CITS1401 Problem Solving and Programming

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CITS1401
Problem Solving and Programming
Problem-solving Techniques
Semester 1, 2014
A/Prof Lyndon While
School of Computer Science & Software Engineering
The University of Western Australia
CITS1401
• CITS1401 covers
– many important problem-solving techniques used
widely in Computer Science and in programming
– writing basic programs in Python, a modern
high-level programming language
– an introduction to software engineering
• Problem-solving techniques have been covered
mostly via lab work
– in this lecture we will review the techniques covered
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Techniques discussed
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Morphological analysis and testing
Reduction and analogy
Enumeration and search
Abstraction
Divide-and-conquer
Backtracking
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Morphological analysis and testing
• Lab 2
• “morphology” is the study of patterns or forms
– occurs widely in many branches of science
• In CS/SE/IT, it occurs mostly in two contexts
– classification of inputs for processing
– classification of inputs for testing
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Classification of inputs for processing
• A term in a polynomial is defined by
its coefficient and its exponent
– what if we want to turn the term into a string?
• In the “easy case”:
(–6, 4)
→
“–6x^4”
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Classification of inputs for processing
• But what if the coefficient
== 0?
> 0?
== 1?
== –1?
(0, 4)
(6, 4)
(1, 4)
(–1, 4)
→
→
→
→
“0”
“6x^4”
“x^4”
“–x^4”
→
→
→
→
“–6”
“–6x”
“–6/x^4”
“–6/x”
• And what if the exponent
== 0?
== 1?
< 0?
== –1?
(–6, 0)
(–6, 1)
(–6, –4)
(–6, –1)
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Classification of inputs for testing
• In optional preferential voting, the voter can rank
any subset of the candidates, from one of them
up to all of them
– so any sequence of non-repeating integers
increasing from 1 is a valid vote
• If we write a function to parse OPV votes,
what would be a good set of test data?
• Assume an election with three candidates
– a vote is represented by a string
containing three characters
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Testing example: classifying votes
• Intentionally ranking all three candidates:
– i.e. permutations of 123
– 6 of these
– 123, 132, 213, 231, 312, 321
• Intentionally ranking only two candidates:
– i.e. permutations of 12<sp>
– 6 of these
– 12<sp>, 1<sp>2, <sp>12, 21<sp>, 2<sp>1, <sp>21
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Testing example: classifying votes contd.
• Intentionally ranking only one candidate:
– i.e. a 1 with two spaces
– 3 of these
– 1<sp><sp>, <sp>1<sp>, <sp><sp>1
• Accidentally ranking only two candidates:
– i.e. some non-space instead of the 3
• permutations of 124?
– 6 of these
– 124, 142, 214, 241, 412, 421
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Testing example: classifying votes contd.
• Accidentally ranking only one candidate:
– omitting the 2
• 6 permutations of 134?
– duplicating the 2
• 3 permutations of 122
• Accidentally ranking no candidates:
– omitting the 1
• 6 permutations of 234?
– replicating the 1
• 4 possibilities: 11<sp>, 1<sp>1, <sp>11, 111
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Testing example: classifying votes contd.
• All spaces:
– <sp><sp><sp>
• Over-length:
– anything with more than three characters
• Under-length:
– anything with fewer than three characters
• Already we have 43 test cases!
• Every time we change the function,
we should re-run all tests
– clearly we need a testing program!
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Reduction and analogy
• Lab 3
• Reduction is solving a new problem
by converting it into another problem
for which we already have a solution
– e.g. the problem of finding your way around
an unknown city can be reduced to the problem
of finding a map of the city
• assuming you can read maps!
• That problem can be reduced to the problem
of finding a shop that sells maps
– which can be reduced to the problem of
reading the information at the airport…
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Reduction example: building tables
• Assume that we have written a function
buildEvenTable that works for even n
• Now we need to write the function buildTable
that works for all n
• It would be huge mistake to duplicate the code
• Instead define buildTable by reducing the problem
to buildEvenTable
– plus stripDummy and some other logic
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Reduction example: running programs
• Imagine we have a Python interpreter
that can run while-loops
• Then someone says “Let’s add for-loops to Python”
• We could change the interpreter
– but that might be a lot of work, especially if it was
originally written by someone else
• Or we could use reduction
– replace each for-loop with an equivalent while-loop
for k in range(n):
<statements>
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becomes
k=0
while k < n:
<statements>
k += 1
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Reduction example: programs contd.
• Then someone says
“Let’s add list comprehensions to Python”
• Use reduction again: replace each list
comprehension with an equivalent for-loop
zs = [f(x) for k in range(n) if p(k)]
becomes
zs = []
for k in range(n):
if p(k):
zs.append(f(x))
• Note the hierarchical approach
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Enumeration and search
• Lab 4
• Very simple idea: generate all possible solutions
to the problem, and then check each one to see
if it’s correct/good
• Used widely in
– cryptography
– artificial intelligence
– game playing
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Enumeration example: verbal arithmetic
• SEND + MORE = MONEY
– consistently replace each letter with a digit
from 0, 1, …, 9 so that the arithmetic is correct
• By enumeration, we could create all 10 x 9 x … x 3
= 1,814,400 possible assignments of digits to
letters, and check each one
– notionally very easy
– computationally very expensive
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Enumeration issues
• Often “all possible solutions” is way too many!
– especially if there’s an infinite number of them…
• Often advantageous to
– rank potential solutions
• likely to find a correct/good one sooner
– use known correct/good solutions to develop
new (improved) possibilities
• e.g. hill-climbing algorithms or genetic algorithms
– randomness helps sometimes!
