Other Optimizations

If associativity and commutativity are not used, a synthesis tool may generate three adders and one subtracter after subexpression identification, it may generate only two adders and one subtracter, thus providing increased savings in logic. 

In general, there are two other optimizations that a synthesis tool has no problem handling. These are  

These optimizations are usually performed by a synthesis system and a designer does not have to worry about it. These optimizations are nonetheless explained below. 

Dead code elimination deletes code that never gets executed. For example, 

Clearly, there is no need to synthesize an and gate since the assignment statement will never get executed and represents dead code. 

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Pattern 6.2, The Golden Rule versus Other Optimizations This is the pattern to use when stringent requirements about reuse, performance, or flexibility keep you from maintaining the most straightforward continuity to code. 

Compromise with Practicality. The design that mirrors the users concepts most closely is not always the most efficient, and it sometimes takes significant factoring in design to achieve flexibility. Compromises must be made, and there is an architectural decision about how far to do so. Fortunately, this can be taken to different degrees in different parts of the design see Pattern 6.2, The Golden Rule versus Other Optimizations. 

Pattern 6.2 The Golden Rule versus Other Optimizations... 

Several other optimizations, such as concentration of SDS in run buffer, separation voltage, detection voltage can be performed. Examples of such optimizations can be found in Refs. [2-6]. 

A number of other optimization strategies will be discussed later in this chapter. The savings resulting from replacing the letdown valves with expander turbine generators will be discussed in Section 2.13. The optimization of multistage chillers will be covered in connection with hydrogen liquefaction in Section 2.15.3, and coolant distribution controls will be covered under pump optimization in Section 2.17.2. 

In section 5.7 these characteristics will be compared with those of the other optimization procedures described in this chapter. 

Variation and selection turns out to be an enormously potent tool for improvement also in vitro. Why this is so, does not trivially follow from the nature of random searches. The efficiency of Monte-Carlo methods may work very poorly as we know from other optimization problems. The intrinsic regularities of genotype-phenotype mappings with high degrees of neutrality and very wide scatter of the points in sequence space, which lead to the same or very similar solutions, are the clues to evolutionary success. 

Other optimization paths are possible. One can think that a covering of the exponential product e b niay be better achieved by a simultaneous n-dimensional optimization process like Newton-Raphson procedure [58]. In fact, however, the overlap as described in equation (4.19) is an irregular function of the p,e parameter pairs, and possesses multiple maxima and minima with respect to the scale factors variation. A method with a scanning or sweep feature among a given parameter range will be perhaps superior in this case than a global search procedure, and so was adopted. 

Unlike the other optimization methods described here, the sequential simplex method for optimization neither assumes nor determines a mathematical model for the phenomena studied. 

The computational approach described here, based on the combination of the Kalman filter algorithm and iterative optimization by the simulated annealing method, was able to find the optimal alignment of the pure component peaks with respect to the shifted components in the overlapped spectra, and hence, to correctly estimate the contributions of each component in the mixture. The simulated annealing demonstrated superior ability over the other optimization methods, simplex and steepest descent, in yielding more reliable convergences at the expense of not much more computer time, at least for resolving ternary shifted overlapped spectra. 

Because they employ a random search technique, OSA routines do not require (or deduce) any functional derivative information. Thus, they are unaffected by the type of nonlinearities that cause problems to other optimization routines. 

WEXPRED calculates weighted (1/square root y) sum of squared deviations for fitting % pharmacokinetic data (biexp) to a four parameter, biexponential decay model. This % allows demonstration of non-linear regression by simplex, simulated annealing, or other % optimization techniques. 

Lineweaver-Burk plots was 12.5 tmol/L 18 ftmol/L of HDI-laurate (2.0 mmol/L as DMF solution) was selected as a saturating concentration in the reaction mixture. The other optimization results for both hydrolysis and CL reactions are described in METHODS . 

The response surface methodology approach has many advantages over other optimization procedures. These are listed in Table 2. 

Efforts are being made to simplify the workups and the recovery of the chiral ligands for recycling by the use of polymeric reagents. In some cases this works well, but in others, optimization is needed to make the results as good as those in solution. Asymmetrical protonation with a polymer-supported chiral proton donor (10.60) has given better results than the comparable reaction in solution.137 The... 

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