This document discusses algorithms for solving the feedback vertex set problem, which aims to find the minimum number of nodes that need to be removed from a graph to make it acyclic. It describes several algorithms including a naive algorithm, fixed parameter tractable algorithm, 2-approximation algorithm, disjoint feedback vertex set algorithm, and randomized algorithm. For each algorithm, it provides definitions, pseudocode, and an example to illustrate how it works. The document concludes that this problem remains an active area of research to develop more efficient algorithms.
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems.
Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.
Dijkstra's algorithm was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later. He received the Turing award in 1972. The Dijkstra prize is named after him which is given for outstanding papers on the principles of distributed computing. One of his famous quote is that Computer Science is no more about computers than astronomy is about telescopes.
Dijkstra's algorithm solves the shortest-path problem for any weighted graph with non-negative weights and finds shortest distance between 2 vertices. The algorithm creates the tree of the shortest paths from the starting source vertex from all other points in the graph. Works on both directed and undirected graph. It differs from the minimum spanning tree as the shortest distance between two vertices may not be included in all the vertices of the graph.
This presentation gives a conceptual idea of a graphical algorithm that is Dijkstra's Algorithm. Includes general introduction , the pseudocode, code in form of QR Card, graphical. Also discussing the algorithm in form of graphical images and nodes. Also it include the complexity and application of algorithm in various ranges of fields. This is a fun, eye-catching, conceptual presentation, best suited for students into engineering.
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems.
Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.
Dijkstra's algorithm was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later. He received the Turing award in 1972. The Dijkstra prize is named after him which is given for outstanding papers on the principles of distributed computing. One of his famous quote is that Computer Science is no more about computers than astronomy is about telescopes.
Dijkstra's algorithm solves the shortest-path problem for any weighted graph with non-negative weights and finds shortest distance between 2 vertices. The algorithm creates the tree of the shortest paths from the starting source vertex from all other points in the graph. Works on both directed and undirected graph. It differs from the minimum spanning tree as the shortest distance between two vertices may not be included in all the vertices of the graph.
This presentation gives a conceptual idea of a graphical algorithm that is Dijkstra's Algorithm. Includes general introduction , the pseudocode, code in form of QR Card, graphical. Also discussing the algorithm in form of graphical images and nodes. Also it include the complexity and application of algorithm in various ranges of fields. This is a fun, eye-catching, conceptual presentation, best suited for students into engineering.
One of the main reasons for the popularity of Dijkstra's Algorithm is that it is one of the most important and useful algorithms available for generating (exact) optimal solutions to a large class of shortest path problems. The point being that this class of problems is extremely important theoretically, practically, as well as educationally.
A talk I gave at Creative Crew (Singapore) on 12 August 2016 to introduce newcomers to the Raspberry Pi.
Video link of this talk can be found here: https://engineers.sg/v/955
Code used in the talk can be found here: https://github.com/yeokm1/getting-started-with-rpi
LAS16-112: mbed OS Technical Overview
Speakers: Sam Grove
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★ Session Description ★
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One of the main reasons for the popularity of Dijkstra's Algorithm is that it is one of the most important and useful algorithms available for generating (exact) optimal solutions to a large class of shortest path problems. The point being that this class of problems is extremely important theoretically, practically, as well as educationally.
A talk I gave at Creative Crew (Singapore) on 12 August 2016 to introduce newcomers to the Raspberry Pi.
Video link of this talk can be found here: https://engineers.sg/v/955
Code used in the talk can be found here: https://github.com/yeokm1/getting-started-with-rpi
LAS16-112: mbed OS Technical Overview
Speakers: Sam Grove
Date: September 26, 2016
★ Session Description ★
ARM mbed OS is an open source embedded operating system designed
specifically for the “things” in the Internet of Things. It includes all the features you need to develop a connected product based on very small memory footprint ARM Cortex-M microcontrollers, including security,connectivity, an RTOS, and drivers for sensors and I/O devices. You can start developing with mbed OS 5.1.0 today using a choice of 40 different development boards from 11 different providers and a wide choice of toolchains including a complete command line build management and configuration tool mbed CLI, industry standard desktop IDEs or ARM’s free online IDE.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-112
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-112/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
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Metric dimension in graph theory has many applications in the real world. It has been applied to the
optimization problems in complex networks, analyzing electrical networks; show the business relations,
robotics, control of production processes etc. This paper studies the metric dimension of graphs with
respect to contraction and its bijection between them. Also an algorithm to avoid the overlapping between
the robots in a network is introduced.
We consider here k-valent plane and toroidal maps with faces of size a and b. The faces are said to be in a lego if the faces are organized in blocks that then tile the sphere. We expose some enumeration results and the technical enumeration methods.
