Towards Smart Proof Search for Isabelle PSL and all that
|
|
- Abraham Thornton
- 5 years ago
- Views:
Transcription
1 formerly known as NICTA Towards Smart Proof Search for Isabelle PSL and all that Yutaka Nagashima Trustworthy System Research Group March 2017 until last week
2 Example proof at Data61 Click to edit Master text styles Second level Third level Fourth level Fifth level taken from: 2 Presentation title Presenter name
3 PSL and try-hard for Isabelle/HOL The percentage of automatically proved obligations out of 1526 proof obligations (timeout = 300s) 100% 75% 50% 25% 73% Part 1 28% Part 2 20% 16% 57% Not specific to Isabelle! Other ITPs / Logic Programming 0% try_hard sledgehammer 3
4 Isabelle/HOL before PSL proof context tactic / sub-tool 4 error-message It's blatantly clear You stupid machine, subs that what I tell you is true (Michael Norrish) no sub-!
5 PSL (Proof Strategy Language) programming language extensible (Eisbach) extensive proof search low memory usage meta-tool approach parallel search PSL tactics sledgehammer almost no code clutter!! quickcheck runtime tactic generation efficient proof generation native Isabelle proof script 5
6 Isabelle/HOL with PSL context proof strategy proof context tactic / sub-tool PSL efficient tactic proved theorem / subs / message Much less interaction with Isabelle. 6
7 Tactics 1 preproces imp principle of explosion tactic False imp P Case 1 Case 2 new imp Case 3 sub 1 imp sub 2 imp imp 7 PSL and all that. Yutaka Nagashima
8 ,, [ ] Tactics 2 preproces Case 1 new Case 3 sub 1 imp imp tactic : thm Case 2 imp sub 2 imp imp 8 PSL and all that. Yutaka Nagashima
9 Tactics 2 preproces imp [ ] Case 4 (failure = empty list) tactic 9 PSL and all that. Yutaka Nagashima
10 Tactics 3 [,, ] :: thm tactic 1:: thm 2 :: thm fun tactic :: thm -> [ thm ] Lazy simp OR auto auto simp induct REPEAT simp induct THEN auto 10 PSL and all that. Yutaka Nagashima
11 Tactical (THEN) :: thm tactic1 THEN tactic2 tactic1 [,, ] 1 2 giant tactic? tactic2 tactic2 tactic2 [, ] , ]@[ 11
12 Giant tactic giant tactic? simp OR fast OR force OR auto problem 1: Default tactics are too weak! problem 2: Giant tactics are too slow! problem 3: Sledgehammer and quick-check are not tactics! 12
13 Thens [Dynamic(Induct), Auto, IsSolved] runtime interpretation (InductA ++ InductB ++ ) THEN auto THEN is_solved Dynamic ( Induct ) Auto sequential combination (THEN) IsSolved 13 Towards Smart Proof Search. non-determinism Yutaka Nagashima
14 Monadic interpretation type tactic = thm -> thm Seq.seq type a tactic = a -> a monad explicit tree construction? pointer? Dynamic ( Induct ) writer monad + non-deterministic monad Auto IsSolved efficient proof scripts as state 14
15 Sledgehammer as tactic problem 3: Sledgehammer and quick-check are not tactics! They work on Proof.state not on thm. type a tactic = 'a -> a nondet_state_monad type tactic = P.state -> P.state nondet_state_monad parallel persistant hammering PThenOne Thens [Dyn (Induct), Thens[Hammer+, IsSolved]] 15
16 try_hard: the default strategy strategy Basic = Ors [ Auto_Solve, Blast_Solve, FF_Solve, Thens [IntroClasses, Auto_Solve], Thens [Transfer, Auto_Solve], Thens [Normalization, IsSolved], Thens [DInduct, Auto_Solve], Thens [Hammer, IsSolved], Thens [DCases, Auto_Solve], Thens [DCoinduction, Auto_Solve], Thens [Auto, RepeatN(Hammer), IsSolved], Thens [DAuto, IsSolved]] strategy Try_Hard = Ors [Thens [Sub, Basic], Thens [DInductTac, Auto_Solve], Thens [DCaseTac, Auto_Solve], Thens [Sub, Advanced], Thens [DCaseTac, Solve_Many], Thens [DInductTac, Solve_Many] ] 16 PSL and all that. Yutaka Nagashima
17 PSL: Demo
18 PSL and try-hard for Isabelle/HOL The percentage of automatically proved obligations out of 1526 proof obligations (timeout = 300s) 100% 75% try_smart 73% 28% Part 2 50% Part 1 20% 16% 57% 25% 0% try_hard sledgehammer 18
19 PaMpeR: Proof Method Recommendation System huge and complex proof context strategy proof and context as a vector of boolean values context proof assertions? 19 proof method recommendation:: (proof method * double) list PaMpeR Regression Algorithm Type class mechanism? Recursively defined constant? Proof Data Base e.g. AFP & sel4
20 PaMpeR: Demo Affine_Arithmetic/Affine_Approximation
21 Future work: try-hard to try-smart PaMpeR proof context proof context small strategy proof context tactic / sub-tool try_smart PSL efficient tactic state monad transformer runtime tactic generation 21 Even better than PSL.
