SlideShare una empresa de Scribd logo
1 de 3
Descargar para leer sin conexión
MATH 350: Graph Theory and Combinatorics. Fall 2012.
Assignment #2: Bipartite graphs, matchings and connectivity.

1.

Show that a graph G is bipartite if and only if for every subgraph
H of G, there is a subset X ⊆ V (H) with |X| ≥ 1 |V (H)| so that no two
2
members of X are adjacent in H.
Solution: If G is bipartite and H is a subgraph of G then either A ∩ V (H)
or B ∩ V (H) satisfies the requirement. If G is not bipartite then it contains
an odd cycle H as a subgraph and the size of a maximum independent set
in H is 1 (|V (H)| − 1).
2
Let G be a loopless graph in which every vertex has degree ≥ 1.
Let X be the largest matching in G, and let Y be the smallest set of edges
of G so that every vertex of G is incident with ≥ 1 edge in Y . Show that
|X| + |Y | = |V (G)|.

2.

Solution: The solution of this problem was presented in class as Theorem
11.2.

3.

Let G be a bipartite graph with bipartition (A, B) in which every
vertex has degree ≥ 1. Assume that for every edge of G with ends a ∈ A
and b ∈ B we have deg(a) ≥ deg(b). Show that there exists a matching in
G covering A.
Solution: Suppose not. By Hall’s theorem there exists X ⊂ A with < |X|
neighbors in B. Choose such a set X with |X| minimum. Let Y ⊂ B
denote the set of vertices adjacent to any of the vertices in X. Then there
exists a matching M consisting of edges joining vertices of Y to vertices of
X of size |Y |. Indeed, otherwise, by Hall’s theorem, there exists Y ′ ⊂ Y
so that the set X ′ of vertices in X adjacent to any of the vertices of Y ′
satisfies |X ′ | < |Y ′ |. It follows that |X − X ′ | < |Y − Y ′ | and, as the vertices
of X − X ′ have no neighbors in Y ′ , we deduce that X − X ′ contradicts the
minimality of X.
Let F denote the set of edges joining X and Y . Then
∑
∑
|F | =
deg(a) >
deg(a) ≥
a∈A

=

∑

e=ab∈M
a∈A,b∈B

deg(b) ≥ |F |,

e=ab∈M
a∈A,b∈B

a contradiction. Thus G contains a matching covering A, as desired.
Given integers n ≥ m ≥ k ≥ 0, determine the maximum possible
number of edges in a simple bipartite graph G with bipartition (A, B),
with |A| = n, |B| = m and no matching of size k.

4.

Solution: If G has no matching of size k then by K¨nig’s theorem it
o
contains a set X with |X| ≤ k−1 so that every edge has an end in X. Every
vertex in X is incident with at most n edges. Therefore, |E(G)| ≤ (k −1)n.
One can have a graph with these many edges satisfying all the criteria by
having exactly k − 1 vertices of B with non-zero degree, each joined to all
the vertices of A.

5.

Let G be a connected graph in which every vertex has degree three.
Show that if G has no cut-edge then every two edges of G lie on a common
cycle.
Solution: Note that G is loopless, as otherwise it would contain a cutedge. Consider e1 , e2 ∈ E(G) and let xi , yi be the ends of ei for i = 1, 2. If
there exist two vertex-disjoint paths from x1 , y1 to x2 , y2 then these paths
together with e1 and e2 form the required cycle. Otherwise, by Menger’s
theorem, there exists a separation (A, B) of order 1 with x1 , y1 ∈ A, x2 , y2 ∈
B. Let {v} = A ∩ B. Let u1 , u2 , u3 be the other ends of edges incident
to v. (These three vertices are not necessarily distinct.) Without loss of
generality, u1 ∈ A, u2 , u3 ∈ B. Then the edge joining u1 and v is a cut-edge,
a contradiction.

