There isn't a good answer to this in a single dispatch language like java. The overhead is not excessive.
Sent from my iPad On Jul 23, 2011, at 5:32 PM, "Lance Norskog (JIRA)" <j...@apache.org> wrote: > > [ > https://issues.apache.org/jira/browse/MAHOUT-672?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13070080#comment-13070080 > ] > > Lance Norskog commented on MAHOUT-672: > -------------------------------------- > > May I suggest that a redo of Matrices include a solution to the > double-dispatch problem? > > [Double Dispatch|http://en.wikipedia.org/wiki/Double_dispatch] > > In this case, there are many variations of the exact code to apply for Matrix > :: Vector operations, and way too many uses of *instanceof*. > > Also, the LinearOperator suite is big enough to be its own patch. > > > > >> Implementation of Conjugate Gradient for solving large linear systems >> --------------------------------------------------------------------- >> >> Key: MAHOUT-672 >> URL: https://issues.apache.org/jira/browse/MAHOUT-672 >> Project: Mahout >> Issue Type: New Feature >> Components: Math >> Affects Versions: 0.5 >> Reporter: Jonathan Traupman >> Priority: Minor >> Fix For: 0.6 >> >> Attachments: 0001-MAHOUT-672-LSMR-iterative-linear-solver.patch, >> 0001-MAHOUT-672-LSMR-iterative-linear-solver.patch, MAHOUT-672.patch, >> MAHOUT-672.patch >> >> Original Estimate: 48h >> Remaining Estimate: 48h >> >> This patch contains an implementation of conjugate gradient, an iterative >> algorithm for solving large linear systems. In particular, it is well suited >> for large sparse systems where a traditional QR or Cholesky decomposition is >> infeasible. Conjugate gradient only works for matrices that are square, >> symmetric, and positive definite (basically the same types where Cholesky >> decomposition is applicable). Systems like these commonly occur in >> statistics and machine learning problems (e.g. regression). >> Both a standard (in memory) solver and a distributed hadoop-based solver >> (basically the standard solver run using a DistributedRowMatrix a la >> DistributedLanczosSolver) are included. >> There is already a version of this algorithm in taste package, but it >> doesn't operate on standard mahout matrix/vector objects, nor does it >> implement a distributed version. I believe this implementation will be more >> generically useful to the community than the specialized one in taste. >> This implementation solves the following types of systems: >> Ax = b, where A is square, symmetric, and positive definite >> A'Ax = b where A is arbitrary but A'A is positive definite. Directly solving >> this system is more efficient than computing A'A explicitly then solving. >> (A + lambda * I)x = b and (A'A + lambda * I)x = b, for systems where A or >> A'A is singular and/or not full rank. This occurs commonly if A is large and >> sparse. Solving a system of this form is used, for example, in ridge >> regression. >> In addition to the normal conjugate gradient solver, this implementation >> also handles preconditioning, and has a sample Jacobi preconditioner >> included as an example. More work will be needed to build more advanced >> preconditioners if desired. > > -- > This message is automatically generated by JIRA. > For more information on JIRA, see: http://www.atlassian.com/software/jira > >