22 Isabelle/PSL on Github ( Leave a star if you like. I want you to use PSL / adopt the idea Isabelle/PaMpeR on Github (still work in progress) Lean/PSL coming soon(?) 22
23 Thank You TS/ProofEngineering Yutaka Nagashima Engineer
Fibonacci Heaps Y Y o o u u c c an an s s u u b b m miitt P P ro ro b blle e m m S S et et 3 3 iin n t t h h e e b b o o x x u u p p fro fro n n tt..
Fibonacci Heaps You You can can submit submit Problem Problem Set Set 3 in in the the box box up up front. front. Outline for Today Review from Last Time Quick refresher on binomial heaps and lazy binomial
More informationHW 1 Reminder. Principles of Programming Languages. Lets try another proof. Induction. Induction on Derivations. CSE 230: Winter 2007
CSE 230: Winter 2007 Principles of Programming Languages Lecture 4: Induction, Small-Step Semantics HW 1 Reminder Due next Tue Instructions about turning in code to follow Send me mail if you have issues
More informationCS 188: Artificial Intelligence
CS 188: Artificial Intelligence Markov Decision Processes Dan Klein, Pieter Abbeel University of California, Berkeley Non Deterministic Search Example: Grid World A maze like problem The agent lives in
More informationTEST 1 SOLUTIONS MATH 1002
October 17, 2014 1 TEST 1 SOLUTIONS MATH 1002 1. Indicate whether each it below exists or does not exist. If the it exists then write what it is. No proofs are required. For example, 1 n exists and is
More informationFrom Concurrent Programs to Simulating Sequential Programs: Correctness of a Transformation
From Concurrent s to Simulating Sequential s: Correctness of a Transformation VPT 2017 Allan Blanchard, Frédéric Loulergue, Nikolai Kosmatov April 29 th, 2017 Table of Contents 1 From Concurrent s to Simulating
More informationStructural Induction
Structural Induction Jason Filippou CMSC250 @ UMCP 07-05-2016 Jason Filippou (CMSC250 @ UMCP) Structural Induction 07-05-2016 1 / 26 Outline 1 Recursively defined structures 2 Proofs Binary Trees Jason
More information4 Reinforcement Learning Basic Algorithms
Learning in Complex Systems Spring 2011 Lecture Notes Nahum Shimkin 4 Reinforcement Learning Basic Algorithms 4.1 Introduction RL methods essentially deal with the solution of (optimal) control problems
More informationAlgorithmic Game Theory and Applications. Lecture 11: Games of Perfect Information
Algorithmic Game Theory and Applications Lecture 11: Games of Perfect Information Kousha Etessami finite games of perfect information Recall, a perfect information (PI) game has only 1 node per information
More informationOutline for this Week
Binomial Heaps Outline for this Week Binomial Heaps (Today) A simple, flexible, and versatile priority queue. Lazy Binomial Heaps (Today) A powerful building block for designing advanced data structures.