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Accelerate Enterprise Software Engineering with PlatformlessWSO2
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3. 1. Introduction
This paper deals with one of the first NP complete problems : FeedBack Vertex Set.
NP complete category problems are hard to solve. In general, algorithms providing a solution
to a problem of these are rather slow. That’s why, these solutions are unusable in practice
(although on a reduced instance of a problem).
However, it is possible to verify a solution to a NP complete problems in polynomial time.
At present, we know that it is sufficient to find a polynomial solution to a NP-complete
problem in order to solve them all. This is the subject of the biggest computer science prob-
lem : P versus NP.
In the rest of this paper, we will analyze different algorithms to solve this problem. FVS
is the subject of much research (still ongoing), solutions provide an improvement in time
complexity (they tend to Θ(polynomial)).
What is FVS ? This is a problem related to cyclic graph. The goal is to find a mini-
mum number of nodes "playing a role" in cycles of a graph. By removing these nodes of the
graph is obtained an acyclic graph. More formally, FVS is the set of N nodes of a cyclic
graph G whose deletion makes the graph G acyclic.
This problem is a major problem in combinatorial optimization. It is frequently encoun-
tered in operating systems, databases (management of deadlocks), creation of printed circuits
(VLSI), compilers optimizing ...
4. 2. Strategies
2.1 Naive Algorithm
A naive algorithm is often an intuitive solution. Here, it would be to test all combinations
C of vertices of graph G. If G − C makes G acyclic then C is a FV S of G.
This approach is set aside in view of its time complexity elevated.
2.2 Fixed Parameter Tractable
2.2.1 Definitions and notations
u
a
b
c
d
e
f
Figure 2.1: u is an u-flower of order 2 : u has two disjoints cycles but u in common. This
cycles are petals and u is a "flower with two petals".
5. 2.2 Fixed Parameter Tractable 3
ts
a
b
c
d
e
f
Figure 2.2: u-flower computation : We split u into two nodes t and s. The subgraph s
contains all neighbors nodes v of s such that (vs) is a directed "incoming" arc (there is a
oriented path from v to s). The subgraph t contains all neighbors nodes with "outgoing"
arc. The maximal number of flower is the number of disjoint path (st), called petal(u).
2.2.2 Algorithm
A Fixed Parameter Tractable algorithm (FPT) solves a problem in Θ(f(k)polynomial(n)).
Therefore, It does not only depend on the input size of the problem. Thus, for relatively
small values of k, we hope to obtain a solution in Θ(polynomial(n)).
FPT Algorithm
-----
Input : Graph G = (V,E)
Output : k-FVS of G
Choose a (k+1) nodes subgraph H of G
Choose a (k) nodes subgraph I of H
while H != G
Add a node of G into H and I
Compute FVS -Reduction on I
if FVS -Reduction fails
return G does not have k-FVS
return I
To improve the quality of this algorithm, we can define heuristics in order to get better
subgraph H and I. Let’s give some heuristic ideas :
• Select by greedy method, maximum degree nodes which make up G acyclic.
• We compute an -approximation (see 2.4) of G. Then, we use previous heuristic on
-approximation nodes.
6. 2.2 Fixed Parameter Tractable 4
FV S − Reduction is a set of rules to apply on a graph. This algorithm performs
FV S − Reduction on I (I is a (k + 1)FV S of H) to reduce it to k.
Let I = ∅ and we going to give rules to carry out during FV S − Reduction :
u
Figure 2.3: Add u to I, decrease k and remove u and adjacent edges of u from the graph.
u v
u v
Figure 2.4: Remove all edge of (uv) but one.
u Graphe
Graphe
Figure 2.5: If u does not have any incoming edge or outgoing edge then remove u and its
incident edges from the graph.
u v
merge
Figure 2.6: If there is only single path between u and v then merge u and v.
uGraphe
a
b
c
7. 2.2 Fixed Parameter Tractable 5
Graphe
a
b
c
Figure 2.7: If petal(u) = 1, remove u and its incident edges from Graphe. Create an edge
between u neighbors in Graphe and nodes of the cycle.
u
Graphe
a
b
c
d
e
f
Figure 2.8: If petal(u) > |k|, add u to I, decrease k, remove u and its incident edges from
Graphe.
2.2.3 Example
A
B
C
D
E F
Figure 2.9: We set FTP to k = 2 for this graph G.
8. 2.2 Fixed Parameter Tractable 6
A
B
C
Figure 2.10: We choose a k + 1 = 3 nodes subgraph H of G following the first heuristic (see
2.2.2).
A
C
Figure 2.11: We choose a k = 2 nodes subgraph I of H and we execute rules 4 of FV S −
Reduction, we merge C and A (see 2.6).
B
A
D
E F
Figure 2.12: The graph G is equals to H and FV S − Reduction has succeeded on I. Then,
I is a 2 − FV S of G.
