## HAMILTONIAN CIRCUITS

DISTINGUISHED PROFESSOR OF
MATHEMATICS & NATURAL SCIENCES

ENDOWED CHAIR

INSTITUTE OF MATHEMATICS
H-501 PALAM VIHAR
DISTRICT  GURGAON
HARYANA  1 2 2 0 1 7
INDIA We present a new polynomial-time algorithm for finding Hamiltonian circuits in graphs. It is shown that the algorithm always finds a Hamiltonian circuit in graphs that have at least three vertices and minimum degree at least half the total number of vertices. In the process, we also obtain a constructive proof of Dirac's famous theorem of 1952, for the first time. The algorithm finds a Hamiltonian circuit (respectively, tour) in all known examples of graphs that have a Hamiltonian circuit (respectively, tour). In view of the importance of the P versus NP question, we ask: does there exist a graph that has a Hamiltonian circuit (respectively, tour) but for which this algorithm cannot find a Hamiltonian circuit (respectively, tour)? The algorithm is implemented in C++ and the program is demonstrated with several examples [Download]. Google Scholar Citations © 2004
 Thanks to J.R. Manes for presenting a seminar about this algorithm at the University of Alaska, Fairbanks, in December 2004. Thanks to Guenter Stertenbrink for spending many hours testing the program and providing Examples 3.3 in February 2005. Thanks to Roberto Tauraso and his students at the University of Rome for using this algorithm to find many large square loops and providing Examples 3.4 in November 2005. This algorithm has also been cited in Applications of Graph Theory published by the Korean Society for Industrial and Applied Mathematics. We are pleased to announce that The Hamiltonian Circuit Algorithm has been published by Amazon in 2011. The Endowed Chair of the Institute of Mathematics was bestowed upon Distinguished Professor Ashay Dharwadker in 2012 to honour his fundamental contributions to Mathematics and Natural Sciences.

1.     Introduction
In 1856 , Hamilton described a certain mathematical game called the Icosian played on the surface of a dodecahedron. Starting from a given vertex, the objective was to find a path of consecutive vertices along the edges, visiting every vertex exactly once and returning to the original vertex to complete a circuit. The general problem of trying to find such Hamiltonian Circuits in arbitrary graphs turned out to be very difficult to solve.
In 1952 , Dirac proposed a condition that guarantees the existence of a Hamiltonian circuit in a simple graph G with n ≥ 3 vertices: a lower bound on the minimum degree δ ≥ n/2 suffices. This is the best possible lower bound because the graph consisting of cliques of orders (n+1)/2 and (n+1)/2 sharing a common vertex has minimum degree δ = (n-1)/2 but has no Hamiltonian circuit. However, Dirac's original proof is a proof by contradiction that does not show how one may actually construct the stipulated Hamiltonian circuit.
In 1972 , Karp showed that the problem of finding Hamiltonian circuits (respectively, tours) in graphs is NP-complete. Thus, the existence or non-existence of a polynomial-time algorithm for deciding whether a Hamiltonian circuit (respectively, tour) exists in any given graph would resolve one of the most important open problems in mathematics and computer science, the P versus NP question .
This website presents A NEW ALGORITHM FOR FINDING HAMILTONIAN CIRCUITS in graphs. In Section 2 we provide elementary DEFINITIONS on graphs and algorithms. In Section 3 we present a formal description of the new ALGORITHM for finding Hamiltonian circuits. In Section 4 we show that the algorithm has polynomial-time COMPLEXITY. In Section 5 we present a condition of  SUFFICIENCY for the algorithm to find a Hamiltonian circuit, using a lemma based on the pigeonhole principle. In the process we obtain a constructive proof of Dirac's Theorem showing, for the first time, how to build a Hamiltonian circuit in such graphs explicitly. The algorithm finds a Hamiltonian circuit (respectively, tour) in all known examples of graphs that have a Hamiltonian circuit (respectively, tour). In view of the importance of the P versus NP question, we ask: does there exist a graph that has a Hamiltonian circuit (respectively, tour) but for which this algorithm cannot find a Hamiltonian circuit (respectively, tour)? In Section 6 we provide an IMPLEMENTATION of the algorithm as a C++ program, together with demonstration software for Microsoft ™ Windows. In Section 7 we demonstrate the program for the graphs of the five PLATONIC SOLIDS, including Hamilton's original puzzle. In Section 8 we demonstrate the program for DIRAC GRAPHS which are known to be Hamiltonian. Finally, in Section 9 we further demonstrate the program by finding re-entrant  KNIGHT'S TOURS on chessboards of various dimensions. Section 10 lists the REFERENCES.