6.
a) Distinct u, v ∈ V (G) are k-linked if there are k paths P1 , ..., Pk of G
from u to v so that E(Pi ∩ Pj ) = ∅ (1 ≤ i < j ≤ k). Suppose u, v, w
X

Z

X,Y

Y,Z

Figure 1: Counterexample for Problem 6b).

are distinct and u, v are k-linked, and so are v, w. Does it follow that
u, w are k-linked?
Solution: Yes. By Theorem 9.4 if u and w are not k-linked then there
exists X ⊆ V (G) with u in X, w ̸∈ X and |δ(X)| < k. By symmetry,
we may assume v ∈ X. Then the opposite direction of Theorem 9.4
implies that v and w are not k-linked.
b) Subsets X, Y ⊆ V (G) are k-joined if |X| = |Y | = k and there are k
paths P1 , ..., Pk of G from X to Y so that V (Pi ∩ Pj ) = ∅ (1 ≤ i < j ≤
k). Suppose X, Y, Z ⊆ V (G) and X, Y are k-joined, and so are Y, Z.
Does it follow that X, Z are k-joined?
Solution: No. See Figure 1 for an example with k = 2.

Más contenido relacionado

La actualidad más candente

A study on connectivity in graph theory june 18 pdf
A study on connectivity in graph theory  june 18 pdfA study on connectivity in graph theory  june 18 pdf
A study on connectivity in graph theory june 18 pdf
aswathymaths
 
A study on connectivity in graph theory june 18 123e
A study on connectivity in graph theory  june 18 123eA study on connectivity in graph theory  june 18 123e
A study on connectivity in graph theory june 18 123e
aswathymaths
 
On algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetryOn algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetry
graphhoc
 
Discrete-Chapter 11 Graphs Part III
Discrete-Chapter 11 Graphs Part IIIDiscrete-Chapter 11 Graphs Part III
Discrete-Chapter 11 Graphs Part III
Wongyos Keardsri
 
Discrete-Chapter 11 Graphs Part I
Discrete-Chapter 11 Graphs Part IDiscrete-Chapter 11 Graphs Part I
Discrete-Chapter 11 Graphs Part I
Wongyos Keardsri
 
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
ijceronline
 
Discrete-Chapter 11 Graphs Part II
Discrete-Chapter 11 Graphs Part IIDiscrete-Chapter 11 Graphs Part II
Discrete-Chapter 11 Graphs Part II
Wongyos Keardsri
 

La actualidad más candente (20)

Working Smarter By Using Technology
Working Smarter By Using TechnologyWorking Smarter By Using Technology
Working Smarter By Using Technology
 
A study on connectivity in graph theory june 18 pdf
A study on connectivity in graph theory  june 18 pdfA study on connectivity in graph theory  june 18 pdf
A study on connectivity in graph theory june 18 pdf
 
Lingerie
LingerieLingerie
Lingerie
 
Basics on Graph Theory
Basics on Graph TheoryBasics on Graph Theory
Basics on Graph Theory
 
Connectivity of graph
Connectivity of graphConnectivity of graph
Connectivity of graph
 
A study on connectivity in graph theory june 18 123e
A study on connectivity in graph theory  june 18 123eA study on connectivity in graph theory  june 18 123e
A study on connectivity in graph theory june 18 123e
 
1452 86301000013 m
1452 86301000013 m1452 86301000013 m
1452 86301000013 m
 
International journal of applied sciences and innovation vol 2015 - no 2 - ...
International journal of applied sciences and innovation   vol 2015 - no 2 - ...International journal of applied sciences and innovation   vol 2015 - no 2 - ...
International journal of applied sciences and innovation vol 2015 - no 2 - ...
 
On algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetryOn algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetry
 
Graph theory presentation
Graph theory presentationGraph theory presentation
Graph theory presentation
 
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRYON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
 
Discrete-Chapter 11 Graphs Part III
Discrete-Chapter 11 Graphs Part IIIDiscrete-Chapter 11 Graphs Part III
Discrete-Chapter 11 Graphs Part III
 
Discrete-Chapter 11 Graphs Part I
Discrete-Chapter 11 Graphs Part IDiscrete-Chapter 11 Graphs Part I
Discrete-Chapter 11 Graphs Part I
 
A common fixed point theorem for six mappings in g banach space with weak-com...
A common fixed point theorem for six mappings in g banach space with weak-com...A common fixed point theorem for six mappings in g banach space with weak-com...
A common fixed point theorem for six mappings in g banach space with weak-com...
 
