### Table 4: Comparison to Iterative Improvement Partitioners

"... In PAGE 30: ... 4.2 Comparison to Other Partitioning Algorithms Table4 shows the comparison of our heap based FM-LSRb to state-of-the-art iterative improvement partitioning algorithms K-DualFM [CLS96] (dual net representation based), GMetis [AHK96] (genetic multi-level graph partitioning based), LA3-CDIP [DD96] (LIFO... ..."

### TABLE I TYPICAL MOVES FOR ITERATIVE IMPROVEMENT

### Table 3. Problems collected in the second iteration and improvements in the third iteration

### Table 7.1: Improved accuracy using Iterative Improvement.

1999

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### Table 4: Comparison to Non-Iterative Improvement Partitioners

1997

"... In PAGE 5: ... The O(lg n) runtime overhead due to the heap based bucket structure is negligible. Table4 shows the comparison of our FM-LSRb to non-iterative improvement partitioning algorithms FBB (network ow based), Paraboli (analytical placement based) and PANZA (network ow and spectral based). PANZA reports the best cutsize result among all categories of partitioning algorithms.... ..."

Cited by 34

### Table 4: Comparison to Non-Iterative Improvement Partitioners

"... In PAGE 5: ... The O(lg n) runtime overhead due to the heap based bucket structure is negligible. Table4 shows the comparison of our FM-LSRb to non-iterative improvement partitioning algorithms FBB (network ow based), Paraboli (analytical placement based) and PANZA (network ow and spectral based). PANZA reports the best cutsize result among all categories of partitioning algorithms.... ..."

### Table 5: Comparison to Non-Iterative Improvement Partitioners

"... In PAGE 31: ... The O(lg n) runtime overhead due to the heap based bucket structure is negligible. Table5 shows the comparison of our FM-LSRb to the state-of-the-art non-iterative im- provement partitioning algorithms FBB [YW94] (network ow based), Paraboli [RDJ94] 1based on the 7 common test circuits... ..."

### Table 1. As an example of this framework, consider the following iterative improve- ment (ItIm) algorithm

"... In PAGE 3: ...Initialize probability model p0(x); t = 0 Step 2: Sample from pt(x) D(k) = fx1; : : : ; xkg Step 3: Evaluate F(k) = ff(x1); : : : ; f(xk)g Step 4: Update probability model : pt(x) ! pt+1(x) Step 5: Increment time t = t + 1 Step 6: Goto Step 2 (until termination condition) Table1 : Algorithmic framework for a general probabilistic population-based EA. Initially, construct a probability density p0(x) that represents the model of the search space (or where it is believed that good solutions are distributed in IRn).... ..."

### TABLE 9 Improving the Amoldi vectors with subspace iteration.

1996

Cited by 16