 To begin with, we present basic definitions about graphs and algorithms following . We use the usual notation ⌊x⌋ to denote the floor function i.e. the greatest integer not greater than x and ⌈x⌉ to denote the ceiling function i.e. the least integer not less than x.      A simple graph G with n vertices consists of a set of vertices V, with |V| = n, and a set of edges E, such that each edge is an unordered pair of distinct vertices. Note that the definition of G explicitly forbids loops (edges joining a vertex to itself) and multiple edges (many edges joining a pair of vertices), whence the set E must also be finite. We may label the vertices of G with the integers 1, 2, ..., n. If the unordered pair of vertices {u, v} is an edge in G, we say that u is a neighbor of v and write uv∈E. Neighborhood is clearly a symmetric relationship: uv∈E if and only if vu∈E. The degree of a vertex v, denoted by d(v), is the number of neighbors of v. The minimum degree over all vertices of G is denoted by δ. The adjacency matrix of G is an n×n matrix with the entry in row u and column v equal to 1 if uv∈E and equal to 0 otherwise. A path P in G is a sequence of distinct vertices v1, v2, ..., vk such that vivi+1∈E for i = 1, 2, ..., k-1. Given a path P, its sequence of distinct vertices v1, v2, ..., vk are said to have been visited and any vertex w outside P is said to be unvisited. Given a path P and a vertex v, the number of unvisited neighbors of v is denoted by η(v). A path P in G visiting vertices v1, v2, ..., vk is called a Hamiltonian tour if k = n. Thus, a Hamiltonian tour in a simple graph is a path that visits every vertex exactly once. A path P in G visiting vertices v1, v2, ..., vn is called a Hamiltonian circuit if it is a Hamiltonian tour and v1vn∈E. Thus, a Hamiltonian circuit in a simple graph is a path that visits every vertex exactly once and then allows us to return to the beginning of the path via an edge. If the simple graph G has a Hamiltonian circuit, G is said to be a Hamiltonian graph.      An algorithm is a problem-solving method suitable for implementation as a computer program. While designing algorithms we are typically faced with a number of different approaches. For small problems, it hardly matters which approach we use, as long as it is one that solves the problem correctly. However, there are many problems for which the only known algorithms take so long to compute the solution that they are practically useless. A polynomial-time algorithm is one whose number of computational steps is always bounded by a polynomial function of the size of the input. Thus, a polynomial-time algorithm is one that is actually useful in practice. The class of all such problems that have polynomial-time algorithms is denoted by P. For some problems, there are no known polynomial-time algorithms but these problems do have nondeterministic polynomial-time algorithms: try all candidates for solutions simultaneously and for each given candidate, verify whether it is a correct solution in polynomial-time. The class of all such problems is denoted by NP. Clearly P⊆NP. On the other hand,  there are problems that are known to be in NP and are such that any polynomial-time algorithm for them can be transformed (in polynomial-time) into a polynomial-time algorithm for every problem in NP. Such problems are called NP-complete. The problem of finding a Hamiltonian circuit (respectively, tour) is known to be NP-complete . Thus, if we are able to show the existence of a polynomial-time algorithm that finds a Hamiltonian circuit (respectively, tour) in every graph that has a Hamiltonian circuit (respectively, tour), we could prove that P = NP.  The present algorithm is, so far as we know, a promising candidate for the task. One of the greatest unresolved problems in mathematics and computer science today is whether P = NP or P ≠ NP .

3.     Algorithm
We are now ready to present a formal description of the algorithm. This is followed by a small example illustrating the steps of the algorithm.

3.1. Algorithm. Given as input a simple graph G with n vertices. Label the vertices 1, 2, ..., n in descending order of degrees d(1) ≥ d(2) ≥  ... ≥ d(n). For each initial vertex u = 1, 2, ..., n in turn, perform Parts I, II and III as follows:

