Difference between revisions of "Chapter 1"

From The Algorithm Design Manual Solution Wiki
Jump to navigation Jump to search
Line 1: Line 1:
 
Problems
 
Problems
  
:[[1.1]]. Show that ''a'' + ''b'' can be less than ''min(a, b)''.
+
:[[1.1]]. Show that <math>a + b</math> can be less than <math>\min(a,b)</math>.
  
  
:1.2. Show that ''a'' × ''b'' can be less than ''min(a, b)''.
+
:1.2. Show that <math>a \times b</math> can be less than <math>\min(a,b)</math>.
  
  
:[[1.3]]. Design/draw a road network with two points ''a'' and ''b'' such that the fastest route between ''a'' and ''b'' is not the shortest route.
+
:[[1.3]]. Design/draw a road network with two points <math>a</math> and <math>b</math> such that the fastest route between <math>a</math> and <math>b</math> is not the shortest route.
  
  
:1.4. Design/draw a road network with two points ''a'' and ''b'' such that the shortest route between ''a'' and ''b'' is not the route with the fewest turns.
+
:1.4. Design/draw a road network with two points <math>a</math> and <math>b</math> such that the
 +
shortest route between <math>a</math> and <math>b</math> is not the route with the fewest turns.
  
  
:[[1.5]]. The ''knapsack problem'' is as follows: given a set of integers ''S'' = {''s1, s2. . . , sn''}, and a target number ''T'', find a subset of ''S'' that adds up exactly to ''T''. For example, there exists a subset within ''S'' = {1, 2, 5, 9, 10} that adds up to ''T'' = 22 but not ''T'' = 23.
+
:[[1.5]].The ''knapsack problem''  
 +
is as follows: given a set of integers <math>S = \{s_1, s_2, \ldots, s_n\}</math>, and
 +
a target number <math>T</math>, find a subset of <math>S</math> which adds up
 +
exactly to <math>T</math>.
 +
For example, there exists a subset within <math>S = \{1, 2, 5, 9, 10\}</math> that
 +
adds up to <math>T=22</math> but not <math>T=23</math>.
 +
Find counterexamples to each of the following algorithms for the knapsack
 +
problem.
 +
That is, giving an <math>S</math> and <math>T</math> such that the subset is
 +
selected using the algorithm does not leave the knapsack completely full,
 +
even though such a solution exists.
 +
#Put the elements of <math>S</math> in the knapsack in left to right order if they fit, i.e. the first-fit algorithm.
 +
#Put the elements of <math>S</math> in the knapsack from smallest to largest, i.e. the best-fit algorithm.
 +
#Put the elements of <math>S</math> in the knapsack from largest to smallest.
  
:Find counterexamples to each of the following algorithms for the knapsack problem. That is, give an ''S'' and ''T'' where the algorithm does not find a solution that leaves the knapsack completely full, even though a full-knapsack solution exists.
 
  
::(a) Put the elements of ''S'' in the knapsack in left to right order if they fit, that is, the first-fit algorithm.
+
:1.6. The ''set cover problem''
 
+
is as follows: given a set of subsets <math> S_1, ..., S_m</math>
::(b) Put the elements of ''S'' in the knapsack from smallest to largest, that is, the best-fit algorithm.
+
of the universal set <math>U = \{1,...,n\}</math>,
 
+
find the smallest subset of subsets <math>T \subset S</math>
::(c) Put the elements of ''S'' in the knapsack from largest to smallest.
+
such that <math>\cup_{t_i \in T} t_i = U</math>.
 
+
For example, there are the following subsets, <math>S_1 = \{1, 3, 5\}</math>,
 
+
<math>S_2 = \{2,4\}</math>, <math>S_3 = \{1,4\}</math>, and <math>S_4 = \{2,5\}</math>
:1.6. The ''set cover problem'' is as follows: given a set ''S'' of subsets ''S1, . . . , Sm'' of the universal set ''U'' = {1, ..., ''n''}, find the smallest subset of subsets ''T S'' such that ''∪ti∈T ti'' = ''U''. For example, consider the subsets ''S1'' = {1, 3, 5}, ''S2'' = {2, 4}, ''S3'' = {1, 4}, and ''S4'' = {2, 5}. The set cover of {1, . . . , 5} would then be ''S1'' and ''S2''.
+
The set cover would then be <math>S_1</math> and <math>S_2</math>.
:Find a counterexample for the following algorithm: Select the largest subset for the cover, and then delete all its elements from the universal set. Repeat by adding the subset containing the largest number of uncovered elements until all are covered.
+
Find a counterexample for the following algorithm:
 +
Select the largest subset for the cover,
 +
and then delete all its elements from the universal set.
 +
Repeat by adding the subset containing the largest number of
 +
uncovered elements until all are covered.
  
  

Revision as of 20:07, 23 August 2020

Problems

1.1. Show that can be less than .


1.2. Show that can be less than .


1.3. Design/draw a road network with two points and such that the fastest route between and is not the shortest route.


1.4. Design/draw a road network with two points and such that the

shortest route between and is not the route with the fewest turns.


1.5.The knapsack problem

is as follows: given a set of integers , and a target number , find a subset of which adds up exactly to . For example, there exists a subset within that adds up to but not . Find counterexamples to each of the following algorithms for the knapsack problem. That is, giving an and such that the subset is selected using the algorithm does not leave the knapsack completely full, even though such a solution exists.

  1. Put the elements of in the knapsack in left to right order if they fit, i.e. the first-fit algorithm.
  2. Put the elements of in the knapsack from smallest to largest, i.e. the best-fit algorithm.
  3. Put the elements of in the knapsack from largest to smallest.


1.6. The set cover problem

is as follows: given a set of subsets of the universal set , find the smallest subset of subsets such that . For example, there are the following subsets, , , , and The set cover would then be and . Find a counterexample for the following algorithm: Select the largest subset for the cover, and then delete all its elements from the universal set. Repeat by adding the subset containing the largest number of uncovered elements until all are covered.


1.7. The maximum clique problem in a graph G = (V, E) asks for the largest subset C of vertices V such that there is an edge in E between every pair of vertices in C. Find a counterexample for the following algorithm: Sort the vertices of G from highest to lowest degree. Considering the vertices in order of degree, for each vertex add it to the clique if it is a neighbor of all vertices currently in the clique. Repeat until all vertices have been considered.


1.8. Prove the correctness of the following recursive algorithm to multiply two natural numbers, for all integer constants c ≥ 2.
Multiply(y, z)
if z = 0 then return(0) else
return(Multiply(cy, [z/c]) + y · (z mod c))


1.9. Prove the correctness of the following algorithm for evaluating a polynomial anxn + an−1xn−1 + · · · + a1x + a0.
Horner(a, x)
p = an
for i from n − 1 to 0
p = p · x + ai
return p


1.10
1.11
1.12
1.13
1.14
1.15
1.16
1.17
1.18
1.19
1.20
1.21
1.22
1.23
1.24
1.25
1.26
1.27
1.28
1.29
1.30
1.31
1.32
1.33
1.34
1.35
1.36
1.37
1.38


Back to Chapter List