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Filter readers, just like filter input streams, provide additional functionality in the form of new methods, or process data in a different way (such as buffering) Generally, but not always, they are extended from the javaio FilterReader class, and always connect to another reader

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Solution solve(Problem P) { if (baseCase(P)) return baseSolve(P); else { Problem subProblems[N]; Solution subSolutions[N]; subProblems = split(P); for (int i = 0; i < N; i++) subSolutions[i] = solve(subProblems[i]); return merge(subSolutions); } }

Filters may even be chained together (for example, connecting a BufferedReader to a custom reader, or vice versa) Some useful filter readers are listed in Table 4-6 Table 4-6 Useful Filter Readers of the javaio Package Purpose of Stream BufferedReader Buffers access to data, to improve efficiency FilterReader Provides a class to extend when creating filters PushBackReader Allows text data to be pushed back into the reader's stream LineNumberReader Buffered reader subclass, which maintains a count of which line it is on 4451 BufferedReader One of the most frustrating problems with reading data from a writer, as with an input stream, is that blocking I/O is used When attempting to read, the application will block until data is available This can last for a few milliseconds, a few seconds, or even worse, a few minutes When this happens frequently, the performance and responsiveness of software suffers An alternative is to buffer data so that reads are grouped together for better performance Just as the BufferedInputStream buffers bytes of data, the BufferedReader buffers characters Also, although one would not guess it from the name, the BufferedReader is a partial substitute for the DataInputStream class; it provides a readLine() method that is not deprecated Constructors

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Solution A sequential divide and conquer algorithm has the structure shown in Fig 46 The cornerstone of this structure is a recursively invoked function (solve ()) that drives each stage in the solution Inside solve, the problem is either split into smaller subproblems (using split()) or it is directly solved (using baseSolve()) In the classical strategy, recursion continues until the subproblems are simple enough to be solved directly, often with just a few lines of code each However, efficiency can be improved by adopting the view that baseSolve() should be called when (1) the overhead of performing further splits and merges significantly degrades performance, or (2) the size of the problem is optimal for the target system (for example, when the data required for a baseSolve() fits entirely in cache) The concurrency in a divide and conquer problem is obvious when, as is usually the case, the subproblems can be solved independently (and hence, concurrently) The sequential divide and conquer algorithm maps directly onto a task parallel algorithm by defining one task for each invocation of the solve() function, as illustrated in Fig 47 Note the recursive nature of the design, with each task in effect dynamically generating and then absorbing a task for each subproblem

Mere padding works perfectly well for positive integers, but it does not solve the sorting problem for negative numbersWith negative numbers, a higher number represents a lower valueThe number -5 is numerically less than -1, which is exactly backward from lexicographical orderThe solution is to add an offset value to every number before you zero-pad and store itThe offset must be large enough to push all possible values out of the negativeThe lowest possible value for a 32-bit integer is -2147483648, so the smallest possible offset is 2147483648The smallest 64-bit offset is 9223372036854775808 Using the minimum offset, the examples of large and small 32-bit integers become the following:

BufferedReader (Reader reader) reads data from the specified reader into a BufferedReader (Reader reader, int bufferSize) throws javalang IllegalArgumentException reads data from the specified reader into a buffer,

A reduction operation reduces a collection of data items to a single data item by repeatedly combining

The following public method is added by BufferedReader, as a replacement for the deprecated DataInputStreamreadLine() method In addition, the reader overrides the markSupported() method, to indicate that it supports the mark and reset operations

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underlying stream The line is terminated by a line separator sequence, such as a carriage return/linefeed

the data items pairwise with a binary operator, usually one that is associative and commutative Examples of reductions include finding the sum, product, or maximum of all elements in an array In general, we can represent such an operation as the calculation of Equation 61

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