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Summary Computer Science 144 A2 summaries

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Excellently summarised notes for Computer Science 144 (Stellenbosch University) - all topics needed for the A2 are covered: 4.1, 4.2, 4.4, and 4.5 4.1 Analysis of Algorithms 4.2 Sorting and Searching 4.4 Symbol Tables 4.5 Case Study: Small-World Phenomenon The summaries are neatly di...

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  • October 28, 2022
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miaolivier16
4.1. performance -
analysis of algorithms


Algorithm:
a method for solving a problem that is suitable for implementation as a computer program

Basic
principle:
Scientific method (3-step approach
pay attention to cost of
observe feature of the natural world To
some running programs.
·




·

hypothesize a model that is consistent with these observations study cost, we apply
scientific methods of

predict events using this hypothesis
·




Study
the predictions by making further observations
verify aswell mathematical
·
as


·
validate by repeating until the hypothesis and observations agree analysis to derive concise


models of cost



Reasons to analyze algorithms
① Predict program behaviour The experiments we design

my program finish? be reproducible and
·
when will must


be falsifiable
will
my program finish? hypotheses
·

must


&
compare algorithms and implementations cable to be
proven false)

my program faster?
·
will this change make

·

how can I make my
program faster?
⑤ the
To develop a basis for understanding problem and for designing new algorithms
·
enables new technology
·
enables new research



Al porithmic successes


sorting
NE
·

Rearrange array of h-items in ascending order

·
used in databases, scheduling, stats, genomics, ...




·
force:N" Steps
Brute
NIOPN
·merpesort: NogN steps > enables new technology
N

, Discrete Fourier Transform

·break down waveform of N samples into periodic components

·
used in Dup, JPC6, MRI, astrophysics, ...




·
Brute force:N" Steps

· fFT algorithm: Nogn steps > enables new technology


Observations


measuring the of the stopwatch data type
exact
running time a
program: use



run a
program on various inputs
first observation: there is "problem size"
qualitative a



the problem size characterizes the difficulty of the computational task


normally, the problem size is either:

·
the size of the input or



the value of command-line
a
argument
·




the time should increase with the problem (but, by how much?)
running size




e.g. Threesum
java counts the number of triples in an
array of
integers that sums to 0




Hypotheses
·
Daniel Knuth showed that it is possible to create an accurate model that can predict program

running-time.
of:
This
requires detailed understanding
·




~ the program
~the system and computer
and advanced tools of mathematical analysis

, Log-log plot
Lop-logplot
Initial
hypothesis:
Running time approximately obeys a
power law TCN) = and standard plot

Data analysis:
Plot running time vs.
Input size N on a log-log scale


consequence
law line.
·
power
yields straight
slope b =




Refined hypothesis slope
-



Running time grows as cube of input size: aNe

/log-logplot is a
straight line with slope 3) TIN = and




Hypothesis:
time is about and with b 19(
Running =




Doubling hypothesis
xI
Quick way to estimate b in a power law hypothesis ↑
E

Run the
program, doubling the size of the input. I




2
x

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