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CS314: Formal languages and automata theory L. Nada ALZaben. Chapter 7 . Time Complexity. Quick Note. don’t forget to read chapter 6section 6.1 and 6.2 Always check the blog for new updates: Cs314pnu.wordpress.com. Quick revision. Lecture #16. - PowerPoint PPT Presentation
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Chapter 7. Time Complexity
CS314: FORMAL LANGUAGES AND AUTOMATA THEORYL. NADA ALZABEN
2
Quick Note don’t forget to read
chapter 6section 6.1 and 6.2
Always check the blog for new updates:
Cs314pnu.wordpress.com
Computer Science Department
Quick revision The theory of computation is divided over
three parts: Part1: Automata Part2: Computability Part3: Complexity.
→ Complexity
→ Computabilit
y → Automata
Computer problems (hard or
easy)Ch:7- end of
book
Problem can be solved
(true – false) Ch:3-6
Deal with definitions
and properties of mathematic
al model (FA – CFG)
Ch:1-2
Lecture #16
Time complexity Even when a problem is decidable and
computationally solvable it may not be solvable in practice if the solution requires an inordinate amount of time and space.
Complexity theory: is an investigation of the time, memory or other resources required for solving computational problems.
7.1 measuring complexity How much time does a Turing Machine
tape need to decide language A?
Describe TM ,M1, at a low level and count the number of steps that m1 uses when it runs.
7.1 measuring complexity The number of steps that an algorithm uses on
a particular input may depend on several parameters but for simplicity we compute as a function of the length of the string representing the inputs.
In worst-case analysis consider the longest running time of all inputs of a particular length.
In average-case analysis consider the average running time of all inputs of a particular length.
7.1 measuring complexity
Big-O and Small-O notation Asymptotic analysis one convenient
form of estimation. By just considering the highest order term of the expression and ignore any coefficient and any lower order term .
For example:
The asymptotic notation or Big-O notation is
Big-O and Small-O notation
Big-O and Small-O notation Example:
Example:
Example: Let f3(n) be the function
Big-O and Small-O notation
Example:
Analyzing algorithms Lecture #17
Analyzing algorithms
Time () contains all languages that can be decided with time complexity O () so we can say for the language A:
Analyzing algorithms Machine M2 uses different algorithm to
decide on language A to give a shorter time than M1.
Analyzing algorithms Machine M3 uses second tape and uses a
different algorithm to decide on language A (linear time )
Complexity Relationships among models
The choice of computational model can effect the time complexity of languages.
Three models used:1. The single tape TM.2. The multitape TM.3. Nondeterministic TM
Complexity Relationships among models
Complexity Relationships among models
Complexity Relationships among models
7.2 The Class P Previous theorems show two important
distinction: Polynomial difference in running time is small Exponential difference in running time is large. Ex: when n=1000.
Polynomial Time: Algorithms are fast enough for many
purposes unlike the Exponential Time. (ex. Brute-force search)
Polynomially equivalent. (ex. Deterministic single and multi tape TM’s)
Lecture #18
7.2 The Class P
The class P plays an important role because:1. P is invariant for all models of computation that are
Polynomially equivalent to the deterministic single-tape TM.
2. P roughly corresponds to the class of problems that are realistically solvable on a computer.
7.2 The Class P Giving a polynomial time algorithm in a high-level
description with out referring to a particular computation model or even details of tapes and head motion.
Algorithms are described with numbered stages. Stage of an algorithm might be implemented by several
steps in TM. To give the polynomial time of an algorithm :1. Give the polynomial upper bound on the number of
stages that an algorithm uses when running on input of length n.
2. Examine the individual stages to be sure that each can be implemented in polynomial time on a deterministic model.
7.2 The Class P Examples of problems in P: Encoding of graphs – an encoding of a graph is a list
of its nodes and edges. (adjacency matrix : the (i,j)th entry is 1 if there is an edge from I to j) and 0 when not.
The running time on graphs is computed by the number of nodes instead of the size of the node. So it gives a polynomial time on the number of nodes and then it is polynomial in the size of the input.
In the directed graph G contains nodes s and t the PATH problem is to find a directed path between the nodes s and t. as in the figure.
7.2 The Class P Examples of problems in P: PATH
problem is there a path from s to t?
7.2 The Class P
7.2 The Class P Examples of problems in P: RELPRIME
problem is deciding if two numbers are relatively prime?
7.2 The Class P
Greatest common divisor gcd(x,y)
7.2 The Class P Using a powerful technique called
dynamic programming
7.2 The Class P
7.3 The Class NP We previously produced the polynomial time
and stated its is faster than the exponential one.
Some problems was solved by brute-force search and we had an alternative solution to have it in polynomial time
Sometimes this can not be obtain. Example: Hamiltonian path in directed
graph G is a directed path that goes through each node exactly once.
7.3 The Class NP
7.3 The Class NP By modifying the PATH algorithm that was solved by
brute-force search (just before it was solved by a polynomial time) we can obtain an algorithm that can decide if the path is Hamiltonian.
The HAMPATH problem have feature called polynomial verifiability which is used for its complexity.
Although deciding if a path is Hamiltonian requires an exponential time it may be solved and proven to be existed. This is called verifying.
Verifying the existence of Hamiltonian path is easier than determining its existence.
Example: of polynomial verifiability is compositeness. (composite number is a none prime number)
7.3 The Class NP
HAMPATH and COMPOSITES are members of NP.
COMPOSITES are members of P too. P is a subset of NP.
7.3 The Class NP NTM that decides HAMPATH problem in NP:
Although each stage is decided in a polynomial time this algorithm runs in nondeterministic polynomial time.
7.3 The Class NP
7.3 The Class NP Example of problem in NP: A clique in an undirected graph is a sub-
graph where every two nodes are connected by an edge.
A k-clique is a clique that contain k nodes.
7.3 The Class NP Example
CSC314 Course is over