More informationHarvard School of Engineering and Applied Sciences CS 152: Programming Languages
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 2 Thursday, January 30, 2014 1 Expressing Program Properties Now that we have defined our small-step operational
More informationMarkov Decision Processes
Markov Decision Processes Robert Platt Northeastern University Some images and slides are used from: 1. CS188 UC Berkeley 2. RN, AIMA Stochastic domains Image: Berkeley CS188 course notes (downloaded Summer
More informationOutline for this Week
Binomial Heaps Outline for this Week Binomial Heaps (Today) A simple, fexible, and versatile priority queue. Lazy Binomial Heaps (Today) A powerful building block for designing advanced data structures.
More informationCS 188: Artificial Intelligence
CS 188: Artificial Intelligence Markov Decision Processes Dan Klein, Pieter Abbeel University of California, Berkeley Non-Deterministic Search 1 Example: Grid World A maze-like problem The agent lives
More informationRecitation 1. Solving Recurrences. 1.1 Announcements. Welcome to 15210!
Recitation 1 Solving Recurrences 1.1 Announcements Welcome to 1510! The course website is http://www.cs.cmu.edu/ 1510/. It contains the syllabus, schedule, library documentation, staff contact information,
More informationHCI: CONTENT LAYOUT. Dr Kami Vaniea
HCI: CONTENT LAYOUT Dr Kami Vaniea 1 2 Affordance and Metaphores 3 Affordance An attribute of an object that allows people to know how to use it. -- ID book To afford means to give a clue It should be
More informationConditional Rewriting
Conditional Rewriting Bernhard Gramlich ISR 2009, Brasilia, Brazil, June 22-26, 2009 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26, 2009 1 Outline Introduction Basics in Conditional Rewriting
More informationAbstract stack machines for LL and LR parsing
Abstract stack machines for LL and LR parsing Hayo Thielecke August 13, 2015 Contents Introduction Background and preliminaries Parsing machines LL machine LL(1) machine LR machine Parsing and (non-)deterministic
More informationProof Techniques for Operational Semantics. Questions? Why Bother? Mathematical Induction Well-Founded Induction Structural Induction
Proof Techniques for Operational Semantics Announcements Homework 1 feedback/grades posted Homework 2 due tonight at 11:55pm Meeting 10, CSCI 5535, Spring 2010 2 Plan Questions? Why Bother? Mathematical
More informationThe Advanced Budget Project Part D The Budget Report
The Advanced Budget Project Part D The Budget Report A budget is probably the most important spreadsheet you can create. A good budget will keep you focused on your ultimate financial goal and help you
More informationAssociated Connect. Reference Guide: Quick Payments
Associated Connect Reference Guide: Quick Payments Page 2 of 14 Quick Payments Use the Quick Payments service to send, save and manage your ACH payments. Depending on your configuration, you can use Quick
More informationInstruction (Manual) Document
Instruction (Manual) Document This part should be filled by author before your submission. 1. Information about Author Your Surname Your First Name Your Country Your Email Address Your ID on our website
More informationHarvard School of Engineering and Applied Sciences CS 152: Programming Languages
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 3 Tuesday, January 30, 2018 1 Inductive sets Induction is an important concept in the theory of programming language.
More informationPractical SAT Solving
Practical SAT Solving Lecture 1 Carsten Sinz, Tomáš Balyo April 18, 2016 NSTITUTE FOR THEORETICAL COMPUTER SCIENCE KIT University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz
More information2) What is algorithm?
