CiteSeerX — Table search results — Subexponential lower bounds for randomized pivoting rules for the simplex algorithm

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Table 5. Evaluation of the MARS Algorithm based on 12 nouns, 1 verb, 1 adjective in LDOCE. Word Pos #Senses #Done #Correct Prec

in Class Based Sense Definition Model for Word Sense Tagging and Disambiguation

by Tracy Lin

Table 5. Evaluation of the MARS Algorithm based on 12 nouns, 1 verb, 1 adjective in LDOCE. Word Pos #Senses #Done #Correct Prec

in Class Based Sense Definition Model for Word Sense Tagging and Disambiguation

by Tracy Lin

Table 5. Evaluation of the MARS Algorithm based on 12 nouns, 1 verb, 1 adjective in LDOCE. Word Pos #Senses #Done #Correct Prec

in Class Based Sense Definition Model for Word Sense Tagging and Disambiguation

by Tracy Lin

Table 2.4: Summary of Computational Results Using Lower Bound Based Branching Rules. From the tables we make the following observations: It is in general too costly to perform 10 dual simplex pivots on all fractional vari- ables. Strong branching can be highly e ective on some problems, but the e ectiveness is impacted greatly by the ability to select a suitable subset of variables on which to perform a number of dual simplex pivots. 11

in A Computational Study of Search Strategies for Mixed Integer Programming

by J. T. Linderoth, M. W. P. Savelsbergh 1999

Cited by 33

Table 1: Lower bounds for randomized algorithms.

in Randomized Online Scheduling on Two Uniform Machines

by Leah Epstein, John Noga, Steve Seiden, Jirí Sgall, Gerhard Woeginger

"... In PAGE 8: ... For n = 15 and s = 1 + i=100 for i = 1; : : : ; 99, we have calculated the optimal values of this linear program by using CPLEX. We display the actual lower bounds in Table1 . The value c(i) is a lower bound for any randomized online algorithm at speed s = 1 + i=100.... ..."

Table 4. Asymptotic lower bounds for online simplex range searching.

in Geometric Range Searching and Its Relatives

by Pankaj K. Agarwal, Jeff Erickson 1999

"... In PAGE 28: ... Erickson apos;s techniques also imply nontrivial lower bounds for online and o ine halfspace emptiness searching, but with a few exceptions, these are quite weak. Table4 summarizes the best known lower bounds for online simplex and related queries, and Table 5 summarizes the best known lower bounds for o ine range searching. Lower bounds for emptiness problems apply to counting and reporting problems as well.... ..."

Cited by 205

Table 5. Asymptotic lower bounds for o ine simplex range searching.

in Geometric Range Searching and Its Relatives

by Pankaj K. Agarwal , Jeff Erickson

Table 4. Asymptotic lower bounds for online simplex range searching using O(m) space.

in Geometric Range Searching and Its Relatives

by Pankaj K. Agarwal, Jeff Erickson 1999

"... In PAGE 31: ... Erickson apos;s techniques also imply nontrivial lower bounds for online and o ine halfspace emptiness searching, but with a few exceptions, these are quite weak. Table4 summarizes the best known lower bounds for online simplex queries, and Table 5 summarizes the best known lower bounds for o ine simplex range searching. Lower bounds for emptiness problems apply to counting and reporting problems as well.... ..."

Cited by 205

Table 1. Runtime in seconds for 100; 000 random points in dimension d: pivoting (solid line) and move-to-front (dotted line) (left). Runtime in seconds on regular d-simplex in dimension d (right).

in unknown title

by unknown authors 1999

"... In PAGE 12: ... I have tested the algorithm on random point sets up to di- mension 30 to evaluate the speed of the method, in particular with respect to the relation between the pivoting and the move-to-front variant. Table1 (left) shows the respective runtimes for 100; 000 points randomly chosen in the d-dimensional unit cube, in logarithmic scale (averaged over 100 runs). All runtimes (excluding the time for generating and storing the points) have been obtained on a SUN Ultra-Sparc II (248 MHz), compiling with the GNU C++-Compiler g++ Version 2.... In PAGE 12: ... In this case, the number of support points is d. Table1 (right) shows... ..."

Cited by 15

Table 1. Runtime in seconds for 100; 000 random points in dimension d: pivoting (solid line) and move-to-front (dotted line) (left). Runtime in seconds on regular d-simplex in dimension d (right).

in unknown title

by unknown authors 1999

Cited by 15

Results 1 - 10 of 96,109

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Table 5. Evaluation of the MARS Algorithm based on 12 nouns, 1 verb, 1 adjective in LDOCE. Word Pos #Senses #Done #Correct Prec

Table 5. Evaluation of the MARS Algorithm based on 12 nouns, 1 verb, 1 adjective in LDOCE. Word Pos #Senses #Done #Correct Prec

Table 5. Evaluation of the MARS Algorithm based on 12 nouns, 1 verb, 1 adjective in LDOCE. Word Pos #Senses #Done #Correct Prec

Table 1: Lower bounds for randomized algorithms.

Table 4. Asymptotic lower bounds for online simplex range searching.

Table 5. Asymptotic lower bounds for o ine simplex range searching.

Table 4. Asymptotic lower bounds for online simplex range searching using O(m) space.

Table 1. Runtime in seconds for 100; 000 random points in dimension d: pivoting (solid line) and move-to-front (dotted line) (left). Runtime in seconds on regular d-simplex in dimension d (right).

Table 1. Runtime in seconds for 100; 000 random points in dimension d: pivoting (solid line) and move-to-front (dotted line) (left). Runtime in seconds on regular d-simplex in dimension d (right).