• Part I:
• Initialization: Select the vertex v1 = u and let the path of visited vertices be v1.
• Iteration: Let the last selected vertex be vr and the path of visited vertices be v1, ..., vr. For each unvisited neighbor w of vr, compute η(w), the number of unvisited neighbors of w. Select vr+1 = w such that η(w) is a minimum (if there are many possible choices, select w with the smallest label). Extend the path of visited vertices to v1, ..., vr, vr+1.
• Termination: Iterate until the last selected vertex has no unvisited neighbors.
• Result: A path P(0) visiting vertices u = v1, ..., vk(0) such that vk(0) has no unvisited neighbors.
• Part II: Using the result of Part I,
• (a) If k(0) < n:
• If there is a vertex vi in P(0) such that vi is a neighbor of vk(0) and vi+1 has a neighbor w outside P(0)
• Initialization: For each vi in P(0) such that vi is a neighbor of vk(0) and vi+1 has a neighbor w outside P(0), and for each such neighbor w, compute η(w), the number of unvisited neighbors of w. Choose vi and w0 = w such that η(w) is a maximum (if there are many possible choices, choose the one where w has the smallest label). Reorder the path of visited vertices to be  u = v1, ..., vi-1, vi, vk(0), vk(0)-1, ..., vi+1 and rename the visited vertices u = v1, ..., vk(0) in this order. Select the vertex vk(0)+1 = w0 and let the path of visited vertices be v1, ..., vk(0)+1. Now perform iterations exactly as in Part I to extend the path of visited vertices to u = v1, ..., vk(1) such that vk(1) has no unvisited neighbors. Call this path P(1).
• Iteration: Let the last computed path be P(s) with visited vertices u = v1, ..., vk(s). For each vi in P(s) such that vi is a neighbor of vk(s) and vi+1 has a neighbor w outside P(s), and for each such neighbor w, compute η(w), the number of unvisited neighbors of w. Choose vi and ws = w such that η(w) is a maximum (if there are many possible choices, choose the one where w has the largest label). Reorder the path of visited vertices to be u = v1, ..., vi-1, vi, vk(s), vk(s)-1, ..., vi+1 and rename the visited vertices u = v1, ..., vk(s) in this order. Select the vertex vk(s)+1 = ws and let the path of visited vertices be v1, ..., vk(s)+1. Now perform iterations exactly as in Part I to extend the path of visited vertices to u = v1, ..., vk(s+1) such that vk(s+1) has no unvisited neighbors. Call this path P(s+1).
• Termination: Iterate until the last selected path P(s) with visited vertices u = v1, ..., vk(s) has no vertex vi such that vi is a neighbor of vk(s) and vi+1 has a neighbor ws outside P (s).
• Result: A path P(s) with visited vertices u = v1, ..., vk(s) such that:
• (i) P(s) has no vertex vi such that vi is a neighbor of vk(s) and vi+1 has a neighbor ws outside P(s).
• (ii) The vertex vk(s) has no unvisited neighbors.
• (b) If k(s) < n:
• Try to further extend the path P(s) by finding viin the subpath v1, ..., vk(s)-2 such that vi has a neighbor w1 outside P(s). Now try to find a path w1, w2, ...,wm amongst the unvisited vertices by a procedure similar to Part I. Trim the path from the right, if necessary, so that wm has a neighbor vj such that i+1 < j < k(s) and vi+1 is a neighbor of vj+1. If successful, we obtain the extended path v1, ..., vi, w1, w2, ...,wm, vj, vj-1, ..., vi+1, vj+1, vj+2, ..., vk(s). Repeat this procedure as long as we obtain an extended path. Reassign s and the extended path P(s) with visited vertices u = v1, ..., vk(s).
• (c) If k(s) < n:
• Repeat the above procedure (b) for the reversed path P(s) with visited vertices vk(s), vk(s)-1, ..., v1.
• Part III: Using the result of Part II,
• If k(s) < n:
• Output: Found path v1, ..., vk(s)
• If k(s) = n:
• (i) Output: Found Hamiltonian tour v1, ..., vn.
• (ii) Define X = {vi | v1vi+1E} and Y = {vi | vivnE}. If XY  ≠ ∅, then for each viXY
• Output: Found Hamiltonian circuit v1, ..., vi-1, vi, vn, vn-1, ..., vi+1.
• Exceptional Cases:
• (a) If the graph G has more than two vertices of degree 1, there can be no Hamiltonian tour. If the graph G has exactly two vertices of degree 1, then G cannot have a Hamiltonian circuit but could have a Hamiltonian tour. Suppose the graph G has exactly two vertices of degree 1, a and b. Find a path a = a1, a2, ..., ar using the procedure of Part I such that a2, ..., ar are vertices of degree 2. Similarly, find a path b = b1, b2, ..., bs such that b2, ..., bs are vertices of degree 2. Now let Ga be the graph obtained by deleting the vertices a1, a2, ..., ar-1, b1, b2, ..., bs from G and let Gb be the graph obtained by deleting the vertices a1, a2, ..., ar, b1, b2, ..., bs-1 from G. Use Parts I and II to find paths in the graphs Ga and Gb. If a path in Ga can be connected to a path in Gb to form a path in G, use Parts I and II to try and extend such a path to a Hamiltonian tour in G.
