References etc.
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@ -359,7 +359,8 @@ The expected value for $X$ in a geometric distribution is
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\index{Markov chain}
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A \key{Markov chain} is a random process
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A \key{Markov chain}\footnote{A. A. Markov (1856--1922)
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was a Russian mathematician.} is a random process
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that consists of states and transitions between them.
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For each state, we know the probabilities
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for moving to other states.
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@ -516,7 +517,9 @@ It turns out that we can find order statistics
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using a randomized algorithm without sorting the array.
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The algorithm is a Las Vegas algorithm:
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its running time is usually $O(n)$
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but $O(n^2)$ in the worst case.
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but $O(n^2)$ in the worst case\footnote{C. A. R. Hoare
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discovered both this algorithm, known as \key{quickselect} \cite{hoa61b},
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and a similar sorting algorithm, known as \key{quicksort} \cite{hoa61a}.}.
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The algorithm chooses a random element $x$
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in the array, and moves elements smaller than $x$
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@ -561,7 +564,8 @@ answer would by easier than to calculate it from scratch.
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It turns out that we can solve the problem
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using a Monte Carlo algorithm whose
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time complexity is only $O(n^2)$.
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time complexity is only $O(n^2)$\footnote{This algorithm is sometimes
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called \index{Freivalds' algoritm} \key{Freivalds' algorithm} \cite{fre77}.}.
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The idea is simple: we choose a random vector
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$X$ of $n$ elements, and calculate the matrices
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$ABX$ and $CX$. If $ABX=CX$, we report that $AB=C$,
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@ -248,7 +248,8 @@ and this is always the final state.
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It turns out that we can easily classify
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any nim state by calculating
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the \key{nim sum} $x_1 \oplus x_2 \oplus \cdots \oplus x_n$,
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where $\oplus$ is the xor operation.
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where $\oplus$ is the xor operation\footnote{The optimal strategy
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for nim was published in 1901 by C. L. Bouton \cite{bou01}}.
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The states whose nim sum is 0 are losing states,
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and all other states are winning states.
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For example, the nim sum for
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@ -367,7 +368,8 @@ so the nim sum is not 0.
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\index{Sprague–Grundy theorem}
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The \key{Sprague–Grundy theorem} generalizes the
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The \key{Sprague–Grundy theorem}\footnote{The theorem was discovered
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independently by R. Sprague \cite{spr35} and P. M. Grundy \cite{gru39}} generalizes the
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strategy used in nim to all games that fulfil
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the following requirements:
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30
list.tex
30
list.tex
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@ -35,6 +35,11 @@
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\emph{Programming Pearls}.
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Addison-Wesley, 1986.
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\bibitem{bou01}
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C. L. Bouton.
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Nim, a game with a complete mathematical theory.
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\emph{Annals of Mathematics}, 3(1/4):35--39, 1901.
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\bibitem{cod15}
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Codeforces: On ''Mo's algorithm'',
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\url{http://codeforces.com/blog/entry/20032}
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@ -94,6 +99,11 @@
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Maximal flow through a network.
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\emph{Canadian Journal of Mathematics}, 8(3):399--404, 1956.
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\bibitem{fre77}
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R. Freivalds.
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Probabilistic machines can use less running time.
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In \emph{IFIP congress}, 839--842, 1977.
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\bibitem{gal14}
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F. Le Gall.
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Powers of tensors and fast matrix multiplication.
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@ -116,6 +126,11 @@
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\emph{2014 IEEE 55th Annual Symposium on Foundations of Computer Science},
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621--630, 2014.
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\bibitem{gru39}
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P. M. Grundy.
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Mathematics and games.
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\emph{Eureka}, 2(5):6--8, 1939.
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\bibitem{gus97}
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D. Gusfield.
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\emph{Algorithms on Strings, Trees and Sequences:
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@ -139,6 +154,16 @@
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Über die Möglichkeit, einen Linienzug ohne Wiederholung und ohne Unterbrechung zu umfahren.
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\emph{Mathematische Annalen}, 6(1), 30--32, 1873.
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\bibitem{hoa61a}
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C. A. R. Hoare.
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Algorithm 64: Quicksort.
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\emph{Communications of the ACM}, 4(7):321, 1961.
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\bibitem{hoa61b}
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C. A. R. Hoare.
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Algorithm 65: Find.
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\emph{Communications of the ACM}, 4(7):321--322, 1961.
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\bibitem{hop71}
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J. E. Hopcroft and J. D. Ullman.
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A linear list merging algorithm.
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@ -231,6 +256,11 @@
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A strong-connectivity algorithm and its applications in data flow analysis.
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\emph{Computers \& Mathematics with Applications}, 7(1):67--72, 1981.
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\bibitem{spr35}
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R. Sprague.
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Über mathematische Kampfspiele.
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\emph{Tohoku Mathematical Journal}, 41:438--444, 1935.
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\bibitem{sta06}
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P. Stańczyk.
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\emph{Algorytmika praktyczna w konkursach Informatycznych},
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