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Enumeration example: cryptography
• You are given a coded English message that you
know was derived using a substitution cipher
– i.e. each letter in the original was consistently
replaced by a different letter
• Using naïve enumeration gives 26 x 25 x … x 1 =
403,291,461,126,605,635,584,000,000 possibilities
• So use tricks like:
– ‘e’ probably occurs very often (& ‘a’, ‘r’, ‘t’, ‘n’, etc.)
– the sequence ‘jx’ probably never occurs (& ‘zq’, etc.)
– most occurrences of ‘q’ will be followed by a ‘u’
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Enumeration example: missionaries & cannibals
• On one side of a river are three missionaries, three
cannibals, and a canoe that can carry one or two people
– any time on either side of the river, if the number of
cannibals exceeds the number of missionaries,
something unpleasant happens
• Can you come up with a sequence of canoe trips that gets
everyone safely across the river?
• e.g. the first trip could be
–
–
–
–
M crosses: no!
MM cross: no!
C crosses: ok, but the next trip must be just him coming back
CC cross, or MC cross: maybe…
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Enumeration example: missionaries & cannibals
• By enumeration, we could create
all possible sequences
– but we need to check for loops
– and it’ll be a lot
• Instead just apply the rule “always maximise the
number of people on the far bank”
– leads almost directly to a solution!
– these sorts of guidelines are called heuristics
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Abstraction
• Lab 5
• Abstraction means simplifying a problem
as much as possible before solving it
• Examples of this principle include
–
–
–
–
operate on models instead of the “real world”
ignore some details to focus on others
discretise space and/or time (and other dimensions)
prefer simple data reps. (e.g. integers vs. dates)
• Abstraction can lead to more-general solutions
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Abstraction examples
• Graph problems
– entities as nodes, connections as arcs
– focus is on the topology
• Dates as integers
– faster, simpler, and more flexible
• Database construction
– store only relevant data
– but of course “relevant” is context-dependent…
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Abstraction
• Einstein’s Constraint: “Everything should be made
as simple as possible, but not simpler.”
– omit all details that don’t contribute to a solution
– allows you to focus on the “important bits”
– but don’t omit any important bits!
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Divide-and-conquer
• Lab 6
• Divide-and-conquer means:
– divide a problem instance into several smaller pieces
– solve each of the pieces separately
– combine their solutions to solve the original problem
• Very widely-used technique, especially for
processing data structures
• Often leads to very efficient algorithms
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Divide-and-conquer example: mergesort
• Given a list of n numbers
– [8, 0, 3, 6, 1, 7, 4, 2, 9, 5]
• Split the list down the middle
– [8, 0, 3, 6, 1]
and
[7, 4, 2, 9, 5]
• Separately sort these two lists
– [0, 1, 3, 6, 8]
and
[2, 4, 5, 7, 9]
• Merge the two sorted lists
– [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
– need only (repeatedly) compare the heads
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Divide-and-conquer example: quicksort
• Given a list of n numbers
– [8, 0, 3, 6, 1, 7, 4, 2, 9, 5]
• Choose a pivot (say 5) and partition the list
– [0, 3, 1, 4, 2]
and
[8, 6, 7, 9]
– elements smaller than the pivot in the first list
• Separately sort these two lists
– [0, 1, 2, 3, 4]
and
[6, 7, 8, 9]
• Append the two sorted lists and re-insert the pivot
– [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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Divide-and-conquer issues
• Dividing up the data equally gives
the best performance
– e.g. quicksort
• the best pivot leaves two lists with n/2 elements
• the worst pivot leaves one list with n–1, plus [ ]
• Auxiliary operations should be cheap
– e.g. merge, partition
• Larger base cases may improve performance
• Sometimes identical sub-problems arise
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Backtracking
• Lab 7
• Backtracking is a major enhancement to
enumeration and search
• Enumeration & search:
– generate all possible complete solutions,
then check each one for correctness
• Backtracking:
– build up partial solutions bit by bit,
checking for correctness at each stage
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Backtracking example: verbal arithmetic
•
SEND
MORE +
----------MONEY
– consistently replace each letter with a digit
from 0, 1, …, 9 so that the arithmetic is correct
• e.g. D + E = Y
– or D + E = Y + 10
• e.g. N + R = E
– more generally, N + R [+ 1] = E [+ 10]
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Verbal arithmetic with Enumeration & Search
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D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 7, Y = 8:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 7, Y = 9:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 7, Y = 0:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 8, Y = 7:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 8, Y = 9:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 8, Y = 0:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 9, Y = 7:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 9, Y = 8:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 9, Y = 0:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 0, Y = 7:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 0, Y = 8:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 6, M = 0, Y = 9:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 7, M = 6, Y = 8:
D = 1, E = 2, Y = 3, N = 4, R = 5, S = 7, M = 6, Y = 9:
etc.
1,814,400 possible solutions to check
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NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
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Backtracking example: verbal arithmetic
• D=1
– E=2
• Y = 0: NO – 2,520 possibilities discarded
• Y = 9, 8, 7, 6, 5, 4: NO – 15,120 more discarded
• Y=3
–N=4
» R = 5: NO – 60 more discarded
» etc.
– E=3
– etc.
• D=2
• etc.
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Backtracking issues
• Design a representation that allows building,
checking, and discarding of partial solutions
• Discard partial solutions as early as possible
• Incorporate all available clues!
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