Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course traveled by research ideas as they grow into production systems.

Since 1984, the Condor project has helped ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must reflect the sociology of communities. Throughout, we reflect on the lessons of experience and chart the course travelled by research ideas as they grow into production systems.

We investigate the quality of solutions obtained from sample-average approximations to two-stage stochastic linear programs with recourse. We use a recently developed software tool executing on a computational grid to solve many large instances of these problems, allowing us to obtain high-quality solutions and to verify optimality and near-optimality of the computed solutions in various ways.

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Computational grids provide mechanisms for sharing and accessing large and heterogeneous collections of remote resources such as computers, online instruments, storage space, data, and applications. Resources are requested ("discovered") by specifying a set of desired attributes. Resource attributes have various degrees of dynamism, from mostly static attributes, such as operating system version, to highly dynamic ones, such as available network bandwidth or CPU load. Another dimension of dynamism is introduced by variable and highly diverse sharing policies: resources are made available to the grid community based on locally defined and potentially changing policies.

We survey how semidefinite programming can be used for finding good approximative solutions to hard combinatorial optimization problems.

The Cactus software is representative for a whole class of scientific applications; typically those that are tightly coupled, have regular space decomposition, and huge memory and processor time requirements.

The Cactus software package is suitable for a class of scientific applications that are tightly coupled, have regular space decompositions, and involve huge memory and processor time requirements. Cactus has proved to be a valuable tool for astrophysicists, who first initiated its development. However, today's fastest supercomputers are not powerful enough to perform realistic large-scale astrophysics simulations with Cactus. Instead, we must turn to innovative resource environments -- in particular, computational Grids -- to satisfy this need for computational power. Our paper addresses issues related to the execution of applications such as Cactus in Grid environments. We focus on two types of Grids: a set of geographically distributed supercomputers and a collection of one million Internet-connected workstations.

this paper we consider a complete B&B algorithm for the QAP based on the use of the QP bound at each node of the B&B tree. The lower bound QPB(A;B;C) is reviewed in Section 2. In Section 2 we also decribe an approach based on the FrankWolfe (FW) algorithm that we use to approximately solve the convex quadratic program associated with QPB(A;B;C). The FW algorithm generates dual information that can be used to estimate the effect of fixing an assignment x ij = 1 to create a "child" problem at a node in the B&B tree. Our branching rules, described in Section 3, make extensive use of this dual information. In Section 4 we give computational results on a variety of problems of size n ? 15 from QAPLIB. We obtain state-of-the-art results on many problems, including instances of the famous nugxx problems up to size n = 24

This paper intends to show that the time needed to solve mixed integer programming problems by branch and bound can be roughly predicted early in the solution process. We construct a procedure that can be implemented as part of an MIP solver. It is based on analyzing the partial tree resulting from running the algorithm for a short period of time, and predicting the shape of the whole tree. The procedure is tested on instances from the literature. This work was inspired by the practical applicability of such a result. 1.