### Table 4.1: Independent Optimization Phases

### Table 5: Effect of Vendor Independent Optimizations on Allegro CL Application Performance

1994

Cited by 2

### Table 5: Effect of Vendor Independent Optimizations on Allegro CL Application Performance

1994

Cited by 2

### Table 5: Effect of Vendor Independent Optimizations on Allegro CL Application Performance

1994

Cited by 2

### Table 5: Effect of Vendor Independent Optimizations on Allegro CL Application Performance

### TABLE III. Four-Residue Segment Reconstruction Predictions

2004

Cited by 14

### TABLE VI. Long Segment Reconstruction Results

2004

Cited by 14

### Table 10. Encoding frame rates and speed improvements for all the platform independent optimizations.

2000

"... In PAGE 18: ... D. Overall Performance Improvements Table10 summarizes the speed performance improvements when all of the platform independent algorithmic optimizations are enabled. The partial SAD computation technique is not employed when the fast ME is used.... In PAGE 28: ...able 9. MMX implementation speed performance improvements for each module. ................................ .31 Table10 .... ..."

Cited by 7

### Table 9: Results of ten independent runs of a stochastic optimization algorithm on the third subproblem of Example 2

1998

"... In PAGE 17: ...390600. Table9 comprises the results of ten independent runs of a stochastic global optimization algorithm on the third subproblem. The x3 value for the second run was actually 0.... ..."

Cited by 3

### Table 4-3. Optimal bids for independent or correlated prices, numerical example

in Interdependencies of Electricity Market Characteristics and Bidding Strategies of Power Producers

2002

"... In PAGE 8: ...able 3-10. Profits generator 1 and 2 during 01/01/2002-02/28/2002 ............................. 58 Table4 -1.... In PAGE 8: ...able 4-1. Approach matrix.............................................................................................. 75 Table4 -2.... In PAGE 8: ...able 4-2. Optimal bids and expected profits, numerical example .................................. 83 Table4 -3.... In PAGE 74: ... In order to compare the results, the unconditional probability distribution of the second hour is the same as in the uncorrelated variant. Table4 -1 shows the matrix of different approaches. ... In PAGE 75: ... 75 MC P(k) t (hours) 1 p(P2) P2 P1 2 P2 b p(P2) P2 |P1= b P2 |P1= a a p(P2) P2 |P1= d P2 |P1= c p(P2) p(P2) P2 |P1= e p(P2) Figure 4-8. Version c: prices between periods are correlated Simultaneous Sequential On/ Off decision; Bids MC 1A 1B On/ Off decision; Bids to participate 2A 2B Prices independent Bid height; Optimal bid 3A 3B On/ Off decision; Bids MC 1Ac 1Bc On/ Off decision; Bids to participate 2Ac 2Bc Prices correlated Bid height; Optimal bid 3Ac 3Bc Table4 -1. Approach matrix 4.... In PAGE 82: ... We use one example to illustrate our conclusions. Parameters are listed in 3 Appendix E E; Table4 -2 contains the numerical results. Bold numbers are expected profits for each of the optimization methods and assumed dependencies.... In PAGE 83: ...7650 (60, [x]) 2.0503 Table4 -2. Optimal bids and expected profits, numerical example 4.... In PAGE 84: ....7.2 Knowledge about Correlation If prices have the same unconditional probability distribution, but correlation between successive hours exists, then the optimal bid decisions are different. In our numerical example, the optimal bid sequence in the independent case is either (58,52) or (60,54), whereas it is (60,52) in the case that the prices of each hour are correlated ( Table4 -3). Figure 4-9 shows the difference in expected profits for possible bids under the assumption of uncorrelated prices (solid lines) or correlated prices (dashed lines).... ..."