Academic literature on the topic 'Pebbling'

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Journal articles on the topic "Pebbling"

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Ye, Yongsheng, Fang Liu, and Caixia Shi. "The 2-Pebbling Property of the Middle Graph of Fan Graphs." Journal of Applied Mathematics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/304514.

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A pebbling move on a graphGconsists of taking two pebbles off one vertex and placing one pebble on an adjacent vertex. The pebbling number of a connected graphG, denoted byf(G), is the leastnsuch that any distribution ofnpebbles onGallows one pebble to be moved to any specified but arbitrary vertex by a sequence of pebbling moves. This paper determines the pebbling numbers and the 2-pebbling property of the middle graph of fan graphs.
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Bunde, David P., Erin W. Chambers, Daniel Cranston, Kevin Milans, and Douglas B. West. "Pebbling and optimal pebbling in graphs." Journal of Graph Theory 57, no. 3 (2008): 215–38. http://dx.doi.org/10.1002/jgt.20278.

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Li, Yueqing, and Yongsheng Ye. "The 2-pebbling property of squares of paths and Graham’s conjecture." Open Mathematics 18, no. 1 (March 10, 2020): 87–92. http://dx.doi.org/10.1515/math-2020-0009.

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Abstract A pebbling move on a graph G consists of taking two pebbles off one vertex and placing one pebble on an adjacent vertex. The pebbling number of a connected graph G, denoted by f(G), is the least n such that any distribution of n pebbles on G allows one pebble to be moved to any specified vertex by a sequence of pebbling moves. In this paper, we determine the 2-pebbling property of squares of paths and Graham’s conjecture on $\begin{array}{} P_{2n}^2 \end{array} $.
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Moews, David. "Pebbling graphs." Journal of Combinatorial Theory, Series B 55, no. 2 (July 1992): 244–52. http://dx.doi.org/10.1016/0095-8956(92)90043-w.

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Lourdusamy, A., and T. Mathivanan. "Herscovici’s conjecture on C2n × G." Discrete Mathematics, Algorithms and Applications 12, no. 05 (July 1, 2020): 2050071. http://dx.doi.org/10.1142/s1793830920500718.

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The [Formula: see text]-pebbling number, [Formula: see text], of a connected graph [Formula: see text], is the smallest positive integer such that from every placement of [Formula: see text] pebbles, [Formula: see text] pebbles can be moved to any specified target vertex by a sequence of pebbling moves, each move taking two pebbles off a vertex and placing one on an adjacent vertex. A graph [Formula: see text] satisfies the [Formula: see text]-pebbling property if [Formula: see text] pebbles can be moved to any specified vertex when the total starting number of pebbles is [Formula: see text], where [Formula: see text] is the number of vertices with at least one pebble. We show that the cycle [Formula: see text] satisfies the [Formula: see text]-pebbling property. Herscovici conjectured that for any connected graphs [Formula: see text] and [Formula: see text], [Formula: see text]. We prove Herscovici’s conjecture is true, when [Formula: see text] is an even cycle and for variety of graphs [Formula: see text] which satisfy the [Formula: see text]-pebbling property.
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Cusack, Charles A., Airat Bekmetjev, and Mark Powers. "Two-pebbling and odd-two-pebbling are not equivalent." Discrete Mathematics 342, no. 3 (March 2019): 777–83. http://dx.doi.org/10.1016/j.disc.2018.10.030.

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Gao, Ze-Tu, and Jian-Hua Yin. "The optimal pebbling of spindle graphs." Open Mathematics 17, no. 1 (November 13, 2019): 582–87. http://dx.doi.org/10.1515/math-2019-0094.

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Abstract Given a distribution of pebbles on the vertices of a connected graph G, a pebbling move on G consists of taking two pebbles off one vertex and placing one on an adjacent vertex. The optimal pebbling number of G, denoted by πopt(G), is the smallest number m such that for some distribution of m pebbles on G, one pebble can be moved to any vertex of G by a sequence of pebbling moves. Let Pk be the path on k vertices. Snevily defined the n–k spindle graph as follows: take n copies of Pk and two extra vertices x and y, and then join the left endpoint (respectively, the right endpoint) of each Pk to x (respectively, y), the resulting graph is denoted by S(n, k), and called the n–k spindle graph. In this paper, we determine the optimal pebbling number for spindle graphs.
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Chung, Fan, Ron Graham, John Morrison, and Andrew Odlyzko. "Pebbling a Chessboard." American Mathematical Monthly 102, no. 2 (February 1995): 113. http://dx.doi.org/10.2307/2975345.