• (b) Suppose the algorithm finds a Hamiltonian tour in G but Part III could not find a Hamiltonian circuit. For each edge ab in G, let Gab denote the graph obtained by deleting the edge ab in G and adding two new vertices a' and b' and two new edges aa' and bb'. Use the algorithm to try and find a Hamiltonian tour a', a, ..., b, b' in Gab. Then a, ..., b would be a Hamiltonian circuit in G.
3.2. Example. We demonstrate some of the steps of the algorithm with a small example. Consider the labeled graph with n = 8 vertices shown below in Figure 3.1. We go through the steps for initial vertex u = 1. First perform Part I. To initialize (Iteration 1), we select v1= 1 and let the path of visited vertices be v1 = 1. The unvisited neighbors of v1 = 1 are 2, 5 and 6. The vertex 2 has two unvisited neighbors 3 and 8, so η(2) = 2; the vertex 5 has two unvisited neighbors 3 and 4, so η(5) = 2; the vertex 6 has two unvisited neighbors 7 and 8, so η(6) = 2. Here vertex 2 has the smallest label such that η(2) = 2 is a minimum. At Iteration 2, select v2 = 2 and let the path of visited vertices be v1 = 1, v2 = 2. The unvisited neighbors of v2 = 2 are 3 and 8. The vertex 3 has two unvisited neighbors 4 and 5, so η(3) = 2; the vertex 8 has two unvisited neighbors 6 and 7, so η(8) = 2. Here vertex 3 has the smallest label such that η(3) = 2 is a minimum. At Iteration 3, select v3 = 3 and let the path of visited vertices be v1 = 1, v2 = 2, v3 = 3. The unvisited neighbors of v3 = 3 are 4 and 5. The vertex 4 has only one unvisited neighbor 5, so η(4) = 1; the vertex 5 also has only one unvisited neighbor 4, so η(5) = 1. Here vertex 4 has the smallest label such that η(4) = 1 is a minimum. At Iteration 4, select v4 = 4 and let the path of visited vertices be v1 = 1, v2 = 2, v3 = 3, v4 = 4. The only unvisited neighbor of v4 = 4 is 5. The vertex 5 has no unvisited neighbors, so η(5) = 0. We select v5 = 5, and let the path of visited vertices be v1 = 1, v2 = 2, v3 = 3, v4 = 4, v5 = 5. Part I terminates with the resulting path P(0) visiting vertices v1 = 1, v2 = 2, v3 = 3, v4 = 4, v5 = 5 and cardinality k(0) = 5.
Now perform Part II. We have k(0) = 5 < 8 = n. The neighbors of v5 = 5 are v1 = 1, v3 = 3 and v4 = 4, all in P(0) by construction. Consider the respective successor vertices v1+1 = v2 = 2, v3+1 = v4 = 4 and v4+1 = v5 = 5. Now v2 = 2 has only one neighbor 8 outside P(0). The vertex 8 has two unvisited neighbors 6 and 7, so η(8) = 2. The vertices v4 = 4 and v5 = 5 have no neighbors outside P(0) to consider. Here v1 = 1 (trivially, with no comparisons to be made) has the largest label such that its successor v1+1 = v2 = 2 has a neighbor 8 outside P(0) with maximum η(8) = 2. We first reorder the path of visited vertices v1 = 1, v5 = 5, v4 = 4, v3 = 3, v2 = 2. We then rename the path of visited vertices in this order v1 = 1, v2 = 5, v3 = 4, v4 = 3, v5 = 2. Now select the vertex v6 = 8 found above and let the path of visited vertices be v1 = 1, v2 = 5, v3 = 4, v4 = 3, v5 = 2, v6 = 8. Perform iterations exactly as in Part I from here: the unvisited neighbors of v6 = 8 are 6 and 7. The vertex 6 has only one unvisited neighbor 7, so η(6) = 1; the vertex 7 also has only one unvisited neighbor 6, so η(7) = 1. Here vertex 6 has the smallest label such that η(6) = 1 is a minimum. At the next iteration, select v7 = 6 and let the path of visited vertices be v1 = 1, v2 = 5, v3 = 4, v4 = 3, v5 = 2, v6 = 8, v7 = 6. The only unvisited neighbor of v7 = 6 is 7. The vertex 7 has no unvisited neighbors, so η(7) = 0. We select v8 = 7 and obtain the path P(1) with visited vertices v1 = 1, v2 = 5, v3 = 4, v4 = 3, v5 = 2, v6 = 8, v7 = 6, v8 = 7. Since there are no vertices left outside P(1), Part II terminates with the resulting path P(1) visiting vertices v1 = 1, v2 = 5, v3 = 4, v4 = 3, v5 = 2, v6 = 8, v7 = 6, v8 = 7 and cardinality k(1) = 8.
Finally, perform Part III. Since k(1) = 8 = n, the algorithm has found a Hamiltonian tour. Define X = {vi | v1vi+1E}  = {v1, v4, v6} = {1, 3, 8} and Y = {vi | vivnE} = {v6, v7} = {8, 6}. Then XY = {v6} = {8}, so the algorithm has found a Hamiltonian circuit v1 = 1, v2 = 5, v3 = 4, v4 = 3, v5 = 2, v6 = 8, v8 = 7, v7 = 6.
Similarly, we can go through Parts I, II and III starting with initial vertices u = 2, 3, 4, 5, 6, 7 and 8 respectively.