2) What is algorithm? Step by step procedure designed to perform an operation, and which (like a map or flowchart) will lead to the sought result if followed correctly. Algorithms have a definite beginning
More informationReinforcement Learning (1): Discrete MDP, Value Iteration, Policy Iteration
Reinforcement Learning (1): Discrete MDP, Value Iteration, Policy Iteration Piyush Rai CS5350/6350: Machine Learning November 29, 2011 Reinforcement Learning Supervised Learning: Uses explicit supervision
More informationReinforcement Learning (1): Discrete MDP, Value Iteration, Policy Iteration
Reinforcement Learning (1): Discrete MDP, Value Iteration, Policy Iteration Piyush Rai CS5350/6350: Machine Learning November 29, 2011 Reinforcement Learning Supervised Learning: Uses explicit supervision
More informationCompliance in the Collections Industry
Compliance in the Collections Industry Table of Contents Compliance in the Collections Industry...3 Understanding Unfair, Deceptive, or Abusive Acts or Practices (UDAAPs)...4 Fair Debt Collections Practices
More informationOnline Algorithms SS 2013
Faculty of Computer Science, Electrical Engineering and Mathematics Algorithms and Complexity research group Jun.-Prof. Dr. Alexander Skopalik Online Algorithms SS 2013 Summary of the lecture by Vanessa
More informationReinforcement Learning. Slides based on those used in Berkeley's AI class taught by Dan Klein
Reinforcement Learning Slides based on those used in Berkeley's AI class taught by Dan Klein Reinforcement Learning Basic idea: Receive feedback in the form of rewards Agent s utility is defined by the
More informationCSE 21 Winter 2016 Homework 6 Due: Wednesday, May 11, 2016 at 11:59pm. Instructions
CSE 1 Winter 016 Homework 6 Due: Wednesday, May 11, 016 at 11:59pm Instructions Homework should be done in groups of one to three people. You are free to change group members at any time throughout the
More informationInsurance Tracking with Advisors Assistant
Insurance Tracking with Advisors Assistant Client Marketing Systems, Inc. 880 Price Street Pismo Beach, CA 93449 800 643-4488 805 773-7985 fax www.advisorsassistant.com support@climark.com 2015 Client
More informationOutline for Today. Quick refresher on binomial heaps and lazy binomial heaps. An important operation in many graph algorithms.
Fibonacci Heaps Outline for Today Review from Last Time Quick refresher on binomial heaps and lazy binomial heaps. The Need for decrease-key An important operation in many graph algorithms. Fibonacci Heaps
More informationMICROSOFT DYNAMICS 365
USER GUIDE AMC BANKING 365 FINANCIALS SERVICE FOR MICROSOFT DYNAMICS 365 English edition AMC Consult A/S 12. June 2017 Version 0.71 Contents 1 Introduction... 3 2 AMC Banking 365 Financials Service Setup...
More informationDecision Trees: Booths
DECISION ANALYSIS Decision Trees: Booths Terri Donovan recorded: January, 2010 Hi. Tony has given you a challenge of setting up a spreadsheet, so you can really understand whether it s wiser to play in
More informationChapter 16. Binary Search Trees (BSTs)
Chapter 16 Binary Search Trees (BSTs) Search trees are tree-based data structures that can be used to store and search for items that satisfy a total order. There are many types of search trees designed
More informationInstruction (Manual) Document
Instruction (Manual) Document This part should be filled by author before your submission. 1. Information about Author Your Surname Your First Name Your Country Your Email Address Your ID on our website
More informationHarvard School of Engineering and Applied Sciences CS 152: Programming Languages
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 3 Tuesday, February 2, 2016 1 Inductive proofs, continued Last lecture we considered inductively defined sets, and
More informationReinforcement Learning
Reinforcement Learning Basic idea: Receive feedback in the form of rewards Agent s utility is defined by the reward function Must (learn to) act so as to maximize expected rewards Grid World The agent
More informationLecture 7: Bayesian approach to MAB - Gittins index
Advanced Topics in Machine Learning and Algorithmic Game Theory Lecture 7: Bayesian approach to MAB - Gittins index Lecturer: Yishay Mansour Scribe: Mariano Schain 7.1 Introduction In the Bayesian approach
More informationSetting Up and Assigning Bank Rec Groups
Setting Up and Assigning Bank Rec Groups Important Note Salary checks existing in the system before updating to version 2.1.763 or higher must be viewed and reconciled under All Bank Rec Groups in Bank
More informationMAC Learning Objectives. Learning Objectives (Cont.)
MAC 1140 Module 12 Introduction to Sequences, Counting, The Binomial Theorem, and Mathematical Induction Learning Objectives Upon completing this module, you should be able to 1. represent sequences. 2.