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Chung, Fan, Ron Graham, John Morrison, and Andrew Odlyzko. "Pebbling a Chessboard." American Mathematical Monthly 102, no. 2 (February 1995): 113–23. http://dx.doi.org/10.1080/00029890.1995.11990546.

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Kirousis, Lefteris M., and Christos H. Papadimitriou. "Searching and pebbling." Theoretical Computer Science 47 (1986): 205–18. http://dx.doi.org/10.1016/0304-3975(86)90146-5.

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Dissertations / Theses on the topic "Pebbling"

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Yerger, Carl. "Extensions of Graph Pebbling." Scholarship @ Claremont, 2005. https://scholarship.claremont.edu/hmc_theses/176.

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My thesis will consist of extensions to results that I proved at the 2004 East Tennessee State REU. Most of these results have to do with graph pebbling and various probabilistic extensions. Specifically, in Chapter 2 we compute the cover pebbling number for complete multipartite graphs and prove upper bounds for cover pebbling numbers for graphs of a specified diameter and order. We also prove that the cover pebbling decision problem is NP complete. In Chapters 3 and 4 we examine domination cover pebbling. In Chapter 5, we obtain structural and probabilistic results for deep graphs, and in Chapter 6 we compute cover pebbling probability thresholds for the complete graph.
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Bounadi, Monir. "The Foundations of Graph Pebbling." Thesis, Stockholms universitet, Matematiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-128482.

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Graph pebbling modeling started as a method for solving a combinatorialnumber theory conjecture by Erdös and Lemke. Using thismethod, Chung proved the conjecture in 1989. Since then, the literaturehas grown considerably. Several variations and possible applicationshave been discussed, in graph theory, computer science andnetwork optimization. The main focus in graph pebbling is graphs, mathematical structuresmodeling binary relations between vertices. To every vertex insome graph we assign a number of pebbles. If two pebbles are movedacross an edge joining two distinct vertices, one pebble arrives andone pebble is lost. This is called a pebbling step. The basic question in graph pebbling asks if one may from a givendistribution of pebbles on a set of vertices move to another distributionon the same set via a series of pebbling steps. In this Master’s thesis we approach the above question using twomodels: a deterministic, which includes the notion of a pebblingnumber, and a probabilistic, which includes the notion of a threshold. For both these models we clarify earlier proofs, and provide newproofs, of foundational theorems in graph pebbling. These resultsconstitute the backbone for our discussion on recent research, whichconcentrates on generalizing and extending central notions in graphpebbling, for example the generalized idea of a pebbling number:the pi-pebbling function. Simultaneously, a corollary to the so calledcover pebbling theorem is derived. This corollary lets us prove established,and newly found, theorems. Regarding applications in graph pebbling, we argue that one shouldgeneralize existing results, and incorporate directed graphs into abigger part of the theory. We suggest how this can be done.

Min handledare, Cecilia Holmgren, var tidigare anställd vid Matematiska institutionen, Stockholms universitet, men arbetar numera vid Matematiska institutionen, Uppsala universitet.

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Nyberg, Brodda Carl-Fredrik. "Deterministic and Random Pebbling of Graphs." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325833.

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Murphy, Kyle. "On t-Restricted Optimal Rubbling of Graphs." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3251.

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For a graph G = (V;E), a pebble distribution is defined as a mapping of the vertex set in to the integers, where each vertex begins with f(v) pebbles. A pebbling move takes two pebbles from some vertex adjacent to v and places one pebble on v. A rubbling move takes one pebble from each of two vertices that are adjacent to v and places one pebble on v. A vertex x is reachable under a pebbling distribution f if there exists some sequence of rubbling and pebbling moves that places a pebble on x. A pebbling distribution where every vertex is reachable is called a rubbling configuration. The t-restricted optimal rubbling number of G is the minimum number of pebbles required for a rubbling configuration where no vertex is initially assigned more than t pebbles. Here we present results on the 1-restricted optimal rubbling number and the 2- restricted optimal rubbling number.
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Nordström, Jakob. "Short Proofs May Be Spacious : Understanding Space in Resolution." Doctoral thesis, KTH, Teoretisk datalogi, TCS, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4704.