3.3. Example. Provided by Guenter Stertenbrink, February 2005 [Download]. This example shows that it is necessary to order the vertices by descending degrees and Parts II(b) and II(c) are also used. 3.4. Example. Provided by Roberto Tauraso, November 2005 [Download]. This example shows how the algorithm works in the exceptional cases (a) and (b). A square loop of size n is a circular arrangement of the integers 1, 2, ..., n such that the sum of any two adjacent integers is a perfect square. Define a graph Gn with vertices 1, 2, ..., n such that there is an edge between vertex i and vertex j if and only if i+j is a perfect square. Then a Hamiltonian circuit in Gn is exactly a square loop of size n. The graph of figure 3.3 shows a Hamiltonian circuit in G32 and a square loop of size 32. Roberto Tauraso and his students at the University of Rome computed many large square loops using this algorithm and conjecture that square loops exist for all n ≥ 32.

 We shall now show that the above algorithm terminates in polynomial-time, by specifying a polynomial of the number of vertices n of the input graph, that is an upper bound on the total number of computational steps performed by the algorithm. Note that we consider  checking whether a given pair of vertices is connected by an edge in G, and comparing whether a given integer is less than another given integer to be elementary computational steps. 4.1. Proposition. Given as input a simple graph G with n vertices, the algorithm takes less than 4n5+8n4+3n3+2n2+n steps to terminate. Proof. Given an initial vertex u, first consider Part I of the algorithm. At iteration r, it takes less than n steps to determine the unvisited neighbors v(r)1, ..., v(r)m of  vr. Then for each v(r)i it takes less than n steps to determine the unvisited neighbors of v(r)i and hence find η(v(r)i). Thereafter it takes less than n steps to find the minimum of the integers η(v(r)1), ..., η(v(r)m). Thus we count a total of less than n2+n steps at iteration r. There are at most n iterations, so a total of less than n(n2+n) = n3+n2 steps terminates Part I to find path P(0).      Next consider Part II. If k(0) < n, it takes less than n steps to determine the neighbors of vk(0). Thereafter, it takes less than n steps to try and find a neighbor w outside P(0) for each successor of a neighbor of vk(0). For each such neighbor w, it takes less than n steps to count its η(w) unvisited neighbors. So it takes less than n3 steps to find all the η(w) and then it takes less than n steps to find w for which η(w) is a maximum. Thus we count less than n3+n steps so far in Part II (a). Now reordering and renaming the vertices takes less than n steps, and then the iterations to find path P(1) take less than n3+n2 steps, exactly as in Part I. Thus we count less than n3+n+n+n3+n2 = 2n3+n2+2n steps so far in Part II (a). Since Part II performs at most n iterations, a total of less than n(2n3+n2+2n) = 2n4+n3+2n2 steps terminates Part II (a) to find path P(s). If Part II (b) is required, it takes less than n2 steps to find vi and w1. Thereafter, it takes less than n3+n2 steps as in Part I to find the path w1, w2, ..., wm and less than n2 steps to find vj. Since there can be at most n iterations, Part II(b) takes less than n(n2+n3+n2+n2) =  n4+3n3 steps. Similarly, Part II (c) takes less than n4+3n3 steps. Thus Part II takes less than 2n4+n3+2n2+n4+3n3+n4+3n3 = 4n4+7n3+2n2 steps to terminate.      Finally consider Part III. Here (i) is just one step and (ii) may be accomplished in less than 2n steps, thus a total of less than 2n+1 steps terminates Part III.      Hence, the algorithm starting at any initial vertex u, takes a total of less than n3+n2+4n4+7n3+2n2+2n+1 = 4n4+8n3+3n2+2n+1 steps to complete Parts I, II and III. Since there are n choices for the initial vertex, the algorithm must finally terminate after executing a grand total of less than n(4n4+8n3+3n2+2n+1) = 4n5+8n4+3n3+2n2+n steps. ☐  4.2. Remark. A simple graph G with n vertices can have at most n(n-1)/2 edges. In the exceptional case (b), the algorithm will run once for each edge, so the running time of the program will increase at most by a factor of O(n2).

