This part will get you started in thinking about designing and analyzing algorithms. It is intended to be a gentle introduction to how we specify algorithms, some of the design strategies we will use throughout this book, and many of the fundamental ideas used in algorithm analysis. Later parts of this book will build upon this base. Chapter 1 is an overview of algorithms and their place in modern computing systems. This chapter defines what an algorithm is and lists some examples. It also makes a case that algorithms are a technology, just as are fast hardware, graphical user interfaces, object-oriented systems, and networks. In Chapter 2, we see our first algorithms, which solve the problem of sorting a sequence of n numbers. They are written in a pseudocode which, although not directly translatable to any conventional programming language, conveys the structure of the algorithm clearly enough that a competent programmer can implement it in the language of his choice. The sorting algorithms we examine are insertion sort, which uses an incremental approach, and merge sort, which uses a recursive technique known as “divide and conquer.” Although the time each requires increases with the value of n, the rate of increase differs between the two algorithms. We determine these running times in Chapter 2, and we develop a useful notation to express them. Chapter 3 precisely defines this notation, which we call asymptotic notation. It starts by defining several asymptotic notations, which we use for bounding algorithm running times from above and/or below. The rest of Chapter 3 is primarily a presentation of mathematical notation. Its purpose is more to ensure that your use of notation matches that in this book than to teach you new mathematical concepts.

Nearest neighbor searching is an important geometric subproblem in vector quantization.

. A priority queue Q is a data structure that maintains a collection of elements, each element having an associated priority drawn from a totally ordered universe, under the operations Insert, which inserts an element into Q, and DeleteMin, which deletes an element with the minimum priority from Q. In this paper a priority-queue implementation is given which is efficient with respect to the number of block transfers or I/Os performed between the internal and external memories of a computer. Let B and M denote the respective capacity of a block and the internal memory measured in elements. The developed data structure handles any intermixed sequence of Insert and DeleteMin operations such that in every disjoint interval of B consecutive priorityqueue operations at most c log M=B N M I/Os are performed, for some positive constant c. These I/Os are divided evenly among the operations: if B c log M=B N M , one I/O is necessary for every B=(c log M=B N M )th operation ...

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A linear time algorithm is presented for finding dominators in control flow graphs.

The issue of temporal fairness in periodic real-time scheduling is considered. It is argued that such fairness is often a desirable characteristic in real-time schedules. A concrete criterion for temporal fairness -- pfairness -- is described. The weight-monotonic scheduling algorithm, a static priority scheduling algorithm for generating pfair schedules, is presented and proven correct. A feasibility test is presented which, if satisfied by a system of periodic tasks, ensures that the weight-monotonic scheduling algorithm will schedule the system in a pfair manner.

Proportionate fair (Pfair) scheduling is the only known way to optimally schedule periodic real-time task systems on multiprocessors in an on-line manner. Under Pfair scheduling, the execution of each task is broken into a sequence of quantum-length subtasks that must execute within intervals of approximately-equal lengths. This scheduling policy results in allocations that mimic those of an ideal “fluid ” scheduler, and in periodic task systems, ensures that all deadlines are met. Though Pfair scheduling algorithms hold much promise, prior to our work, research on this topic was limited in that only static systems consisting of synchronous periodic tasks were considered. My dissertation thesis is that the Pfair scheduling framework for the on-line scheduling of real-time tasks on multiprocessors can be made more flexible by allowing the underlying task model to be more general than the periodic model and by allowing dynamic task behaviors. Further, this flexibility can be efficiently achieved. Towards the goal of improving the efficiency of Pfair scheduling algorithms, we develop the PD 2 Pfair algorithm, which is the most efficient optimal Pfair scheduling algorithm devised to date. Through a series of counterexamples, we show that it is

A pair in a string is the occurrence of the same substring twice. A pair is maximal if the two occurrences of the substring cannot be extended to the left and right without making them different. The gap of a pair is the number of characters between the two occurrences of the substring. In this paper we present methods for finding all maximal pairs under various constraints on the gap. In a string of length n we can find all maximal pairs with gap in an upper and lower bounded interval in time O(n log n + z) where z is the number of reported pairs. If the upper bound is removed the time reduces to O(n+z). Since a tandem repeat is a pair where the gap is zero, our methods can be seen as a generalization of finding tandem repeats. The running time of our methods equals the running time of well known methods for finding tandem repeats.

We present an efficient algorithm which can check the answers given by the fundamental abstract data types priority queues and mergeable priority queues. This is the first linear-time checker for mergeable priority queues. These abstract data types are widely used in routing, scheduling, simulation, computational geometry and many other algorithmic domains. We have implemented our answer checker and have performed experiments comparing the speed of our checker to recently benchmarked priority queue and mergeable priority queue implementations, and our checker is substantially faster than the best of these implementations. 1 Introduction This paper concerns the fundamental abstract data types of priority queues (PQs) and mergeable priority queues (MPQs). These abstract data types have been recognized as centrally important from the early days of computer-algorithm design. They appear in seminal algorithm texts such as Knuth's [10] and Aho, Hopcroft and Ullman's [1]. Data structure impl...

Abstract. We construct a readable, compact and efficient implementation of Dijkstra’s shortest path algorithm and Fibonacci heaps using Constraint Handling Rules (CHR), which is increasingly used as a high-level rule-based general-purpose programming language. We measure its performance in different CHR systems, investigating both the theoretical asymptotic complexity and the constant factors realized in practice. 1