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Om man ser på de bästa nu kända algoritmerna för att avgöra satisfierbarhet hos logiska formler så är de allra flesta baserade på den så kallade DPLL-metoden utökad med klausulinlärning. De två viktigaste gränssättande faktorerna för sådana algoritmer är hur mycket tid och minne de använder, och att förstå sig på detta är därför en fråga som har stor praktisk betydelse. Inom området beviskomplexitet svarar tids- och minnesåtgång mot längd och minne hos resolutionsbevis för formler i konjunktiv normalform (CNF-formler). En lång rad arbeten har studerat dessa mått och även jämfört dem med bredden av bevis, ett annat mått som visat sig höra nära samman med både längd och minne. Mer formellt är längden hos ett bevis antalet rader, dvs. klausuler, bredden är storleken av den största klausulen, och minnet är maximala antalet klausuler som man behöver komma ihåg samtidigt om man under bevisets gång bara får dra nya slutsatser från klausuler som finns sparade. För längd och bredd har man lyckats visa en rad starka resultat men förståelsen av måttet minne har lämnat mycket i övrigt att önska. Till exempel så är det känt att minnet som behövs för att bevisa en formel är minst lika stort som den nödvändiga bredden, men det har varit en öppen fråga om minne och bredd kan separeras eller om de två måtten mäter "samma sak" i den meningen att de alltid är asymptotiskt lika stora för en formel. Det har också varit okänt om det faktum att det finns ett kort bevis för en formel medför att formeln också kan bevisas i litet minne (motsvarande påstående är sant för längd jämfört med bredd) eller om det tvärtom kan vara så att längd och minne är "helt orelaterade" på så sätt att även korta bevis kan kräva maximal mängd minne. I denna avhandling presenterar vi först ett förenklat bevis av trade-off-resultatet för längd jämfört med minne i (Hertel och Pitassi 2007) och visar hur samma idéer kan användas för att visa ett par andra exponentiella avvägningar i relationerna mellan olika beviskomplexitetsmått för resolution. Sedan visar vi att det finns formler som kan bevisas i linjär längd och konstant bredd men som kräver en mängd minne som växer logaritmiskt i formelstorleken, vilket vi senare förbättrar till kvadratroten av formelstorleken. Dessa resultat separerar således minne och bredd. Genom att använda andra men besläktade idéer besvarar vi därefter frågan om hur minne och längd förhåller sig till varandra genom att separera dem på starkast möjliga sätt. Mer precist visar vi att det finns CNF-formler av storlek O(n) som har resolutionbevis i längd O(n) och bredd O(1) men som kräver minne minst Omega(n/log n). Det gemensamma temat för dessa resultat är att vi studerar formler som beskriver stenläggningsspel, eller pebblingspel, på riktade acykliska grafer. Vi bevisar undre gränser för det minne som behövs för den så kallade pebblingformeln över en graf uttryckt i det svart-vita pebblingpriset för grafen i fråga. Slutligen observerar vi att vår optimala separation av minne och längd i själva verket är ett specialfall av en mer generell sats. Låt F vara en CNF-formel och f:{0,1}^d->{0,1} en boolesk funktion. Ersätt varje variabel x i F med f(x_1, ..., x_d) och skriv om denna nya formel på naturligt sätt som en CNF-formel F[f]. Då gäller, givet att F och f har rätt egenskaper, att F[f] kan bevisas i resolution i väsentligen samma längd och bredd som F, men att den minimala mängd minne som behövs för F[f] är åtminstone lika stor som det minimala antalet variabler som måste förekomma samtidigt i ett bevis för F.
Most state-of-the-art satisfiability algorithms today are variants of the DPLL procedure augmented with clause learning. The two main bottlenecks for such algorithms are the amounts of time and memory used. Thus, understanding time and memory requirements for clause learning algorithms, and how these requirements are related to one another, is a question of considerable practical importance. In the field of proof complexity, these resources correspond to the length and space of resolution proofs for formulas in conjunctive normal form (CNF). There has been a long line of research investigating these proof complexity measures and relating them to the width of proofs, another measure which has turned out to be intimately connected with both length and space. Formally, the length of a resolution proof is the number of lines, i.e., clauses, the width of a proof is the maximal size of any clause in it, and the space is the maximal number of clauses kept in memory simultaneously if the proof is only allowed to infer new clauses from clauses currently in memory. While strong results have been established for length and width, our understanding of space has been quite poor. For instance, the space required to prove a formula is known to be at least as large as the needed width, but it has remained open whether space can be separated from width or whether the two measures coincide asymptotically. It has also been unknown whether the fact that a formula is provable in short length implies that it is also provable in small space (which is the case for length versus width), or whether on the contrary these measures are "completely unrelated" in the sense that short proofs can be maximally complex with respect to space. In this thesis, as an easy first observation we present a simplified proof of the recent length-space trade-off result for resolution in (Hertel and Pitassi 2007) and show how our ideas can be used to prove a couple of other exponential trade-offs in resolution. Next, we prove that there are families of CNF formulas that can be proven in linear length and constant width but require space growing logarithmically in the formula size, later improving this exponentially to the square root of the size. These results thus separate space and width. Using a related but different approach, we then resolve the question about the relation between space and length by proving an optimal separation between them. More precisely, we show that there are families of CNF formulas of size O(n) that have resolution proofs of length O(n) and width O(1) but for which any proof requires space Omega(n/log n). All of these results are achieved by studying so-called pebbling formulas defined in terms of pebble games over directed acyclic graphs (DAGs) and proving lower bounds on the space requirements for such formulas in terms of the black-white pebbling price of the underlying DAGs. Finally, we observe that our optimal separation of space and length is in fact a special case of a more general phenomenon. Namely, for any CNF formula F and any Boolean function f:{0,1}^d->{0,1}, replace every variable x in F by f(x_1, ..., x_d) and rewrite this new formula in CNF in the natural way, denoting the resulting formula F[f]. Then if F and f have the right properties, F[f] can be proven in resolution in essentially the same length and width as F but the minimal space needed for F[f] is lower-bounded by the number of variables that have to be mentioned simultaneously in any proof for F.
QC 20100831
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Chiang, Hung-Hsing, and 姜宏興. "Optimally t-Pebbling Graphs." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3tnr33.

