Abstract. We present a new derivation of efficient algorithms for a class of optimization problems called maximum marking problems. We extend the class of weight functions used in the specification to allow for weight functions with accumulation, which is particularly useful when the weight of each element depends on adjacent elements. This extension of weight functions enables us to treat more interesting optimization problems such as a variant of the maximum segment sum problem and the fair bonus distribution problem. The complexity of the derived algorithm is linear with respect to the size of the input data.

Program generation has seen an important role in a wide range of software development processes, where effective calculation rules are critical. In this paper, we propose a more general calculation rule for generation of efficient programs for solving maximum marking problems. Easy to use and implement, our new rule gives a significant extension of the rule proposed by Sasano et al., allowing multiple kinds of marks as well as more general description of the property of acceptable markings. We illustrate its effectiveness using several interesting problems.

In existing work on graph algorithms, it is known that a linear time algorithm can be derived mechanically from a logical formula for a class of optimization problems. But this has a serious problem that the derived algorithm has huge constant factor. In this work, we redene this problem on recursive data structures as a maximum marking problem and propose method for deriving a linear time algorithm for that. In this method, speci cation is given using recursive functions instead of logical formula, which results in a practical linear time algorithm. This method is mechanical and in fact, based on this deriving method, we make a system which automatically generates a practical linear time algorithm from specication for a maximum marking problem.

It is well-known that a naive algorithm can often be turned into an efficient program by applying appropriate semanticspreserving transformations. This technique has been used to derive programs to solve a variety of maximum-sum programs. One problem with this approach is that each problem variation requires a new set of transformations to be derived. An alternative approach to synthesis combines problem specifications with flexible algorithm theories to derive efficient algorithms. We show how this approach can be implemented in Haskell and applied to solve constraint satisfaction problems. We illustrate this technique by deriving programs for three varieties of maximum-weightsum problem. The derivations of the different programs are similar, and the resulting programs are asymptotically faster in practice than the programs created by transformation. 1.

scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder. Generator-based GG Fortress Library —Collection of GGs and Theories—

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scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder. An Algebraic Approach to Efficient Parallel

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