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Algorithms and Data Structures 1
 Syllabus
 Asymptotical notation
 (Binary trees,) AVL trees, Red-Black trees
 B-trees
 Hashing
 Graph alg: searching, topological sorting,
strongly connected components
 Minimal spaning tree (d.s. Union-Find)
 Divide et Impera method
 Sorting: low approximation of sorting problem,
average case of Quicksort, randomization of
Quicksort, linear sorting alg.
 Algebraic alg. (LUP decomposition)
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Literature
T.H. Cormen, Ch.E. Leiserson, R.L. Rivest,
Introduction to Algorithms, MIT Press, 1991
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Organization
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Lecture
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Seminar
Comparing of algorithms
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Measures:
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Time complexity, in (elementary) steps
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Space complexity, in words/cells
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Communication complexity, in packets/bytes
How it is measured:
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Worst case, average case (wrt probability distribution)
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Usually approximation: upper bound
Using functions depending on size of input data
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We abstract from particular data to data size
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We compare functions
Size of data
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Q: How to measure the size of (input) data?
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Formally: number of bits of data
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Ex: Input are (natural) numbers
size D of input data is
,
Time complexity: a function f: N->N, such that
f(|D|) gives number of algorithm steps
depending on data of the size |D|
Intuitively: Asymptotical behaviour: exact graph
of function f does not matter (ignoring additive
and multiplicative constants), a class of f
matters (linear, quadratic, exponential)
Step of algorithm

In theory: Based on a abstract machine:
Random Access Machine (RAM), Turing m.

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Informally: algorithm step = operation executable in
constant time (independent of data size)
RAM, operations:
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Arithmetical: +, -, *, mod, <<, && …
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Comparision of two numbers
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Assignment of basic data types (not for arrays)
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Numbers have fixed maximal size
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Ex: sorting of n numbers: |D| = n
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(Contra)Ex: test for zero vector
Why to measure time comlexity
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Why sometimes more quickly machine doesn't help
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Difference between polynomial and worse algs.
Asymptotical complexity
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Measures a behaviour of the algorithm on „big“
data

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It supress additive and multiplicative constants
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It ignores a finite number of exceptions
Abstracts from processor, language, (Moore law)
Classifies algs to categories: linear, quadratic,
logarithmic, exponential, constant ...
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Compares functions
(Big) O notation, definitions
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f(n) is asymptotically less or equal g(n),
notation
iff
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f(n) is asymptotically greater or equal g(n),
notation
, „big Omega“
iff
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f(n) is asymptotically equal g(n),
notation
iff
, „big Theta“
O-notation, def. II
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f(n) is asymptotically strictly less than g(n),
notation
, „small o“
iff
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f(n) is asymptotically strictly greater than g(n),
notation
, „small omega“
iff
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Examples of classes: O(1), log log n, log n, n,
n log n, n^2, n^3, 2^n, 2^2n, n!, n^n, 2^(2^n),...
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Some functions are incomparable
Exercise
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Notation: sometimes is used f=O(g)
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Prove: max(f,g) ɛ Θ(f+g)
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Prove: if c,d>0, g(n)=c.f(n)+d then g ɛ O(f)
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Ex.: if f ɛ O(h), g ɛ O(h) then f+g ɛ O(h)
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Appl: A bound to sequence of commands
Compare n+100 to n^2, 2^10 n to n^2
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Simple algorithms (with low overhead) are
sometimes better for small data
Dynamic sets
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Data structures for remembering some data
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Dynamic structure: changes in time
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An element of a dynamic d.s. is accesible
through a pointer and has
1. A key, usually from some (lineary) ordered set
2. Pointer(s) to other elements, or parts of d.s.
3. Other (user) data (!)
Operations
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S is a dynamic set, k is a key, x is a pointer to a
element
Operations
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Find(S,k) – returns a pointer to a element with the
key k or NIL
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Insert(S,x) – Inserts to S an element x
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Delete(S, x) – Deletes from S an element x
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Min(S) – returns a pointer to an element with
minimal key in S
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Succ(S,x) – returms a pointer to an element next to
x (wrt. linear ordering)
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Max(S), Predec(S,x) – analogy to Min, Succ
Binary search trees
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Dynamic d.s. which supports all operations