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博士
中原大學
應用數學研究所
106
Let $G$ be a simple graph. A distribution $delta$ of $G$ is a mapping from $V(G)$ into the set of non-negative integers, where $delta(v)$ is the number of pebbles distributed to the vertex $v$ for each $vin V(G)$. A {it pebbling move} consists of removing two pebbles from one vertex and then placing one pebble at an adjacent vertex. A distribution of a given graph $G$ is $t$-fold solvable if, whenever we choose any target vertex $v$ of $G$, we can move $t$ pebbles on $v$ by using pebbling moves. The optimal $t$-pebbling number of the graph $G$, denoted by $pi^*_{t}(G)$, is the minimum number of pebbles necessary so that there is a $t$-fold solvable distribution of $G$. When $t=1$, the optimal $t$-pebbling number is the optimal pebbling number. In this thesis, we mainly study the optimal $t$-pebbling number of a graph. In Chapter $1$, we introduce the background of this study and give some basic definitions. For completeness, we also introduce the known results on the optimal pebbling number and the optimal $t$-pebbling number of a graph in Chapter $2$. In Chapter $3$, we study the optimal $t$-pebbling number of the cycle. As a consequence, we determine the exact value of $pi^*_{2}(C_{n})$ for each cycle $C_n$. We then derive an upper bound and a lower bound for $pi^*_{t}(C_{n})$ for $tgeq 3$. In Chapter $4$, let $T^m_h$ denote the complete $m$-ary tree with height $h$, we first show that $$pi^{ast}_{2}(T^2_{h})=pi^{ast}(T^2_{h+1})qquad ext{and} qquad pi^{ast}_{4}(T^2_{h})=pi^{ast}(T^2_{h+2})$$ and then determine the exact value of $pi^{ast}_{3}(T^2_{h})$. In Chapter $5$, we conclude our research results in this thesis.
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Tsai, Tzung-Han, and 蔡宗翰. "Critical Pebbling Numbers of Graphs." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/78244719347004481682.

