}\log n=O(n)}$
7. ${\displaystyle \log n=O(n^{-1/2})}$

2.29. For each of the following pairs of functions ${\displaystyle f(n)}$ and ${\displaystyle g(n)}$, state whether ${\displaystyle f(n)=O(g(n))}$, ${\displaystyle f(n)=\Omega (g(n))}$, ${\displaystyle f(n)=\Theta (g(n))}$, or none

of the above.

1. ${\displaystyle f(n)=n^{2}+3n+4}$, ${\displaystyle g(n)=6n+7}$
2. ${\displaystyle f(n)=n{\sqrt {n}}}$, ${\displaystyle g(n)=n^{2}-n}$
3. ${\displaystyle f(n)=2^{n}-n^{2}}$, ${\displaystyle g(n)=n^{4}+n^{2}}$

(a) If I prove that an algorithm takes ${\displaystyle O(n^{2})}$ worst-case time, is it possible that it takes ${\displaystyle O(n)}$ on some inputs?
(b) If I prove that an algorithm takes ${\displaystyle O(n^{2})}$ worst-case time, is it possible that it takes ${\displaystyle O(n)}$ on all inputs?
(c) If I prove that an algorithm takes ${\displaystyle \Theta (n^{2})}$ worst-case time, is it possible that it takes ${\displaystyle O(n)}$ on some inputs?
(d) If I prove that an algorithm takes ${\displaystyle \Theta (n^{2})}$ worst-case time, is it possible that it takes ${\displaystyle O(n)}$ on all inputs?
(e) Is the function ${\displaystyle f(n)=\Theta (n^{2})}$, where ${\displaystyle f(n)=100n^{2}}$ for even ${\displaystyle n}$ and ${\displaystyle f(n)=20n^{2}-n\log _{2}n}$ for odd ${\displaystyle n}$?

2.31. For each of the following, answer yes, no, or can't tell. Explain your reasoning.

(a) Is ${\displaystyle 3^{n}=O(2^{n})}$?
(b) Is ${\displaystyle \log 3^{n}=O(\log 2^{n})}$?
(c) Is ${\displaystyle 3^{n}=\Omega (2^{n})}$?
(d) Is ${\displaystyle \log 3^{n}=\Omega (\log 2^{n})}$?

2.32. For each of the following expressions ${\displaystyle f(n)}$ find a simple ${\displaystyle g(n)}$ such that

${\displaystyle f(n)=\Theta (g(n))}$.

1. ${\displaystyle f(n)=\sum _{i=1}^{n}{1 \over i}}$.
2. ${\displaystyle f(n)=\sum _{i=1}^{n}\lceil {1 \over i}\rceil }$.
3. ${\displaystyle f(n)=\sum _{i=1}^{n}\log i}$.
4. ${\displaystyle f(n)=\log(n!)}$.

2.33. Place the following functions into increasing asymptotic order.

${\displaystyle f_{1}(n)=n^{2}\log _{2}n}$, ${\displaystyle f_{2}(n)=n(\log _{2}n)^{2}}$, ${\displaystyle f_{3}(n)=\sum _{i=0}^{n}2^{i}}$, ${\displaystyle f_{4}(n)=\log _{2}(\sum _{i=0}^{n}2^{i})}$.

2.34. Which of the following are true?
1. ${\displaystyle \sum _{i=1}^{n}3^{i}=\Theta (3^{n-1})}$.
2. ${\displaystyle \sum _{i=1}^{n}3^{i}=\Theta (3^{n})}$.
3. ${\displaystyle \sum _{i=1}^{n}3^{i}=\Theta (3^{n+1})}$.

2.35. For each of the following functions ${\displaystyle f}$ find a simple function ${\displaystyle g}$ such that ${\displaystyle f(n)=\Theta (g(n))}$.
1. ${\displaystyle f_{1}(n)=(1000)2^{n}+4^{n}}$.
2. ${\displaystyle f_{2}(n)=n+n\log n+{\sqrt {n}}}$.
3. ${\displaystyle f_{3}(n)=\log(n^{20})+(\log n)^{10}}$.
4. ${\displaystyle f_{4}(n)=(0.99)^{n}+n^{100}.}$

2.36. For each pair of expressions ${\displaystyle (A,B)}$ below,

indicate whether ${\displaystyle A}$ is ${\displaystyle O}$, ${\displaystyle o}$, ${\displaystyle \Omega }$, ${\displaystyle \omega }$, or ${\displaystyle \Theta }$ of ${\displaystyle B}$. Note that zero, one or more of these relations may hold for a given pair; list all correct ones.
${\displaystyle {\begin{array}{lcc}&A&B\\(a)&n^{100}&2^{n}\\(b)&(\lg n)^{12}&{\sqrt {n}}\\(c)&{\sqrt {n}}&n^{\cos(\pi n/8)}\\(d)&10^{n}&100^{n}\\(e)&n^{\lg n}&(\lg n)^{n}\\(f)&\lg {(n!)}&n\lg n\end{array}}}$

2.37
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2.39
2.40
2.41
2.42
2.43

2.44
2.45
2.46
2.47

### Interview Problems

2.48
2.49
2.50
2.51
2.52
2.53
2.54
2.55

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