 The algorithm may be applied to any simple graph and will always terminate in polynomial-time. The theorem below establishes a sufficient condition on the input graph which guarantees that the algorithm will find a Hamiltonian circuit. As a corollary we obtain a constructive proof of Dirac's theorem . For the proof of the theorem, we shall need the following lemma that is a direct consequence of the 5.1. Pigeonhole Principle. If l letters are distributed into p pigeonholes and l > p ≥ 1, then some pigeonhole must receive at least two letters. 5.2. Lemma. Let G be a simple graph with n ≥ 3 vertices and δ ≥ n/2. If X is a subset with ⌈n/2⌉ vertices and v is a vertex outside X then v must have a neighbor in X. Proof. If n = 3, X must consist of the two vertices other than v and since d(v) ≥ 2 both vertices in X must be neighbors of v. Let n > 3 and suppose v has no neighbors in X. Then since d(v) ≥ n/2, there are at least l = ⌈n/2⌉ edges (letters) with one end vertex v and the other end vertex among the p = n-1-⌈n/2⌉ ≥ 1 vertices (pigeonholes) excluding v and X. Since l > p, the pigeonhole principle implies that some pigeonhole vertex must receive at least two edges with the other end vertex being v. This contradicts the fact that G is simple and has no multiple edges. Thus v must have a neighbor in X. ☐   5.3. Theorem. If G is a simple graph with n ≥ 3 vertices and δ ≥ n/2, then the algorithm finds a Hamiltonian circuit in G. Proof. Label the vertices of the graph G as 1, 2, ..., n. Start at any initial vertex u. We first show that the path P(0) produced by Part I contains more than ⌈n/2⌉ vertices. Consider the iterations of Part I.      Iteration 1 (Initialization): We select vertex v1 = u and initialize the path of visited vertices to be v1. Using the hypothesis δ ≥ n/2, there are at least ⌈n/2⌉ unvisited neighbors of v1 and for each unvisited neighbor w of v1 we have η(w) ≥ ⌈n/2⌉-1, since there is only one visited vertex so far. Let v2 = w be the unvisited neighbor of v1 with the smallest label such that  η(w) is a minimum.      Iteration 2: We select vertex v2 and update the path of visited vertices to be v1, v2. Using the hypothesis δ ≥ n/2, there are at least ⌈n/2⌉-1 unvisited neighbors of v2 and for each unvisited neighbor w of v2 we have η(w) ≥ ⌈n/2⌉-2, since there are only two visited vertices so far. Let v3 = w be the unvisited neighbor of v2 with the smallest label such that η(w) is a minimum.      Continuing to iterate this way without termination, we arrive at:      Iteration ⌈n/2⌉: We select vertex v⌈n/2⌉ and update the path of visited vertices to be v1, v2, ..., v⌈n/2⌉. Using the hypothesis δ ≥ n/2, there is at least ⌈n/2⌉-( ⌈n/2⌉-1) = 1 unvisited neighbor w of v⌈n/2⌉ and for each unvisited neighbor w of v⌈n/2⌉ we have η(w) ≥ ⌈n/2⌉-⌈n/2⌉ = 0, since there are only ⌈n/2⌉ visited vertices so far. Let v⌈n/2⌉+1 = w be the unvisited neighbor of v⌈n/2⌉ with the smallest label such that η(w) is a minimum.       We must finally arrive at Iteration k(0) where we select a vertex vk(0) such that vk(0) has no unvisited neighbors and then Part I terminates. Thus, as a result of Part I, we have a path P(0) with vertices u = v1, ..., vk(0) such that all the neighbors of vk(0) are in P(0) and k(0) > ⌈n/2⌉.       We now show that the path P(s) produced by Part II must be a Hamiltonian tour with k(s) = n. If k(0) = n, we are done. Suppose k(0) < n, so that there exists a vertex w outside the path P(0). Note that by construction vk(0) has at least ⌈n/2⌉ neighbors in P(0), say vt(1), vt(2), ..., vt(⌈n/2⌉). Using the Lemma, any such w must have a neighbor amongst the ⌈n/2⌉ distinct vertices vt(1)+1, vt(2)+1, ..., vt(⌈n/2⌉)+1 in P(0). Then the initialization of Part II chooses a vt(i) and a w0 with maximal η(w0) to produce a path P(1) with reordered and renamed vertices u = v1, ..., vk(1) that contains all the vertices of P(0) plus the vertex w0. If k(1) = n, we are done. Again, suppose k(1)