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碩士
國立交通大學
應用數學系所
94
Given a distribution of pebbles on the vertices of a graph G, a pebbling step (or pebbling move) takes two pebbles from one vertex and place one pebble on an adjacent vertex. A distribution D of pebbles on the graph G is called solvable if, starting from D, it is possible to place a pebble on any given vertex using a sequence of pebbling steps. The pebbling number f(G) of a connected graph is the smallest number of pebbles such that every distribution with f(G) pebbles on G is solvable. A distribution D is r-solvable if there exists a sequence of pebbling steps which begin from D and end in at least one pebble on the vertex r. The r-critical pebbling number cr(G) is the largest size of a minimally r-solvable rooted distribution on G for any r. In this thesis, we first derive several known results from the study of pebbling numbers, and then we also obtain several new results on critical pebbling numbers. The results are (1) cr(Cm) = f(Cm)-1 if m_3 (mod 4), and f(Cm) otherwise; (2) cr(T) = 2d, where T is a tree and d is the diameter of T; (3) cr(P) = 6, where P is the Petersen graph; (4) cr(Qn) = 2n, where Qn is an n-cube; and (5) cr(C5¤C5) = 25.
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Hertel, Philipp. "Clause Learning, Resolution Space, and Pebbling." Thesis, 2008. http://hdl.handle.net/1807/16736.

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Currently, the most effective complete SAT solvers are based on the DPLL algorithm augmented by Clause Learning. These solvers can handle many real-world problems from application areas like verification, diagnosis, planning, and design. Clause Learning works by storing previously computed, intermediate results and then reusing them to prune the future search tree. Without Clause Learning, however, DPLL loses most of its effectiveness on real world problems. Recently there has been some work on obtaining a deeper understanding of the technique of Clause Learning. In this thesis, we contribute to the understanding of Clause Learning, and the Resolution proof system that underlies it, in a number of ways. We characterize Clause Learning as a new, intuitive Resolution refinement which we call CL. We then show that CL proofs can effectively p-simulate general Resolution. Furthermore, this result holds even for the more restrictive class of greedy, unit propagating CL proofs, which more accurately characterize Clause Learning as it is used in practice. This result is surprising and indicates that Clause Learning is significantly more powerful than was previously known. Since Clause Learning makes use of previously derived clauses, it motivates the study of Resolution space. We contribute to this area of study by proving that determining the variable space of a Resolution derivation is PSPACE-complete. The reduction also yields a surprising exponential size/space trade-off for Resolution in which an increase of just 3 units of variable space results in an exponential decrease in proofsize. This result runs counter to the intuitions of many in the SAT-solving community who have generally believed that proof-size should decrease smoothly as available space increases. In order to prove these Resolution results, we need to make use of some intuition regarding the relationship between Black-White Pebbling and Resolution. In fact, determining the complexity of Resolution variable space required us to first prove that Black-White Pebbling is PSPACE-complete. The complexity of the Black-White Pebbling Game has remained an open problem for 30 years and resisted numerous attempts to solve it. Its solution is the primary contribution of this thesis.
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Chen, Ying-Zhen, and 陳盈臻. "Optimal pebbling of Cartesian product of Graphs." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/73288408397953881699.

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碩士
國立東華大學
應用數學系
104
Given a graph G, a distribution of pebbles on the vertices of G is a function D:V(G)→ℕ∪{0}. A basic pebbling move from a vertex v to a neighbor u takes away two pebbles at v and adds one pebble at u. Before the move, v must have at least two pebbles. A pebbling type α of G is a mapping from V(G) into ℕ∪{0}. A distribution D of pebbles of G is said to be an α-pebbling distribution if whenever we choose a target vertex v in G, we can move α(v) pebbles to v by applying pebbling moves successively(if necessary). In this case, we say that the vertex v is α(v)-solvable under D. For a given positive integer t, a t-pebbling distribution of G is an α-pebbling distribution of G with α(v)=t for any v∈V(G). The optimal α-pebbling number π_{α}^{∗}(G) is the minimum number of pebbles required in an α-pebbling distribution of G. If α is a t-pebbling distribution of G, we use π_{t}^{∗}(G) to replace π_{α}^{∗}(G) and call π_{t}^{∗}(G) the optimal t-pebbling number of G. And when t=1, we use π^{∗}(G) to replace π₁^{∗}(G) for simplicity. We study the optimal pebbling number and optimal 2-pebbling number of grids in this thesis. We give some basic properties for the optimal t-pebbling number of graphs in Section 2, and give the exact value of the optimal 2-pebbling number of P₂×P_{n} in Section 3. And, in Section 4, we give an upper bound for the optimal pebbling number of P_{m}×P_{n} for all n≥m≥6.
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吳柏翰. "A Study on the Optimal Pebbling of Graphs." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/63762129550384143981.

