mlmc 2.1.1
- Bug fix in parallel processing for main driver and
mlmc.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, and
gamma 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 in
mlmc.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 and
cost element.
- The
gamma argument is no longer required, since it is
not used in automatic cost computation, and can be estimated as for
alpha and beta.
mlmc.test() no longer takes M, a level
refinement factor, since this was only used to calculate the cost as
N*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)!
- There is now a documentation website at https://mlmc.louisaslett.com/
mlmc 1.0.0