Parallel Restarted Search

A Monte Carlo Study of Randomised Restarted Search in ILP

Recent statistical performance surveys of search algorithms in difficult combinatorial problems have demonstrated the benefits of randomising and restarting the search procedure. Specifically, it has been found that if the search cost distribution (SCD) of the non-restarted randomised search exhibits a slower-than-exponential decay (that is, a “heavy tail”), restarts can reduce the search cost ...

Multiple Explicitly Restarted Arnoldi Method for Solving Large Eigenproblems

In this paper we propose a new approach for calculating some eigenpairs of large sparse nonHermitian matrices. This method, called Multiple Explicitly Restarted Arnoldi (MERAM), is particularly well suited for environments that combine different parallel programming paradigms. This technique is based on a multiple use of Explicitly Restarted Arnoldi method and improves its convergence. This tec...

ZRAM: a library of parallel search algorithms and its use in enumeration and combinatorial optimization

ZRAM is a software library which renders the power of parallel computers usable for combinatorial optimization and enumeration. It provides a set of parallel search engines for branch-and-bound, reverse search, backtrack¬ ing and tree-size estimation. These search engines hide the complexity of parallelism from the application programmer. ZRAM contains the first parallel implementation of the r...

Expected Gains from Parallelizing Constraint Solving for

A number of recent studies have examined how the difficulty of various NP-hard problems varies with simple parameters describing their structure. In particular, they have identified parameter values that distinguish regions with many hard problem instances from relatively easier ones. In this paper we continue this work by examining independent parallel search. SpecificalIy, we evaluate the spe...

A Comparison Between Parallel Multiple Explicitly Restarted and Explicitly Restarted Block Arnoldi Methods