Input Sensitive Optimal Parallel Randomized Algorithms for Addition And Identif
Input Sensitive Optimal Parallel Randomized Algorithms for Addition And Identif
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In our eReader you can find the full English version of the book. Read Input Sensitive Optimal Parallel Randomized Algorithms for Addition And Identif 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 Input Sensitive Optimal Parallel Randomized Algorithms for Addition And Identif
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We give tne algorithm in two parts : Procedure ADDITION (m') actually performs the addition, assuming an estimate m'= cm +d, (c, d > 1 constants) known. Function ESTIMATION produces such an estimate. So, the whole algorithm has the following high level description : begin m' ^ ESTIMATION ADDITION (m') end We provide the description of ESTIMATION first. In ESTIMATION, each Pi with x-^'o produces k estimates of m (k is a constant) through a probabilistic technique, and then does a variance-reduct...ion process to get the final estimate. The actual . /. . -4- value of k is determined in the analysis. Function ESTIMATION procedure PRODUCE -AN -ESTIMATE begin stage 1 (Initialization) Processor P, initializes a special shared memory location (CLOCK) to zero. Them, each P. Executes TIME. -6- o. 1 stage 2 (Estimate) Processor P. 1 if X. ^ o then begin (1) Flip a fair coin (two-sided) (2) If the autcome is 'tail 'then begin (2a) TIME^- : -r-TIMEj +1 (2b) CLOCK . 1. The rest is a relatively easy calculation, since Prob [ CLOCK o, if we choose k ^ 4/6 then, with probability at least 1-5, we have (1) iE - logml ^ 2 and (2) The total running time of ESTIMATION is 4 ( -5- .
What to read after Input Sensitive Optimal Parallel Randomized Algorithms for Addition And Identif?
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To quickly assess the difficulty of the text, read a short excerpt:
We give tne algorithm in two parts : Procedure ADDITION (m') actually performs the addition, assuming an estimate m'= cm +d, (c, d > 1 constants) known. Function ESTIMATION produces such an estimate. So, the whole algorithm has the following high level description : begin m' ^ ESTIMATION ADDITION (m') end We provide the description of ESTIMATION first. In ESTIMATION, each Pi with x-^'o produces k estimates of m (k is a constant) through a probabilistic technique, and then does a variance-reduct...ion process to get the final estimate. The actual . /. . -4- value of k is determined in the analysis. Function ESTIMATION procedure PRODUCE -AN -ESTIMATE begin stage 1 (Initialization) Processor P, initializes a special shared memory location (CLOCK) to zero. Them, each P. Executes TIME. -6- o. 1 stage 2 (Estimate) Processor P. 1 if X. ^ o then begin (1) Flip a fair coin (two-sided) (2) If the autcome is 'tail 'then begin (2a) TIME^- : -r-TIMEj +1 (2b) CLOCK . 1. The rest is a relatively easy calculation, since Prob [ CLOCK o, if we choose k ^ 4/6 then, with probability at least 1-5, we have (1) iE - logml ^ 2 and (2) The total running time of ESTIMATION is 4 ( -5- .
What to read after Input Sensitive Optimal Parallel Randomized Algorithms for Addition And Identif?
You can find similar books in the "Read Also" column, or choose other free books by Paul G Spirakis to read onlineMoreLess
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