Graph theory concepts complex networks presents-rouhollah nabati
Graph theory concepts   complex networks presents-rouhollah nabatiGraph theory concepts   complex networks presents-rouhollah nabati
Graph theory concepts complex networks presents-rouhollah nabati
 
Am03102420247
Am03102420247Am03102420247
Am03102420247
 
Graph theory and its applications
Graph theory and its applicationsGraph theory and its applications
Graph theory and its applications
 
thesis
thesisthesis
thesis
 
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
 
Discrete-Chapter 11 Graphs Part II
Discrete-Chapter 11 Graphs Part IIDiscrete-Chapter 11 Graphs Part II
Discrete-Chapter 11 Graphs Part II
 

Destacado (8)

Summer+training
Summer+trainingSummer+training
Summer+training
 
Summer+training 2
Summer+training 2Summer+training 2
Summer+training 2
 
Mcs student
Mcs studentMcs student
Mcs student
 
Scribed lec8
Scribed lec8Scribed lec8
Scribed lec8
 
Networks 2
Networks 2Networks 2
Networks 2
 
Scholarship sc st
Scholarship sc stScholarship sc st
Scholarship sc st
 
Slides15
Slides15Slides15
Slides15
 
Mithfh lecturenotes 9
Mithfh lecturenotes 9Mithfh lecturenotes 9
Mithfh lecturenotes 9
 

Similar a Math350 hw2solutions

1. Graph and Graph Terminologiesimp.pptx
1. Graph and Graph Terminologiesimp.pptx1. Graph and Graph Terminologiesimp.pptx
1. Graph and Graph Terminologiesimp.pptx
swapnilbs2728
 
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docxLecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
croysierkathey
 
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docxLecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
jeremylockett77
 
Simple algorithm & hopcroft karp for bipartite graph
Simple algorithm & hopcroft karp for bipartite graphSimple algorithm & hopcroft karp for bipartite graph
Simple algorithm & hopcroft karp for bipartite graph
Miguel Pereira
 
optimal graph realization
optimal graph realizationoptimal graph realization
optimal graph realization
Igor Mandric
 

Similar a Math350 hw2solutions (20)

Matching
MatchingMatching
Matching
 
Rational points on elliptic curves
Rational points on elliptic curvesRational points on elliptic curves
Rational points on elliptic curves
 
Text book pdf
Text book pdfText book pdf
Text book pdf
 
Graphs.pdf
Graphs.pdfGraphs.pdf
Graphs.pdf
 
1. Graph and Graph Terminologiesimp.pptx
1. Graph and Graph Terminologiesimp.pptx1. Graph and Graph Terminologiesimp.pptx
1. Graph and Graph Terminologiesimp.pptx
 
Degree Equitable Connected cototal dominating graph
Degree Equitable Connected cototal dominating graphDegree Equitable Connected cototal dominating graph
Degree Equitable Connected cototal dominating graph
 
Graphs - Discrete Math
Graphs - Discrete MathGraphs - Discrete Math
Graphs - Discrete Math
 
Alg grp
Alg grpAlg grp
Alg grp
 
Stochastic Processes Homework Help
Stochastic Processes Homework Help Stochastic Processes Homework Help
Stochastic Processes Homework Help
 
DIGITAL TEXT BOOK
DIGITAL TEXT BOOKDIGITAL TEXT BOOK
DIGITAL TEXT BOOK
 
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docxLecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
 
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docxLecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
Lecture13p.pdf.pdfThedeepness of freedom are threevalues.docx
 
