Preface

One of the major differences between combinatorial computing and other areas of computing such as statistics, numerical analysis and linear programming is the use of complex data types. Whilst the built-in types, such as integers, reals, vectors, and matrices, usually suffice in the other areas, combinatorial computing relies heavily on types like stacks, queues, dictionaries, sequences, sorted sequences, priority queues, graphs, points, segments, ... In the fall of 1988, we started a project (called LEDA for Library of Efficient Data types and Algorithms) to build a small, but growing library of data types and algorithms in a form which allows them to be used by non-experts. We hope that the system will narrow the gap between algorithms research, teaching, and implementation. The main features of LEDA are:

1. LEDA provides a sizable collection of data types and algorithms in a form which allows them to be used by non-experts. This collection includes most of the data types and algorithms described in the text books of the area.

2. LEDA gives a precise and readable specification for each of the data types and algorithms mentioned above. The specifications are short (typically, not more than a page), general (so as to allow several implementations), and abstract (so as to hide all details of the implementation).

3. For many efficient data structures access by position is important. In LEDA, we use an item concept to cast positions into an abstract form. We mention that most of the specifications given in the LEDA manual use this concept, i.e., the concept is adequate for the description of many data types.

4. LEDA contains efficient implementations for each of the data types, e.g., Fibonacci heaps for priority queues, skip lists and dynamic perfect hashing for dictionaries, ...

5. LEDA contains a comfortable data type graph. It offers the standard iterations such as ``for all nodes v of a graph G do'' or ``for all neighbors w of v do'', it allows to add and delete vertices and edges and it offers arrays and matrices indexed by nodes and edges,... The data type graph allows to write programs for graph problems in a form close to the typical text book presentation.

6. LEDA is implemented by a C++ class library. It can be used with almost any C++ compiler that supports templates.

7. LEDA is available from Algorithmic Solutions Software GmbH. See http://www.algorithmic-solutions.com.

This manual contains the specifications of all data types and algorithms currently available in LEDA. Users should be familiar with the C++ programming language (see [83] or [56]).

The manual is structured as follows: In Chapter Basics, which is a prerequisite for all other chapters, we discuss the basic concepts and notations used in LEDA. New users of LEDA should carefully read Section User Defined Parameter Types to avoid problems when plugging in self defined parameter types. If you want to get information about the LEDA documentation scheme please read Section DocTools. For technical information concerning the installation and usage of LEDA users should refer to Chapter TechnicalInformation. There is also a section describing namespaces and the interaction with other software libraries (Section NameSpace). The other chapters define the data types and algorithms available in LEDA and give examples of their use. These chapters can be consulted independently from one another.

More information about LEDA can be found on the LEDA web page:
http://www.algorithmic-solutions.com/enleda.html

Finally there's a tool called xlman which allows online help and demonstration on all unix platforms having a L^ATEX package installed.

New in Version 5.2

Version Macro:

__LEDA__ (defined to 510 in this version)

Globally:

Support for g++-4.0.x and Visual Studio 2005 (x86)has been added.

Graphs:

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Graph and subgraph isomorphism algorithms have been added. See the Manual pages and demo/graph_iso/gw_isomorphism.cpp for a demo program (this part uses static graphs; they are not available when using MSVC++ 6.0, Borland 5.5.1, 5.6.). This package is in experimental state!

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The implementation of static graphs has been improved.

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The time efficiency of flow algorithms has been improved considerably.

Geometry:

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A one sweep unite operation for polygons has been added. It improves the running time considerably.

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Time efficiency of circular arc computations has been improved (up to a speedup of 10).

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Approximated computation (= input and output data contain curved segments, only the computation uses straight line approximations) of boolean operations has been added. It computes as fast as in the case of straight line segments.

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Several new functions for (rat_)gen_polygon added, such as buffering (experimental; see manual pages), reader/writer for WKB (Well Known Binary) format, and make_weakly_simple (see manual for new options).

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Contour operation for r_circle_polygon has been added.

Only the most important changes are listed here. Please read the CHANGES and FIXES files in the LEDA root directory for more information.