More informationChapter 5: Algorithms
Chapter 5: Algorithms Computer Science: An Overview Tenth Edition by J. Glenn Brookshear Presentation files modified by Farn Wang Copyright 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley
More informationNATIONAL UNIVERSITY OF SINGAPORE NUS Business School Department of Finance. BMA5324 Value Investing in Asia. Instructor: Robert Du
NATIONAL UNIVERSITY OF SINGAPORE NUS Business School Department of Finance BMA5324 Value Investing in Asia Instructor: Robert Du Robert is a doctoral candidate at Hong Kong Polytechnic University and is
More informationMaximum Contiguous Subsequences
Chapter 8 Maximum Contiguous Subsequences In this chapter, we consider a well-know problem and apply the algorithm-design techniques that we have learned thus far to this problem. While applying these
More informationPredictive Runtime Enforcement
Predictive Runtime Enforcement Srinivas Pinisetty 1, Viorel Preoteasa 1, Stavros Tripakis 1,2, Thierry Jéron 3, Yliès Falcone 4, Hervé Marchand 3 Aalto University, Finland University of California, Berkeley
More informationRetractable and Speculative Contracts
Retractable and Speculative Contracts Ivan Lanese Computer Science Department University of Bologna/INRIA Italy Joint work with Franco Barbanera and Ugo de'liguoro Map of the talk What retractable/speculative
More informationOnline Presentment and Payment FAQ s
General Online Presentment and Payment FAQ s What are some of the benefits of receiving my bill electronically? It is convenient, saves time, reduces errors, allows you to receive bills anywhere at any
More informationGetting Ready to Trade
Section VI. Getting Ready to Trade In This Section 1. Adding new securities 78 2. Updating your data 79 3. It's important to keep your data clean 80 4. Using Real-Time Alerts 81 5. Monitoring your tickers
More informationWe are not saying it s easy, we are just trying to make it simpler than before. An Online Platform for backtesting quantitative trading strategies.
We are not saying it s easy, we are just trying to make it simpler than before. An Online Platform for backtesting quantitative trading strategies. Visit www.kuants.in to get your free access to Stock
More informationSequential allocation of indivisible goods
1 / 27 Sequential allocation of indivisible goods Thomas Kalinowski Institut für Mathematik, Universität Rostock Newcastle Tuesday, January 22, 2013 joint work with... 2 / 27 Nina Narodytska Toby Walsh
More informationUSER GUIDE AMC BANKING 365 BUSINESS MICROSOFT DYNAMICS 365 BUSINESS CENTRAL FOR. English edition. AMC Consult A/S 26. October 2018 Version 2
USER GUIDE AMC BANKING 365 BUSINESS FOR MICROSOFT DYNAMICS 365 BUSINESS CENTRAL English edition AMC Consult A/S 26. October 2018 Version 2 Contents 1 Introduction... 3 2 AMC Banking 365 Business Setup...
More informationMonte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMSN50)
Monte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMSN50) Magnus Wiktorsson Centre for Mathematical Sciences Lund University, Sweden Lecture 2 Random number generation January 18, 2018
More informationfor Finance Python Yves Hilpisch Koln Sebastopol Tokyo O'REILLY Farnham Cambridge Beijing
Python for Finance Yves Hilpisch Beijing Cambridge Farnham Koln Sebastopol Tokyo O'REILLY Table of Contents Preface xi Part I. Python and Finance 1. Why Python for Finance? 3 What Is Python? 3 Brief History
More informationLoan Approval and Quality Prediction in the Lending Club Marketplace
Loan Approval and Quality Prediction in the Lending Club Marketplace Final Write-up Yondon Fu, Matt Marcus and Shuo Zheng Introduction Lending Club is a peer-to-peer lending marketplace where individual
More informationFannie Mae Connect Release Notification
Fannie Mae Connect Release Notification June 26, 2017 During the weekend of June 24, 2017, Fannie Mae will implement Fannie Mae Connect Version 7.0, which will provide a new, easy-to-use design and layout.
