Bug fix in parallel processing for main driver andmlmc.test (thanks to Qian Xin, University of Bristol, for bug report).
At the same time, improve the method of splitting simulations in parallel for the main mlmc driver, so that work is more evenly distributed to keep all cores busy.
mlmc 2.1.0
Add parameter value checks in mlmc.test.
Allow user to specify alpha, beta, andgamma to mlmc.test, rather than forcing estimation by linear regression. Note this is a departure from the original Matlab code, but if they are left unspecified then the same results as under Matlab are reproduced.
Improve specificity of some argument documentation inmlmc.test.
mlmc 2.0.2
Package was removed from CRAN because I didn’t notice my old Oxford email address wasn’t forwarding any longer. In order to comply with CRAN changes, the C++ routines are now registered and maintainer info updated to my Durham email.
The Matlab driver code by Mike Giles has been quite substantially updated, so this major version bump in the R package addresses updating this code to match the new driver API. None of these sub-bullets are bug fixes, merely changing to match the new best-practice for the MLMC driver designed by Mike Giles. In particular:
User level sampling functions must now also return the total cost of all samples simulated at that level. Therefore user level sampler functions must return a list with a sums andcost element.
The gamma argument is no longer required, since it is not used in automatic cost computation, and can be estimated as foralpha and beta.
mlmc.test() no longer takes M, a level refinement factor, since this was only used to calculate the cost asN*M^l. Per above comment, the user now defines cost completely via the return from the level sampler function.
Along these lines, mlmc.test() now uses the user returned cost in all places: previously CPU time was measured as cost in the convergence tests section, whilst the MLMC complexity tests previously forced costs to be N*M^l.
Some (very) old bugs were squashed in the Euler-Maruyama discretisation level sampler, opre_l() which affected lookback call and Heston model options.
I managed to get hold of a Matlab license, so have now confirmed that the examples in the docs return (within sampling variability) the same results for both Euler-Maruyama and Milstein discretisation example level sampler functions.
There is now a hex sticker! It is hopefully fairly self explanatory: many fast simulations are done at low levels (lots of dice, with the hare running at the bottom of the stairs); fewer simulations are done at higher levels (fewer dice as you go up each step, with a tortoise and fewest dice on top step)!