LAWS@SRI-AI.ARPA (11/22/84)
From: AIList Moderator Kenneth Laws <AIList-REQUEST@SRI-AI> AIList Digest Wednesday, 21 Nov 1984 Volume 2 : Issue 158 Today's Topics: LISP - Public-Domain LISP & Lisp Performance Tools, Expert Systems - Paradocs, Algorithms & Theorem Proving - Karmarkar' Linear Programming Algorithm, Seminars - Solving Problems in Equational Theories & The Grand Tour (in Pattern Recognition) ---------------------------------------------------------------------- Date: 19-Nov-84 10:11:58-PST From: mkm@FORD-WDL1.ARPA Subject: Public-Domain LISP? Is there a public domain copy of a LISP interpreter running around out there? If so, I would like to know where, how to get it, etc. Thanks, Mike McNair Ford Aerospace ------------------------------ Date: Monday, 19 Nov 1984 09:39:20-PST From: cashman%what.DEC@decwrl.ARPA Subject: Lisp performance tools I am interested in pointers to any tools which have been developed for measuring the performance of Lisp application programs (not measuring the performance of Lisp systems themselves). Paul Cashman (Cashman%what.DEC@DECWRL) ------------------------------ Date: Mon 19 Nov 84 18:25:33-PST From: Joe Karnicky <KARNICKY@SU-SCORE.ARPA> Subject: Paradocs Expert System Has anyone out there heard of/seen/used a software system called Paradocs? It is marketed by a company that has undergone several name changes and is currently known as Cogensys (out of Farmington Hills, Mich.). The system is described as a "judgement processing system" and is represented as being able to combine inputs from several domain experts into a judgement base which is then able to diagnose problems in the domain. The name of the fellow who created the system is Buzz Berk (spelling uncertain). I'd very much appreciate ***any*** information and/or opinions about the value or performance of this system. Sincerely, Joe Karnicky <KARNICKY@SCORE> ------------------------------ Date: 19 Nov 1984 1421-EST From: Venkat Venkatasubramanian <VENKAT@CMU-CS-C.ARPA> Subject: Karmarkar [Forwarded from the CMU bboard by Laws@SRI-AI.] There is a front page article in today's NY Times on Karmarkar and his linear programming algorithm. ------------------------------ Date: 19 Nov 84 1204 PST From: Martin Frost <ME@SU-AI.ARPA> Subject: linear programming "breakthrough" [Excerpted from the Stanford bboard by Laws@SRI-AI.] 18 Nov 84 By JAMES GLEICK c.1984 N.Y. Times News Service NEW YORK - A 28-year-old mathematician at AT&T Bell Laboratories has made a startling theoretical breakthrough in the solving of [linear programming] systems of equations. [...] The Bell Labs mathematician, Dr. Narendra Karmarkar, has devised a radically new procedure. [...] The new Karmarkar approach exists so far only in rougher computer code. Its full value will be impossible to judge until it has been tested experimentally on a wide range of problems. But those who have tested the early versions at Bell Labs say that it already appears many times faster than the simplex method, and the advantage grows rapidly with more complicated problems. [...] Karmarkar, the son and nephew of mathematicians, was born in Gwalior, India, and grew up in Poona, near Bombay. He joined Bell Labs last year after attending the California Institute of Technology at Pasadena and getting his doctorate from the University of California at Berkeley. News of his discovery has been spreading through the computer science community in preprinted copies of Karmarkar's paper and in informal seminars. His paper is to be formally published in the journal Combinatorica next month and will be a central topic at the yearly meeting of the Operations Research Society of America this week in Dallas. [...] Mathematicians visualize such problems as complex geometric solids with millions or billions of facets. Each corner of each facet represents a possible solution. The task of the algorithm is to find the best solution, say the corner at the top, without having to calculate the location of every one. The simplex method, devised by the mathematician George B. Dantzig in 1947, in effect runs along the edges of the solid, checking one corner after another but always heading in the direction of the best solution. In practice it usually manages to get there efficiently enough for most problems, as long as the number of variables is no more than 15,000 or 20,000. The Karmarkar algorithm, by contrast, takes a giant short cut, plunging through the middle of the solid. After selecting an arbitrary interior point, the algorithm warps the entire structure - in essence, reshaping the problem - in a way designed to bring the chosen point exactly into the center. The next step is to find a new point in the direction of the best solution and to warp the structure again, bringing the new point into the center. ''Unless you do this warping,'' Karmarkar said, ''the direction that appears to give the best improvement each time is an illusion.'' The repeated transformations, based on a technique known as projective geometry, lead rapidly to the best answer. Computer scientists who have examined the method describe it as ingenious. ''It is very new and surprising - it has more than one theoretical novelty,'' said Laszlo Babai, visiting professor of computer science at the University of Chicago. ''The real surprise is that the two things came together, the theoretical breakthrough and the practical applicability.'' Dantzig, now professor of operations research and computer science at Stanford University, cautioned that it was too early to assess fully the usefulness of the Karmarkar method. ''We have to separate theory from practice,'' he said. ''It is a remarkable theoretical result and it has a lot of promise in it, but the results are not all in yet.'' Many mathematicians interested in the theory of computer science have long been dissatisfied with the simplex method, despite its enormous practical success. This is because the program performs poorly on problems designed specificaly to test its weaknesses, so-called worst possible case problems. [...] But fortunately for computer science, the worst-case problems almost never arise in the real world. ''You had to work hard to produce these examples,'' Graham said. And the simplex method performs far better on average than its worst-case limit would suggest. Five years ago, a group of Soviet mathematicians devised a new algorithm, the ellipsoid method, that handled those worst-case problems far better than the simplex method. It was a