More informationCOMP417 Introduction to Robotics and Intelligent Systems. Reinforcement Learning - 2
COMP417 Introduction to Robotics and Intelligent Systems Reinforcement Learning - 2 Speaker: Sandeep Manjanna Acklowledgement: These slides use material from Pieter Abbeel s, Dan Klein s and John Schulman
More informationLeverage WeChat at its best to reach and serve Chinese consumers. Bruxelles, January 2017
Leverage WeChat at its best to reach and serve Chinese consumers Bruxelles, January 2017 1 2 Internet in China and the role of WeChat 4 3 How to best leverage WeChat to reach retail goals Success stories
More informationsample-bookchapter 2015/7/7 9:44 page 1 #1 THE BINOMIAL MODEL
sample-bookchapter 2015/7/7 9:44 page 1 #1 1 THE BINOMIAL MODEL In this chapter we will study, in some detail, the simplest possible nontrivial model of a financial market the binomial model. This is a
More informationLecture 14: Basic Fixpoint Theorems (cont.)
Lecture 14: Basic Fixpoint Theorems (cont) Predicate Transformers Monotonicity and Continuity Existence of Fixpoints Computing Fixpoints Fixpoint Characterization of CTL Operators 1 2 E M Clarke and E
More informationFrom PSL to NBA: a Modular Symbolic Encoding
From PSL to NBA: a Modular Symbolic Encoding A. Cimatti 1 M. Roveri 1 S. Semprini 1 S. Tonetta 2 1 ITC-irst Trento, Italy {cimatti,roveri}@itc.it 2 University of Lugano, Lugano, Switzerland tonettas@lu.unisi.ch
More informationCIS 500 Software Foundations Fall October. CIS 500, 6 October 1
CIS 500 Software Foundations Fall 2004 6 October CIS 500, 6 October 1 Midterm 1 is next Wednesday Today s lecture will not be covered by the midterm. Next Monday, review class. Old exams and review questions
More informationHomework #4. CMSC351 - Spring 2013 PRINT Name : Due: Thu Apr 16 th at the start of class
Homework #4 CMSC351 - Spring 2013 PRINT Name : Due: Thu Apr 16 th at the start of class o Grades depend on neatness and clarity. o Write your answers with enough detail about your approach and concepts
More informationCash Register Software Release ivue 1.6 Patch 1 March 2005
Frameworks Install/Update Alerts Added the functionality to display Installment Loans alerts. (CR 113372) Launcher Replaced the current Cash Register (CR) Launcher icon with a new icon and changed the
More informationOn the Optimality of a Family of Binary Trees Techical Report TR
On the Optimality of a Family of Binary Trees Techical Report TR-011101-1 Dana Vrajitoru and William Knight Indiana University South Bend Department of Computer and Information Sciences Abstract In this
More informationWelcome To VertexFX Trader Presentation
Welcome To VertexFX Trader Presentation Full Tutorial for VertexFX Trading Platform www.hybridsolutions.com VertexFX Trader Multi-Level Platform For Dealing Rooms, Clearing Houses, Market Makers and Brokerage
More informationIn this lecture, we will use the semantics of our simple language of arithmetic expressions,
CS 4110 Programming Languages and Logics Lecture #3: Inductive definitions and proofs In this lecture, we will use the semantics of our simple language of arithmetic expressions, e ::= x n e 1 + e 2 e
More informationComparing Goal-Oriented and Procedural Service Orchestration
Comparing Goal-Oriented and Procedural Service Orchestration M. Birna van Riemsdijk 1 Martin Wirsing 2 1 Technische Universiteit Delft, The Netherlands m.b.vanriemsdijk@tudelft.nl 2 Ludwig-Maximilians-Universität
More informationThe Traveling Salesman Problem. Time Complexity under Nondeterminism. A Nondeterministic Algorithm for tsp (d)
The Traveling Salesman Problem We are given n cities 1, 2,..., n and integer distances d ij between any two cities i and j. Assume d ij = d ji for convenience. The traveling salesman problem (tsp) asks
More informationNon-Deterministic Search
Non-Deterministic Search MDP s 1 Non-Deterministic Search How do you plan (search) when your actions might fail? In general case, how do you plan, when the actions have multiple possible outcomes? 2 Example:
More informationWork4Me. Algorithmic Version. Aging Accounts Receivable. Problem Eleven. 1 st Web-Based Edition
Work4Me Algorithmic Version 1 st Web-Based Edition Problem Eleven Aging Accounts Receivable Page 1 INTRODUCTION Log on to Algorithmic Work4Me II and from the Problems Menu Bar, select Problem 11, Aging
More informationRegret Minimization against Strategic Buyers
Regret Minimization against Strategic Buyers Mehryar Mohri Courant Institute & Google Research Andrés Muñoz Medina Google Research Motivation Online advertisement: revenue of modern search engine and
More informationA Consistent Semantics of Self-Adjusting Computation
A Consistent Semantics of Self-Adjusting Computation Umut A. Acar 1 Matthias Blume 1 Jacob Donham 2 December 2006 CMU-CS-06-168 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213
More informationMICROSOFT DYNAMICS 365
USER GUIDE AMC BANKING 365 FINANCIALS SERVICE FOR MICROSOFT DYNAMICS 365 English edition AMC Consult A/S 17. November 2017 Version 2 Contents 1 Introduction... 3 2 AMC Banking 365 Financials Service Setup...