6.     Implementation
We provide a C++ program, hamilton.cpp, in the style of , that implements the algorithm, together with sample input/output files.

6.1. Program. The following program will make a simple console application, and is included in the Demonstration Program package. The program was tested using Microsoft ™ Visual C++ 6.0.

 hamilton.cpp #include   #include   #include   #include using namespace std;  vector procedure_1(vector< vector > graph, vector path);  vector procedure_2(vector< vector > graph, vector path);  vector procedure_2b(vector< vector > graph, vector path); vector procedure_2c(vector< vector > graph, vector path); vector procedure_3(vector< vector > graph, vector path); vector sort(vector > graph); vector >              reindex(vector > graph, vector index); ifstream infile ("graph.txt");     //Input file  ofstream outfile("paths.txt");     //Output file  int main()  {   int i, j, k, n, vertex, edge;   infile>>n;                        //Read number of vertices   vector< vector > graph;       //Read adjacency matrix of graph   for(i=0; i row;    for(j=0; j>edge;     row.push_back(edge);    }    graph.push_back(row);   }  vector index=sort(graph);  graph=reindex(graph,index);  for(vertex=0; vertex path;    path.push_back(vertex);           //Select initial vertex   path=procedure_1(graph,path);     //Part I    path=procedure_2(graph,path);     //Part II   k=path.size();    if(k circuit_maker=procedure_3(graph,path);     if(!circuit_maker.empty())     {      for(j=0; jcircuit_maker[j]; k--)        outfile< procedure_1(vector< vector > graph, vector path)  {   int i, j, k, n=graph.size();   vector extended_path;   vector visited;   for(i=0; i neighbor;    for(i=0; i next_neighbor;       for(j=0; j procedure_2(vector< vector > graph, vector path)  {   int i, j, k, n=graph.size();  bool quit=false;  while(quit!=true)   {   int m=path.size(), inlet=-1, outlet=-1;   vector neighbor;    for(i=0; i unvisited;     for(i=0; i next_neighbor;         for(k=0; k=maximum)          {           inlet=neighbor[i];           outlet=unvisited[j];           maximum=eta;          }        }     }     vector extended_path;     if(inlet!=-1 && outlet!=-1)     {      for(i=0; i<=inlet; i++)       extended_path.push_back(path[i]);      for(i=path.size()-1; i>inlet; i--)       extended_path.push_back(path[i]);      extended_path.push_back(outlet);     }     if(!extended_path.empty()) path=extended_path;     if(m procedure_2b(vector< vector > graph, vector path) {   int i, j, k, l, p, n=graph.size();  bool quit=false;  while(quit!=true)   {    vector extended_path;    int m=path.size();   vector unvisited;   for(i=0; i temp_path;        temp_path.push_back(unvisited[j]);        vector temp_extended_path;        vector temp_visited;        for(l=0; l neighbor;        for(l=0; l next_neighbor;           for(k=0; ki; p--)      {       if(graph[path[p]][last_vertex]==1           && graph[path[i+1]][path[p+1]]==1)       {        check=true;        vj=p;        break;       }      }      if(check==false)      {       temp_extended_path.pop_back();       last_vertex=temp_extended_path[temp_extended_path.size()-1];      }      }      if(check==true)      {       vector temp;       for(p=0; p<=i; p++)       temp.push_back(path[p]);       for(p=0; pi; p--)       temp.push_back(path[p]);       for(p=vj+1; p procedure_2c(vector< vector > graph, vector path) {    vector reversed_path;   for(int i=path.size()-1; i>=0; i--) reversed_path.push_back(path[i]);   reversed_path=procedure_2b(graph,reversed_path);   return reversed_path; } vector procedure_3(vector< vector > graph, vector path)  {   int i, n=path.size();  vector circuit_maker;   for(i=0; i sort(vector > graph) {  int i, j;  vector degree;  for(i=0; i index;  for(i=0; i >        reindex(vector > graph, vector index) {   int i, j;   vector > temp=graph;   for(i=0; i

