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In light of the aforementioned complexity results, it is unlikely that exact solutions can be found for many counting problems of practical interest This has motivated the development of ef cient approximation algorithms for counting problems together with the theoretical tools required not just to quantify the approximation error, but to do so as a function of computational complexity In other words, we seek approximation schemes which use the smallest amount of computation both in space and time in order to achieve a desired relative precision with 1 con dence This notion can be and has been formalized both in the theoretical computer science and rare event simulation communities It is the purpose of this section to explain these formalisms and show their correspondence precisely Most of the successful approximation algorithms known for #P problems, with the notable exception of correlation-decay-based methods [11, 28], are randomized schemes Suppose we are interested in a counting problem : N In other words, we get a problem instance x (eg, a particular bipartite graph) and are interested in estimating the output of (x) using some estimator (x) Analysis of the behavior of such an estimator rests on the following de nition (cf [21, p 254]) De nition 3 Fully-Polynomial Randomized Approximation Scheme (FPRAS) A randomized approximation scheme is a randomized algorithm such that when given a problem instance x together with error and con dence parameters 0 < , < 1, the output (x) has the following property for all x: Pr[(1 ) (x) (x) (1 + ) (x)] 1 If the algorithm runs in time polynomial in |x|, 1 , and log(1/ ), then it is referred to as a fully polynomial randomized approximation scheme (FPRAS) Here the quantity |x| is a measure of the size of the problem instance whose particular form is informed by the precise asymptotic regime that we may wish to study For example, when working with binary contingency tables it is convenient to think of |x| as a function of d, the number of ones in the table (ie, |x| = O(d log d)) This allows us to study the ef ciency of the approximation scheme asymptotically in d (ie, as the size of the problem grows large) In particular, we adopt the notation d to underscore the fact that we are interested in studying the behavior of the estimators as a function of the input size Given a speci c counting problem, we suppress the explicit dependence on the encoding x and consider the family of estimators { d : d 1} indexed implicitly as a function of |x| through the parameter d Note that some authors do not stress the dependence of order log(1/ ) and instead appear to be satis ed with a dependence of order O(1/ ) in their de nition (cf [13, pp 25 27]) We now turn our attention to concepts of ef ciency that often appear in the rare event simulation literature In this setting, it is common to work with a family of estimators d,k : d 1 Here, the explicit dependence on the problem.

java data matrix barcode reader

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In the previous section, we looked at an example where the method we wanted to set a breakpoint on was not yet JIT compiled Let s take a look at the same example, but this time we will precompile the assembly first and see what differences we can see The application we will use is called 03ObjTypesexe (refer to Listing 3-10) Before we can use it for this exercise, we first need to go through the process of precompiling it:

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java data matrix barcode reader

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After it has been precompiled, we go ahead and launch the application under the debugger The initial debugger spew looks like the following:

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java data matrix reader

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java data matrix barcode reader

java data matrix barcode reader

java data matrix barcode reader

java data matrix reader

java data matrix barcode reader

java data matrix barcode reader

java data matrix reader

java data matrix reader

java data matrix barcode reader

java data matrix barcode reader