More informationWHS FutureStation - Guide LiveStatistics
WHS FutureStation - Guide LiveStatistics LiveStatistics is a paying module for the WHS FutureStation trading platform. This guide is intended to give the reader a flavour of the phenomenal possibilities
More informationSublinear Time Algorithms Oct 19, Lecture 1
0368.416701 Sublinear Time Algorithms Oct 19, 2009 Lecturer: Ronitt Rubinfeld Lecture 1 Scribe: Daniel Shahaf 1 Sublinear-time algorithms: motivation Twenty years ago, there was practically no investigation
More informationParallelizing an Interactive Theorem Prover
Parallelizing an Interactive Theorem Prover Functional Programming and Proofs with ACL2 David L. Rager ragerdl@cs.utexas.edu The University of Texas at Austin August 20, 2012 1/ 45 Project Goals Add parallelism
More informationYao s Minimax Principle
Complexity of algorithms The complexity of an algorithm is usually measured with respect to the size of the input, where size may for example refer to the length of a binary word describing the input,
More informationSAT and DPLL. Introduction. Preliminaries. Normal forms DPLL. Complexity. Espen H. Lian. DPLL Implementation. Bibliography.
SAT and Espen H. Lian Ifi, UiO Implementation May 4, 2010 Espen H. Lian (Ifi, UiO) SAT and May 4, 2010 1 / 59 Espen H. Lian (Ifi, UiO) SAT and May 4, 2010 2 / 59 Introduction Introduction SAT is the problem
More informationSemantics with Applications 2b. Structural Operational Semantics
Semantics with Applications 2b. Structural Operational Semantics Hanne Riis Nielson, Flemming Nielson (thanks to Henrik Pilegaard) [SwA] Hanne Riis Nielson, Flemming Nielson Semantics with Applications:
More informationThis publication is one of a series of practicals that
PRACTICAL WAR TAX RESISTANCE #1 Controlling Federal Income Tax Withholding This publication is one of a series of practicals that offer ideas, tips, and information for individuals who want to cut off
More informationCOMP Analysis of Algorithms & Data Structures
COMP 3170 - Analysis of Algorithms & Data Structures Shahin Kamali Binomial Heaps CLRS 6.1, 6.2, 6.3 University of Manitoba Priority queues A priority queue is an abstract data type formed by a set S of
More informationHansa Financials HansaWorld
Hansa Financials HansaWorld Integrated Accounting, CRM and ERP System for Macintosh, Windows, Linux, PocketPC 2002 and AIX Volume 4: General Modules Assets, Cash Book, Consolidation, Expenses and Quotations
More information91.420/543: Artificial Intelligence UMass Lowell CS Fall 2010
91.420/543: Artificial Intelligence UMass Lowell CS Fall 2010 Lecture 17 & 18: Markov Decision Processes Oct 12 13, 2010 A subset of Lecture 9 slides from Dan Klein UC Berkeley Many slides over the course
More informationMAT385 Final (Spring 2009): Boolean Algebras, FSM, and old stuff
MAT385 Final (Spring 2009): Boolean Algebras, FSM, and old stuff Name: Directions: Problems are equally weighted. Show your work! Answers without justification will likely result in few points. Your written
More informationAVL Trees. The height of the left subtree can differ from the height of the right subtree by at most 1.