6.2. Input File. The input file for the program, graph.txt, has as first entry the number of vertices of the graph, followed by white space, followed by the entries of the adjacency matrix of the graph in row-major order, all separated by white space. We use the graph of Example 3.2.

graph.txt
 8 0 1 0 0 1 1 0 0  1 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0  0 0 1 0 1 0 0 0  1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1  0 0 0 0 0 1 0 1  0 1 0 0 0 1 1 0 6.3. Output File. The output file for the program, paths.txt, lists the paths, tours and circuits found by the algorithm.

 paths.txt Hamiltonian Tour: 1 5 4 3 2 8 6 7  Hamiltonian Circuit: 1 5 4 3 2 8 7 6  Path: 2 1 5 3 4  Hamiltonian Tour: 3 4 5 1 2 8 6 7  Hamiltonian Tour: 4 3 5 1 2 8 6 7  Hamiltonian Tour: 5 4 3 2 1 6 7 8  Hamiltonian Circuit: 5 4 3 2 8 7 6 1  Hamiltonian Tour: 6 7 8 2 1 5 3 4  Hamiltonian Tour: 7 6 8 2 1 5 3 4  Hamiltonian Tour: 8 7 6 1 2 3 4 5  Hamiltonian Circuit: 8 7 6 1 5 4 3 2

7.     Platonic Solids
Convex polyhedra with faces composed of congruent convex regular polygons are called Platonic solids. In the last Book of the Elements, Euclid  proved that there are exactly five platonic solids as described by Plato : the tetrahedron, the octahedron, the cube, the icosahedron and the dodecahedron. The graphs consisting of vertices and edges of the platonic solids are Hamiltonian. We run the program for the graphs of the five platonic solids and show the first Hamiltonian circuit found by the algorithm in each case. For more details, input/output files and visualization see the demonstration program     8.     Dirac Graphs
A simple graph G with n vertices that satisfies Dirac's conditions n ≥ 3 and δ ≥ n/2 is called a Dirac Graph. By theorem 5.3, we know that the algorithm will always find a Hamiltonian circuit in a Dirac graph. We run the program for a small and a large Dirac graph, showing in each case the first Hamiltonian circuit found. For more details, input/output files and visualization, see the demonstration program.  9.     Knight's Tours
In 840 A.D., al-Adli , a renowned shatranj (chess) player of Baghdad is said to have discovered the first re-entrant knight's tour, a sequence of moves that takes the knight to each square on an 8 × 8 chessboard exactly once, returning to the original square. Many other re-entrant knight's tours were subsequently discovered but Euler  was the first mathematician to do a systematic analysis in 1766, not only for the 8×8 chessboard, but for re-entrant knight's tours on the general n×n chessboard. Given an n×n chessboard, define a knight's graph with a vertex corresponding to each square of the chessboard and an edge connecting vertex i with vertex j if and only if there is a legal knight's move from the square corresponding to vertex i to the square corresponding to vertex j. Thus, a re-entrant knight's tour on the chessboard corresponds to a Hamiltonian circuit in the knight's graph. We run the program on the knight's graphs corresponding to chessboards of  dimensions 8×8, 20×20, 40×40 and show the first re-entrant knight's tour found by the algorithm in each case. For more details, input/output files and visualization, see the demonstration program.   10.     References

  W. R. Hamilton, Memorandum respecting a new System of Roots of Unity, Philosophical Magazine, volume 12 (4th series), 1856.  G. A. Dirac, Some theorems on abstract graphs, Proc. London. Math. Soc. 2, 1952.  R. M. Karp, Reducibility among combinatorial problems, Complexity of Computer Computations, Plenum Press, 1972.  Stephen Cook, The P versus NP Problem, Official Problem Description, Millennium Problems, Clay Mathematics Institute, 2000.  J. A. Bondy and U.S.R. Murty , Graph Theory with Applications, North-Holland, 1976.  Stanley Lippman, Essential C++, Addison-Wesley, 2000.  H. J. R. Murray, A History of Chess, Oxford University Press, 1913.  L. Euler, Solution d'une question curieuse qui ne paroit soumise a aucune analyse, Mémoires de l'Académie Royale des Sciences et Belles Lettres de Berlin, Année 1759 15, 310-337, 1766.  Euclid, Elements, circa 300 B.C.  Plato, Timaeaus, circa 350 B.C.