### Table 1: E#0Ecient qsm algorithms for several fundamental problems.

1999

"... In PAGE 5: ... It is also established in Section 4 that there is not much loss in generalityinhaving the gap parameter only at processors, and not at memory locations. 3 Algorithmic Results Table1 summarizes the time and work bounds for qsm algorithms for several basic problems. Most of these results are the consequence of the following four Observations, all of which are from #5B22#5D.... ..."

Cited by 9

### Table 1: Fast, e cient low-contention parallel algorithms for several fundamental problems. For the

1996

"... In PAGE 3: ... This paper considers ve such problems | generating a random permutation, multiple compaction, distributive sorting, parallel hashing, and load balancing | and presents fast, work-optimal qrqw pram algorithms for these fundamental problems. These results are summarized in Table1 , and are contrasted with the best known erew pram algorithms for the same problems. All of our algorithms are randomized, and are of the \Las Vegas quot; type;; they always output correct results, and obtain the stated bounds with high probability.... In PAGE 19: ... Examples of cyclic and noncyclic permutations are given in Figure 1. As indicated in Table1 , the best known linear work random permutation algorithm for the erew pram run in O(n ) time, for xed gt;0. This is also the best bound known for the random cyclic permutation problem.... In PAGE 26: ... Our result is for distinct keys. As shown in Table1 , the best known linear work erew pram algorithm for this problem runs in O(n ) time. 6.... In PAGE 29: ...1 Distributive Sorting The sorting from U(0;; 1) problem is to sort n numbers chosen uniformly at random from the range (0;; 1). As indicated in Table1 , the best known linear work erew pram algorithm for this problem runs in O(n ) time, for xed gt;0. erew pram algorithms that run in polylog time are work ine cien tbyatleasta p lg n lg lgn factor.... ..."

### Table 1: Fast, e#0Ecient low-contention parallel algorithms for several fundamental problems. For the

"... In PAGE 11: ...1 Distributive Sorting The sorting from U#280; 1#29 problem is to sort n numbers chosen uniformly at random from the range #280; 1#29. As indicated in Table1 , the best known linear work erew pram algorithm for this problem runs in O#28n #0F #29 time, for #0Cxed #0F#3E0. erew pram algorithms that run in polylog time are work ine#0Ecientby at least a p lg n lg lgn factor.... In PAGE 14: ... Our result is for distinct keys. As shown in Table1 , the best known linear work erew pram algorithm for this problem runs in O#28n #0F #29 time. 6.... In PAGE 21: ... Examples of cyclic and noncyclic permutations are given in Figure 1. As indicated in Table1 , the best known linear work random permutation algorithm for the erew pram run in O#28n #0F #29 time, for #0Cxed #0F#3E0. This is also the best bound known for the random cyclic permutation problem.... In PAGE 37: ... This paper considers #0Cve such problems | generating a random permutation, multiple compaction, distributive sorting, parallel hashing, and load balancing | and presents fast, work-optimal qrqw pram algorithms for these fundamental problems. These results are summarized in Table1 , and are contrasted with the best known erew pram algorithms for the same problems. All of our algorithms are randomized, and are of the #5CLas Vegas quot; type; they always output correct results, and obtain the stated bounds with high probability.... ..."

### TABLE I Classification of the fundamental problems that counter the advantages of case-based design. Entries in parenthesis refer to systems that would also use case combination.

1993

Cited by 6

### Table 1.6: The time complexity of some fundamental graph drawing problems: general graphs and digraphs.

1997

Cited by 14

### Table 1.7: The time complexity of some fundamental graph drawing problems: planar graphs and digraphs.

1997

Cited by 14

### TABLE 44.3.2 The time complexity of some fundamental graph drawing problems: planar graphs and digraphs.

### Table 11 summarizes several topics which, in our opinion, are at the frontiers of pattern recognition. As we can see from Table 11, many fundamental research problems in statistical pattern recognition remain at the forefront even

2000

Cited by 369

### Table 11 summarizes several topics which, in our opinion, are at the frontiers of pattern recognition. As we can see from Table 11, many fundamental research problems in statistical pattern recognition remain at the forefront even

2000

Cited by 369

### Table 11 summarizes several topics which, in our opinion, are at the frontiers of pattern recognition. As we can see from Table 11, many fundamental research problems in statistical pattern recognition remain at the forefront even

2000

Cited by 369