AVL Trees In order to have a worst case running time for insert and delete operations to be O(log n), we must make it impossible for there to be a very long path in the binary search tree. The first balanced
More informationDecidability and Recursive Languages
Decidability and Recursive Languages Let L (Σ { }) be a language, i.e., a set of strings of symbols with a finite length. For example, {0, 01, 10, 210, 1010,...}. Let M be a TM such that for any string
More informationEpistemic Planning With Implicit Coordination
Epistemic Planning With Implicit Coordination Thomas Bolander, DTU Compute, Technical University of Denmark Joint work with Thorsten Engesser, Robert Mattmüller and Bernhard Nebel from Uni Freiburg Thomas
More informationShareholder Maintenance Worksheet.
Maintenance Income) that the building will receive in the upcoming year. The Total Projected Income is an addition of the Total projected yearly rent, commercial and other income. Shareholder Maintenance
More informationMonte Carlo Methods in Structuring and Derivatives Pricing
Monte Carlo Methods in Structuring and Derivatives Pricing Prof. Manuela Pedio (guest) 20263 Advanced Tools for Risk Management and Pricing Spring 2017 Outline and objectives The basic Monte Carlo algorithm
More informationNotes on Natural Logic
Notes on Natural Logic Notes for PHIL370 Eric Pacuit November 16, 2012 1 Preliminaries: Trees A tree is a structure T = (T, E), where T is a nonempty set whose elements are called nodes and E is a relation
More informationCS 343: Artificial Intelligence
CS 343: Artificial Intelligence Markov Decision Processes II Prof. Scott Niekum The University of Texas at Austin [These slides based on those of Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC
More informationTHE CM TRADING METATRADER 4 USER GUIDE:
THE CM TRADING METATRADER 4 USER GUIDE: THE MAIN SCREEN Main menu (access to the program menu and settings); Toolbars (quick access to the program features and settings); Market Watch window (real-time
More informationBinary and Binomial Heaps. Disclaimer: these slides were adapted from the ones by Kevin Wayne
Binary and Binomial Heaps Disclaimer: these slides were adapted from the ones by Kevin Wayne Priority Queues Supports the following operations. Insert element x. Return min element. Return and delete minimum
More informationchainfrog WHAT ARE SMART CONTRACTS?
chainfrog WHAT ARE SMART CONTRACTS? WHAT ARE SMART CONTRACTS AND WHERE AND WHY WOULD YOU USE THEM A question I get asked again and again at lectures and conferences is, what exactly are smart contracts?
More informationSAT and DPLL. Espen H. Lian. May 4, Ifi, UiO. Espen H. Lian (Ifi, UiO) SAT and DPLL May 4, / 59
SAT and DPLL Espen H. Lian Ifi, UiO May 4, 2010 Espen H. Lian (Ifi, UiO) SAT and DPLL May 4, 2010 1 / 59 Normal forms Normal forms DPLL Complexity DPLL Implementation Bibliography Espen H. Lian (Ifi, UiO)
More informationMargin Direct User Guide
Version 2.0 xx August 2016 Legal Notices No part of this document may be copied, reproduced or translated without the prior written consent of ION Trading UK Limited. ION Trading UK Limited 2016. All Rights
More informationWashington Health Benefit Exchange. 5.0 Washington Healthplanfinder System Release
5.0 Washington Healthplanfinder System Release September, 2017 5.0 System Release Outage September 2017 September 2017 Sunday Monday Tuesday Wednesday Thursday Friday Saturday 1 2 3 4 5 6 7 8 9 Washington
More informationFinancial Accounting and Management Accounting: Overview
UNIT 4 Financial Accounting and Management Accounting: Overview Lesson 1 Explaining Financial Accounting (FI) 94 Lesson 2 Explaining Management Accounting (CO) 103 Lesson 3 Outlining the Integration Between
More informationPayment Portal Registration Quick Guide
Payment Portal Registration Quick Guide Paying your rent is fast and easy with Invitation Homes online portal! Step 1: To register online and create your account, visit www.. Hover over the Current Residents
More informationTHE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE
THE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE GÜNTER ROTE Abstract. A salesperson wants to visit each of n objects that move on a line at given constant speeds in the shortest possible time,
More information