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Books on the topic "Pebbling"

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Pebbling the walk: Surviving cancer caregiving. Portland, Or: Blue Heron Publishing, 2000.

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Book chapters on the topic "Pebbling"

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Galesi, Nicola, and Neil Thapen. "Resolution and Pebbling Games." In Theory and Applications of Satisfiability Testing, 76–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499107_6.

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Dwork, Cynthia, Moni Naor, and Hoeteck Wee. "Pebbling and Proofs of Work." In Advances in Cryptology – CRYPTO 2005, 37–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11535218_3.

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Matias, Yossi, and Ely Porat. "Efficient Pebbling for List Traversal Synopses." In Automata, Languages and Programming, 918–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45061-0_71.

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Král’ovič, Richard. "Time and Space Complexity of Reversible Pebbling." In SOFSEM 2001: Theory and Practice of Informatics, 292–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45627-9_26.

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Ružička, Peter, and Juraj Waczulík. "On time-space trade-offs in dynamic graph pebbling." In Lecture Notes in Computer Science, 671–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57182-5_58.

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Blocki, Jeremiah, and Samson Zhou. "On the Depth-Robustness and Cumulative Pebbling Cost of Argon2i." In Theory of Cryptography, 445–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70500-2_15.

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Blocki, Jeremiah, and Samson Zhou. "On the Computational Complexity of Minimal Cumulative Cost Graph Pebbling." In Financial Cryptography and Data Security, 329–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58387-6_18.

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Ranjan, Desh, John Savage, and Mohammad Zubair. "Upper and Lower I/O Bounds for Pebbling r-Pyramids." In Lecture Notes in Computer Science, 107–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19222-7_12.

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Austrin, Per, Toniann Pitassi, and Yu Wu. "Inapproximability of Treewidth, One-Shot Pebbling, and Related Layout Problems." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32512-0_2.

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Kenter, Franklin, and Daphne Skipper. "Integer-Programming Bounds on Pebbling Numbers of Cartesian-Product Graphs." In Combinatorial Optimization and Applications, 681–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04651-4_46.

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Conference papers on the topic "Pebbling"

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Kwasniewski, Grzegorz, Marko Kabić, Maciej Besta, Joost VandeVondele, Raffaele Solcà, and Torsten Hoefler. "Red-blue pebbling revisited." In SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3295500.3356181.

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Abramsky, Samson, Anuj Dawar, and Pengming Wang. "The pebbling comonad in Finite Model Theory." In 2017 32nd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS). IEEE, 2017. http://dx.doi.org/10.1109/lics.2017.8005129.

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Meuli, Giulia, Mathias Soeken, Martin Roetteler, Nikolaj Bjorner, and Giovanni De Micheli. "Reversible Pebbling Game for Quantum Memory Management." In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2019. http://dx.doi.org/10.23919/date.2019.8715092.

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Nordström, Jakob. "On the Relative Strength of Pebbling and Resolution." In 2010 IEEE 25th Annual Conference on Computational Complexity (CCC). IEEE, 2010. http://dx.doi.org/10.1109/ccc.2010.22.

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Hertel, Philipp, and Toniann Pitassi. "Exponential Time/Space Speedups for Resolution and the PSPACE-completeness of Black-White Pebbling." In 2007 48th Annual IEEE Symposium on Foundations of Computer Science. IEEE, 2007. http://dx.doi.org/10.1109/focs.2007.22.

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Hertel, Philipp, and Toniann Pitassi. "Exponential Time/Space Speedups for Resolution and the PSPACE-completeness of Black-White Pebbling." In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07). IEEE, 2007. http://dx.doi.org/10.1109/focs.2007.4389487.

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