9. 2.3 Disjoint-FVS 7
2.3 Disjoint-FVS
2.3.1 Definitions et notations
Let a graph G(V, E) and FV S F of G. If we can compute a k nodes FV S L of G such as
L V − F then F and L are Disjoint-FV S.
Node set of graph G
FVS F k-FVS L
2.3.2 Algorithm
This algorithm is based on nodes swap. Let’s explain how it works. We find two sets of
nodes which are disjoint-FV S on G and we call them V 1 and V 2.
First of all, we remove all degree-0 and degree-1 nodes. It’s a kind of clean up in order to
compute FV S faster. Then, for all degree-2 v in V 1 nodes, we have to follow two rules.
B
A
C
Figure 2.13: Let B ∈ V 1 and (C, A) ∈ V 22
. B is a degree-2 and its neighbors A, C are into
V 2. So, we include B into V 1 − FV S, we remove B from G and we decrease k.
10. 2.3 Disjoint-FVS 8
B
A
C
Figure 2.14: Let (B, A) ∈ V 12
and C ∈ V 2. B is a degree-2 and only C is into V 2. So, we
move B from V 1 to V 2.
The complete algorithm is written below :
11. 2.4 2-Approximation 9
2.4 2-Approximation
2.4.1 Definitions and notations
Let the function w : V (G) → N denotes the weight of a node in the graph G.
A graph G is clean if it contains no vertex of degree less than two.
A cycle C is semidisjoint if ∀u ∈ C, d(u) = 2 with at most one exception.
2.4.2 Algorithm
An approximation algorithm gives an approximate solution to a complex problem. The qual-
ity of an approximation algorithm is given by the ratio r of the solution approached on the
optimal solution.
We will use a 2-approximation algorithm that applies only to weighted and undirected graphs.
This algorithm will be based on the principle of local searches. This principle focuses on a
subgraph H of G and attempts to resolve the problem on H. Thus, if we can make H acyclic
that means that we can remove cycles on G. This process is repeated until G has no more
cycles. The 2-approximation algorithm is bounded by Θ(min|E|log|V |, |V |2
).
12. 2.4 2-Approximation 10
2.4.3 Example
A
B
C
D
E F
Figure 2.15: A graph G whose nodes are weighted by 1
A
B
C
Figure 2.16: The CleanUp remove all degree-1 nodes. We move A, B, C from G to the Stack
and F. At step 1 et 2 (pop of C and B), F − u is a FV S of G. A the third step (pop of A),
F − u is not a FV S of G then F = A is a FV S of G.
B
C
D
E F
Figure 2.17: The graph G after 2-approximation
13. 2.5 Randomized Algorithm 11
2.5 Randomized Algorithm
2.5.1 Definitions et notations
A leaf is degree-1 node. A link point is a degree-2 node. A branch point is a node of degree
more than three. A graph is rich if it contains only branch point and no node with edges
on itself (self node)
2.5.2 Algorithm
This algorithm is based on the fact that in a rich graph, if we randomly take a node s, there
is a probability of at least 1/2 that s is a neighbor of a FV S node.
Indeed, let a graph G(V, E) and a graph F (FV S of G), then G − F is a forest. Let,
X = V − F. We have, |E(X)| < |X|. Every node of X is a branch point and 3|X| <
w∈X
deg(v) = |E(F, X)| + 2|E(X)|.
We use this property with the algorithm below :
We can notice that this algorithm achieves the FV S with the minimum of probability
(1/4j
). But we can, however, increase artificially the probability of this algorithm.
14. 2.5 Randomized Algorithm 12
We have a FV S with better probability than Single Guess due to the product of proba-
bilities.
Nevertheless, the worries of the Repeat Guess algorithm is that each call to Single Guess is
made independently. So we may have already calculating the Single Guess that one is trying
to achieve. It was a small but real probability that the calculation of FV S ends in a very
long time and that even with a very small FV S.
We will change this algorithm to improve this weakness.
A theorem on branchy graphs states that
w∈X
deg(v) 6x
w∈X
deg(v).
So we will limit our iterations by Max given to arbitrarily set the number of iteration and
c6w(F)
where w(F) is the weight of FV S lowest found by the algorithm.
15. 3. Conclusion
In this paper, I have presented some great algorithms for the FV S problem which are re-
ally tricky. All of those algorithms give a solution with a great time complexity for the
Feedback V ertex Set problem.
As we can see on this paper On Feedback Vertex Set New Measure and New Structures, this
problem is far from being closed and is still relevant for a good time.
Figure 3.1: History of parameterized algorithms for FV S problem.
Acknowledgment. Thank Binh Minh Bui Xuan (see 4) for the help provided through-
out this project.
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