A Strong Connectivity Algorithm And Its Applications in Data Flow Analysis
A Strong Connectivity Algorithm And Its Applications in Data Flow Analysis
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In our eReader you can find the full English version of the book. Read A Strong Connectivity Algorithm And Its Applications in Data Flow Analysis Online - link to read the book on full screen. Our eReader also allows you to upload and read Pdf, Txt, ePub and fb2 books. In the Mini eReder on the page below you can quickly view all pages of the book - Read Book A Strong Connectivity Algorithm And Its Applications in Data Flow Analysis
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To quickly assess the difficulty of the text, read a short excerpt:
In this case we compute the set S(h) = {h} u{{w: w is a T-descendant of h which can reach h along a path consisting solely of T-descendants of h} and extend the SCCROOT map, by mapping all we S (h) to h. The set S(h) is computed as follows (square brackets denote ordered tuples).
S(h) := [h]; NEW := [w: (w, h) e E and w is a T-descendant of h] ; (while NEW is not empty) remove an element w from NEW; SCCROOT (w) := h; add w to S (h) ; add to NEW all nodes v where (v, w) e E, V is a T-descendant ...of h and SCCROOT (v) is undefined; end while; Note that we can test the condition 'v is a T-descendant of h' rapidly using the formula V is a T-descendant of h iff pre (h) (x ) : (m, n) G G}, n e N . N (m, n) m The algorithm in [HU] arranges nodes of N in reverse postorder (with respect to a depth-first spanning tree), and then iterates through this sequence repeatedly (starting with some 'largest' initial value of the x 's), applying (*) to obtain successive approximations of the solution, till these approximations converge to the maximal fixpoint of (*).
What to read after A Strong Connectivity Algorithm And Its Applications in Data Flow Analysis?
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To quickly assess the difficulty of the text, read a short excerpt:
In this case we compute the set S(h) = {h} u{{w: w is a T-descendant of h which can reach h along a path consisting solely of T-descendants of h} and extend the SCCROOT map, by mapping all we S (h) to h. The set S(h) is computed as follows (square brackets denote ordered tuples).
S(h) := [h]; NEW := [w: (w, h) e E and w is a T-descendant of h] ; (while NEW is not empty) remove an element w from NEW; SCCROOT (w) := h; add w to S (h) ; add to NEW all nodes v where (v, w) e E, V is a T-descendant ...of h and SCCROOT (v) is undefined; end while; Note that we can test the condition 'v is a T-descendant of h' rapidly using the formula V is a T-descendant of h iff pre (h) (x ) : (m, n) G G}, n e N . N (m, n) m The algorithm in [HU] arranges nodes of N in reverse postorder (with respect to a depth-first spanning tree), and then iterates through this sequence repeatedly (starting with some 'largest' initial value of the x 's), applying (*) to obtain successive approximations of the solution, till these approximations converge to the maximal fixpoint of (*).
What to read after A Strong Connectivity Algorithm And Its Applications in Data Flow Analysis?
You can find similar books in the "Read Also" column, or choose other free books by M Sharir to read onlineMoreLess
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