About the Book

The Stony Brook Algorithm Repository

Steven Skiena
Stony Brook University
Dept. of Computer Science

• By Language
  • C
  • C++
  • C#
  • Java
  • FORTRAN
  • Python
  • Mathematica
  • Pascal
  • ADA
  • Lisp
  • Binary
• By Problem
  • 1.1 Data Structures
    • Dictionaries
    • Priority Queues
    • Suffix Trees and Arrays
    • Graph Data Structures
    • Set Data Structures
    • Kd-Trees
  • 1.2 Numerical Problems
    • Solving Linear Equations
    • Bandwidth Reduction
    • Matrix Multiplication
    • Determinants and Permanents
    • Constrained and Unconstrained Optimization
    • Linear Programming
    • Random Number Generation
    • Factoring and Primality Testing
    • Arbitrary Precision Arithmetic
    • Knapsack Problem
    • Discrete Fourier Transform
  • 1.3 Combinatorial Problems
    • Sorting
    • Searching
    • Median and Selection
    • Generating Permutations
    • Generating Subsets
    • Generating Partitions
    • Generating Graphs
    • Calendrical Calculations
    • Job Scheduling
    • Satisfiability
  • 1.4 Graph Problems -- polynomial-time problems
    • Connected Components
    • Topological Sorting
    • Minimum Spanning Tree
    • Shortest Path
    • Transitive Closure and Reduction
    • Matching
    • Eulerian Cycle / Chinese Postman
    • Edge and Vertex Connectivity
    • Network Flow
    • Drawing Graphs Nicely
    • Drawing Trees
    • Planarity Detection and Embedding
  • 1.5 Graph Problems -- hard problems
    • Clique
    • Independent Set
    • Vertex Cover
    • Traveling Salesman Problem
    • Hamiltonian Cycle
    • Graph Partition
    • Vertex Coloring
    • Edge Coloring
    • Graph Isomorphism
    • Steiner Tree
    • Feedback Edge/Vertex Set
  • 1.6 Computational Geometry
    • Robust Geometric Primitives
    • Convex Hull
    • Triangulation
    • Voronoi Diagrams
    • Nearest Neighbor Search
    • Range Search
    • Point Location
    • Intersection Detection
    • Bin Packing
    • Medial-Axis Transformation
    • Polygon Partitioning
    • Simplifying Polygons
    • Shape Similarity
    • Motion Planning
    • Maintaining Line Arrangements
    • Minkowski Sum
  • 1.7 Set and String Problems
    • Set Cover
    • Set Packing
    • String Matching
    • Approximate String Matching
    • Text Compression
    • Cryptography
    • Finite State Machine Minimization
    • Longest Common Substring
    • Shortest Common Superstring
• Algorithm Links
  • Algorithm Lectures
  • Algorithm Design Manual Information
  • CSE 373 Course page
  • NIST Dictory of Algorithms and Data Structures
  • World of Mathematics
  • Programming Challenges
  • Graduate Study Opportuinties

ILOG CP contains three modules:

ILOG Solver—The core constraint-programming engine. It provides the foundation for solving complex scheduling, sequencing, timetabling, routing and dispatching problems with large numbers of irregular constraints. Using logical and specialized constraints, the search algorithms find feasible solutions efficiently and allow you to improve your solutions.

ILOG Scheduler—Provides extensions for scheduling problems that solve difficult challenges in manufacturing, transportation and workforce scheduling. Scheduler allows you to build plans and schedules that take into account temporal constraints and many of the special characteristics of the resources and activities you need to schedule. ILOG Scheduler is at the heart of ILOG Plant PowerOps.

ILOG Dispatcher—Provides extensions for vehicle routing that solve difficult challenges in transportation and field service. This CP module helps you determine the most efficient set of routes, given time and distance constraints. ILOG Dispatcher is at the heart of ILOG Transport PowerOps.