By Tsutomu Kamimura (Editor)
This serial is a translation of the unique works in the Japan Society of software program technology and Technology. A key resource of knowledge for desktop scientists within the united states, the serial explores the foremost parts of analysis in software program and expertise in Japan. those volumes are meant to advertise around the globe trade of rules between professionals.This quantity comprises unique learn contributions in such components as Augmented Language common sense (ALL), disbursed interval, Smalltalk eighty, and TAMPOPO-an evolutionary studying computer in response to the foundations of Realtime minimal Skyline Detection
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Extra info for Advances in Software Science and Technology, Volume 5
We have thus obtained an efficient KMP algorithm by simply applying our fully lazy partial evaluator to the refined program. The residual program consists of some hundred lines in Haskell representation. What we have to do is to eliminate redundant parameters that hinder fully lazy evaluation. It is an easy task to locate such parameters. Derivation of a Knuth-Morris-Pratt Algorithm by Fully Lazy Partial Computation 21 kmp p* d* = let start d - cond (null d) False (restart d) — Depends solely on d restart d = cond (head p' == head d) (cond (null(tail p')) True (cond (null(tail d)) False (loop (tail p O (tail d)))) (start (tail d)) loop p d = cond (head p == head d) (cond (null(tail p)) True (cond (null(tail d)) False (loop (tail p) (tail d)))) (let np=static_kmp (tail p') (length(tail p')-(length p)) in (cond (np==p') (cond (head p* == head p) (start (tail d)) (restart d)) (loop np d)) ) static_kmp d n = static_loop p' d d n n static_loop p d d' n n* = cond (n==0) (cond (n'==0) p (cond (head p /= head d) p (static_kmp (tail d>) (n'-l)))) (cond (head p == head d) (staticloop (tail p) (tail d) d' (n-1) n O (static_kmp (tail d') (n'-l))) in cond (null p') True (start d') Fig.
2 i · · Vn · * Π) define a new predicate ck as in ck(V\, V 2 , . . ,Vn). , unfold it). (3) Folding If a constraint that has already been modularized appears as a substructure of the target constraint, replace the substructure with the predicate that is the previously modularized result. Fig. , constraint append(C,D,E). @ append(A,B,C), can be modularized defined The Modularization Algorithm by two unfoldings (Figure 6). For further details, cO(A,B,C,D,E) <==> a p p e n d ( A , B , C ) , a p p e n d ( C , D , E ) .
Lambda-Hoisting: A Transformation Technique for Fully Lazy Evaluation of Functional Programs, New Generation Computing, Vol. 5 (1988), pp. 377-391. Received August 1992 Keiichi Kaneko Department of Mathematical Engineering and Information Physics Faculty of Engineering The University of Tokyo Hongo, Bunkyo-ku, Tokyo 113 Japan Masato Takeichi Department of Mathematical Engineering and Information Physics Faculty of Engineering The University of Tokyo Hongo, Bunkyo-ku, Tokyo 113 Japan Computability of Modularization of Constraints Yutaka Tomioka Summary.