Graph theory
Graph theoryGraph theory
Graph theory
 
graph_theory_ch_3_20201124.ppt
graph_theory_ch_3_20201124.pptgraph_theory_ch_3_20201124.ppt
graph_theory_ch_3_20201124.ppt
 
The Graph Minor Theorem: a walk on the wild side of graphs
The Graph Minor Theorem: a walk on the wild side of graphsThe Graph Minor Theorem: a walk on the wild side of graphs
The Graph Minor Theorem: a walk on the wild side of graphs
 
Simple algorithm & hopcroft karp for bipartite graph
Simple algorithm & hopcroft karp for bipartite graphSimple algorithm & hopcroft karp for bipartite graph
Simple algorithm & hopcroft karp for bipartite graph
 
optimal graph realization
optimal graph realizationoptimal graph realization
optimal graph realization
 
Total Dominating Color Transversal Number of Graphs And Graph Operations
Total Dominating Color Transversal Number of Graphs And Graph OperationsTotal Dominating Color Transversal Number of Graphs And Graph Operations
Total Dominating Color Transversal Number of Graphs And Graph Operations
 
Chapter 1
Chapter   1Chapter   1
Chapter 1
 
Wythoff construction and l1-embedding
Wythoff construction and l1-embeddingWythoff construction and l1-embedding
Wythoff construction and l1-embedding
 

Más de Praveen Kumar

Más de Praveen Kumar (19)

Summer2014 internship
Summer2014 internshipSummer2014 internship
Summer2014 internship
 
Solutions1.1
Solutions1.1Solutions1.1
Solutions1.1
 
Line circle draw
Line circle drawLine circle draw
Line circle draw
 
Lecture3
Lecture3Lecture3
Lecture3
 
Lec2 state space
Lec2 state spaceLec2 state space
Lec2 state space
 
Graphtheory
GraphtheoryGraphtheory
Graphtheory
 
Graphics display-devicesmod-1
Graphics display-devicesmod-1Graphics display-devicesmod-1
Graphics display-devicesmod-1
 
Games.4
Games.4Games.4
Games.4
 
Dda line-algorithm
Dda line-algorithmDda line-algorithm
Dda line-algorithm
 
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
 
Cse3461.c.signal encoding.09 04-2012
Cse3461.c.signal encoding.09 04-2012Cse3461.c.signal encoding.09 04-2012
Cse3461.c.signal encoding.09 04-2012
 
Comerv3 1-12(2)
Comerv3 1-12(2)Comerv3 1-12(2)
Comerv3 1-12(2)
 
Cimplementation
CimplementationCimplementation
Cimplementation
 
Artificial intelligence lecturenotes.v.1.0.4
Artificial intelligence lecturenotes.v.1.0.4Artificial intelligence lecturenotes.v.1.0.4
Artificial intelligence lecturenotes.v.1.0.4
 
Ai ch2
Ai ch2Ai ch2
Ai ch2
 
Aipapercpt
AipapercptAipapercpt
Aipapercpt
 
01127694
0112769401127694
01127694
 
427lects
427lects427lects
427lects
 
10 linescan
10 linescan10 linescan
10 linescan
 

Último

CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
giselly40
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
Earley Information Science
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
 

Último (20)