theoretical advance - but the ellipsoid had little practical significance because its average performance was not much better than its worst-case performance. The Soviet discovery, however, stimulated a burst of activity on the problem and led to Karmarkar's breakthrough. The new algorithm does far better in the worst case, and the improvement appears to apply as well to the kinds of problems of most interest to industry. ''For a long time the mind-set that the simplex method was the way to do things may have blocked other methods from being tested,'' said Dr. Richard Karp, professor of computer science at the University of California at Berkeley. ''It comes as a big surprise that what might have been just a curiosity, like the ellipsoid, turns out to have such practical importance.'' ------------------------------ Date: Tue 20 Nov 84 14:15:01-PST From: John B. Nagle <NAGLE@SU-SCORE.ARPA> Subject: Re: new linear programming algorithm [Forwarded from the Stanford bboard by Laws@SRI-AI.] This may have implications for automatic theorem proving. Since Dick Karp is working on it, they will probably be explored. We may get some really high performance verification techniques someday. ------------------------------ Date: Tue, 20 Nov 84 16:17:41 pst From: Vaughan Pratt <pratt@Navajo> Subject: new linear programming algorithm and automatic theorem proving. [Forwarded from the Stanford bboard by Laws@SRI-AI.] As applied to program verification, the typical linear programming problems that arise are almost all of the form, find any integer solution to a set of inequations of the form x+c<y where x and y are variables and c is an integer constant. (Different inequations are allowed different choices of the two variables, i.e. there are more than two variables in the system as a whole.) There is a simple algorithm for solving these (in either integers or reals) having worst case O(n**3) (essentially Floyd's algorithm for all shortest paths). The sets tend to be sparse, which can be taken advantage of to get better than n**3 performance. The implementation is simple and the constant factor is small. The reason this form crops up is that it is an alternative representation for the inequational theory of successor and predecessor, which in turn crops up since most arithmetic occurring in programs consists of subscript incrementing and decrementing and checking against bounds. Programs whose arithmetic goes beyond this theory also tend to go beyond the theory of + and - by having * and / as well, i.e. the fraction of programs covered by linear programming but not by the above trivial fragment of it is not that large. -v ------------------------------ Date: Tue, 20 Nov 84 17:26:48 pst From: Moshe Vardi <vardi@diablo> Subject: New Algorith for Linear Programming [Forwarded from the Stanford bboard by Laws@SRI-AI.] A preliminary report appeared in the proceedings of the last ACM Symp. on Theory of Computing. It is a provably polynomial time algorithm, which unlike Khachian's algorithm is a practical one. There are doubts among the experts whether the algorithm is as revolutionary as the PR people say it is. Moshe ------------------------------ Date: Sat 17 Nov 84 17:58:32-PST From: Ole Lehrmann Madsen <MADSEN@SU-CSLI.ARPA> Subject: Seminar - Solving Problems in Equational Theories [Forwarded from the Stanford bboard by Laws@SRI-AI.] CENTER FOR THE STUDY OF LANGUAGE AND INFORMATION AREA C MEETING Topic: REVE: a system for solving problems in equational theories, based on term rewriting techniques. Speaker: Jean-Pierre Jouannaud, Professor at University of NANCY, FRANCE, on leave at SRI-Internatinal and CSLI. Time: 1:30-3:30 Date: Wednesday, Nov. 21 Place: Ventura Seminar Room Equational Logic has been adopted by mathematicians for a very long time and by computer scientists recently. Specifications in OBJ2, an "object-oriented" language designed and implemented at SRI-International, uses equations to express relations between objects. To express computations in this logic, equations are used one way, e.g. as rules. To make proofs with rules in this logic requires the so-called "confluence" property, which expresses that the result of a computation is unique, no matter the order the rules are applied. Proofs and computations are therefore integrated in a very simple framework. When a set of rules does not have the confluence property, it is augmented by new rules, using the so-called Knuth and Bendix completion algorithm, until the property becomes satisfied. This algorithm requires the set of rules to have the termination property, i.e., an expression cannot be rewritten forever. It has been proved that this algorithm allows to perform inductive proof without invoking explicitly an induction principle and solve equations (unification) in the corresponding equational theory as well. REVE1, developped by Pierre Lescanne during a leave at MIT, implements all these concepts, including automated proofs for termination. REVE2, developped by Randy Forgaard at MIT, provided REVE with a very sophisticated user interface, including an undo command. REVE3, developped by Claude and Helene Kirchner in NANCY, includes new powerful features, mainly mixted sets of rules and equations for handling theories including permutative axioms. All versions are developped in CLU and run on VAX under UNIX-Berkeley. ------------------------------ Date: Tue 20 Nov 84 22:59:35-PST From: Art Owen <OWEN@SU-SCORE.ARPA> Subject: Seminar - The Grand Tour [Forwarded from the Stanford bboard by Laws@SRI-AI.] Laboratory for Computational Statistics Seminar Time: 3:15pm Wednesday November 21st Place: Sequoia Hall 114 Cookies: at 3:00 in the Lounge Title: Hopes for the Grand Tour by Daniel Asimov Dept. of Computer Science U.C. Berkeley The grand tour is a technique for examining two dimensional projections of higher dimensional objects. The tour essentially picks a trajectory through the space of possible projections, while a data analyst watches the corresponding 'movie' on a graphics terminal. The objective is to as quickly as possible pass near to most of the possible projections. It is a tool for finding projections that are informative. The talk will discuss the current state of grand tour research, identifying desirable properties that a tour might have, indicating which such properties have been achieved and directions for future research. That's 3:00, 21 Nov 84, Sequoia Hall 114 ------------------------------ End of AIList Digest ********************