A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 

Math350 hw2solutions

  • 1. MATH 350: Graph Theory and Combinatorics. Fall 2012. Assignment #2: Bipartite graphs, matchings and connectivity. 1. Show that a graph G is bipartite if and only if for every subgraph H of G, there is a subset X ⊆ V (H) with |X| ≥ 1 |V (H)| so that no two 2 members of X are adjacent in H. Solution: If G is bipartite and H is a subgraph of G then either A ∩ V (H) or B ∩ V (H) satisfies the requirement. If G is not bipartite then it contains an odd cycle H as a subgraph and the size of a maximum independent set in H is 1 (|V (H)| − 1). 2 Let G be a loopless graph in which every vertex has degree ≥ 1. Let X be the largest matching in G, and let Y be the smallest set of edges of G so that every vertex of G is incident with ≥ 1 edge in Y . Show that |X| + |Y | = |V (G)|. 2. Solution: The solution of this problem was presented in class as Theorem 11.2. 3. Let G be a bipartite graph with bipartition (A, B) in which every vertex has degree ≥ 1. Assume that for every edge of G with ends a ∈ A and b ∈ B we have deg(a) ≥ deg(b). Show that there exists a matching in G covering A. Solution: Suppose not. By Hall’s theorem there exists X ⊂ A with < |X| neighbors in B. Choose such a set X with |X| minimum. Let Y ⊂ B denote the set of vertices adjacent to any of the vertices in X. Then there exists a matching M consisting of edges joining vertices of Y to vertices of X of size |Y |. Indeed, otherwise, by Hall’s theorem, there exists Y ′ ⊂ Y so that the set X ′ of vertices in X adjacent to any of the vertices of Y ′ satisfies |X ′ | < |Y ′ |. It follows that |X − X ′ | < |Y − Y ′ | and, as the vertices of X − X ′ have no neighbors in Y ′ , we deduce that X − X ′ contradicts the minimality of X.
  • 2. Let F denote the set of edges joining X and Y . Then ∑ ∑ |F | = deg(a) > deg(a) ≥ a∈A = ∑ e=ab∈M a∈A,b∈B deg(b) ≥ |F |, e=ab∈M a∈A,b∈B a contradiction. Thus G contains a matching covering A, as desired. Given integers n ≥ m ≥ k ≥ 0, determine the maximum possible number of edges in a simple bipartite graph G with bipartition (A, B), with |A| = n, |B| = m and no matching of size k. 4. Solution: If G has no matching of size k then by K¨nig’s theorem it o contains a set X with |X| ≤ k−1 so that every edge has an end in X. Every vertex in X is incident with at most n edges. Therefore, |E(G)| ≤ (k −1)n. One can have a graph with these many edges satisfying all the criteria by having exactly k − 1 vertices of B with non-zero degree, each joined to all the vertices of A. 5. Let G be a connected graph in which every vertex has degree three. Show that if G has no cut-edge then every two edges of G lie on a common cycle. Solution: Note that G is loopless, as otherwise it would contain a cutedge. Consider e1 , e2 ∈ E(G) and let xi , yi be the ends of ei for i = 1, 2. If there exist two vertex-disjoint paths from x1 , y1 to x2 , y2 then these paths together with e1 and e2 form the required cycle. Otherwise, by Menger’s theorem, there exists a separation (A, B) of order 1 with x1 , y1 ∈ A, x2 , y2 ∈ B. Let {v} = A ∩ B. Let u1 , u2 , u3 be the other ends of edges incident to v. (These three vertices are not necessarily distinct.) Without loss of generality, u1 ∈ A, u2 , u3 ∈ B. Then the edge joining u1 and v is a cut-edge, a contradiction. 6. a) Distinct u, v ∈ V (G) are k-linked if there are k paths P1 , ..., Pk of G from u to v so that E(Pi ∩ Pj ) = ∅ (1 ≤ i < j ≤ k). Suppose u, v, w
  • 3. X Z X,Y Y,Z Figure 1: Counterexample for Problem 6b). are distinct and u, v are k-linked, and so are v, w. Does it follow that u, w are k-linked? Solution: Yes. By Theorem 9.4 if u and w are not k-linked then there exists X ⊆ V (G) with u in X, w ̸∈ X and |δ(X)| < k. By symmetry, we may assume v ∈ X. Then the opposite direction of Theorem 9.4 implies that v and w are not k-linked. b) Subsets X, Y ⊆ V (G) are k-joined if |X| = |Y | = k and there are k paths P1 , ..., Pk of G from X to Y so that V (Pi ∩ Pj ) = ∅ (1 ≤ i < j ≤ k). Suppose X, Y, Z ⊆ V (G) and X, Y are k-joined, and so are Y, Z. Does it follow that X, Z are k-joined? Solution: No. See Figure 1 for an example with k = 2.