R: Integration by Stratified Sampling for Variance Reduction

divonne {cubature} | R Documentation

## Integration by Stratified Sampling for Variance Reduction

### Description

Divonne works by stratified sampling, where the partioning of the integration region is aided by methods from numerical optimization.

### Usage

    divonne(
      f,
      nComp = 1L,
      lowerLimit,
      upperLimit,
      ...,
      relTol = 1e-05,
      absTol = 1e-12,
      minEval = 0L,
      maxEval = 10^6,
      flags = list(verbose = 0L, final = 1L, keep_state = 0L, level = 0L),
      rngSeed = 0L,
      nVec = 1L,
      key1 = 47L,
      key2 = 1L,
      key3 = 1L,
      maxPass = 5L,
      border = 0,
      maxChisq = 10,
      minDeviation = 0.25,
      xGiven = NULL,
      nExtra = 0L,
      peakFinder = NULL,
      stateFile = NULL
    )

### Arguments

`f` | The function (integrand) to be integrated as in `cuhre`. Optionally, the function can take an additional arguments in addition to the variable being integrated: - `cuba_phase`. The last argument, phase, indicates the integration phase: 0. sampling of the points in `xgiven`, 1. partitioning phase, 2. final integration phase, 3. refinement phase. This information might be useful if the integrand takes long to compute and a sufficiently accurate approximation of the integrand is available. The actual value of the integral is only of minor importance in the partitioning phase, which is instead much more dependent on the peak structure of the integrand to find an appropriate tessellation. An approximation which reproduces the peak structure while leaving out the fine details might hence be a perfectly viable and much faster substitute when `cuba_phase` < 2. In all other instances, phase can be ignored and it is entirely admissible to define the integrand without it. which is the
`nComp` | The number of components of f, default 1, bears no relation to the dimension of the hypercube over which integration is performed.
`lowerLimit` | The lower limit of integration, a vector for hypercubes.
`upperLimit` | The upper limit of integration, a vector for hypercubes.
`...` | All other arguments passed to the function f.
`relTol` | The maximum tolerance, default 1e-5.
`absTol` | the absolute tolerance, default 1e-12.
`minEval` | the minimum number of function evaluations required
`maxEval` | The maximum number of function evaluations needed, default 10^6. Note that the actual number of function evaluations performed is only approximately guaranteed not to exceed this number.
`flags` | flags governing the integration. The list here is exhaustive to keep the documentation and invocation uniform, but not all flags may be used for a particular method as noted below. List components: `verbose` encodes the verbosity level, from 0 (default) to 3. Level 0 does not print any output, level 1 prints reasonable information on the progress of the integration, level 2 also echoes the input parameters, and level 3 further prints the subregion results. `final` when 0, all sets of samples collected on a subregion during the various iterations or phases contribute to the final result. When 1, only the last (largest) set of samples is used in the final result. `smooth` Applies to Suave and Vegas only. When 0, apply additional smoothing to the importance function, this moderately improves convergence for many integrands. When 1, use the importance function without smoothing, this should be chosen if the integrand has sharp edges. `keep_state` when nonzero, retain state file if argument `stateFile` is non-null, else delete `stateFile` if specified. `load_state` Applies to Vegas only. Reset the integrator's state even if a state file is present, i.e. keep only the grid. Together with `keep_state` this allows a grid adapted by one integration to be used for another integrand. `level` applies only to Divonne, Suave and Vegas. When `0`, Mersenne Twister random numbers are used. When nonzero Ranlux random numbers are used, except when `rngSeed` is zero which forces use of Sobol quasi-random numbers. Ranlux implements Marsaglia and Zaman's 24-bit RCARRY algorithm with generation period p, i.e. for every 24 generated numbers used, another p-24 are skipped. The luxury level for the Ranlux generator may be encoded in `level` as follows: Level 1 (p = 48) gives very long period, passes the gap test but fails spectral test Level 2 (p = 97) passes all known tests, but theoretically still defective Level 3 (p = 223) any theoretically possible correlations have very small chance of being observed Level 4 (p = 389) highest possible luxury, all 24 bits chaotic Levels 5-23 default to 3, values above 24 directly specify the period p. Note that Ranlux's original level 0, (mis)used for selecting Mersenne Twister in Cuba, is equivalent to `level` = 24.
`rngSeed` | seed, default 0, for the random number generator. Note the articulation with `level` settings for `flag`
`nVec` | the number of vectorization points, default 1, but can be set to an integer > 1 for vectorization, for example, 1024 and the function f above needs to handle the vector of points appropriately. See vignette examples.
`key1` | integer that determines sampling in the partitioning phase: `key1 = 7, 9, 11, 13` selects the cubature rule of degree `key1`. Note that the degree-11 rule is available only in 3 dimensions, the degree-13 rule only in 2 dimensions. For other values of `key1`, a quasi-random sample of \code{n=|key1|} points is used, where the sign of `key1` determines the type of sample, `key1 = 0`, use the default rule. `key1 > 0`, use a Korobov quasi-random sample, `key1 < 0`, use a Sobol quasi-random sample if `flags$seed` is zero, otherwise a “standard” sample (Mersenne Twister) pseudo-random sample
`key2` | integer that determines sampling in the final integration phase: same as `key1`, but here \code{n = |key2|} determines the number of points, \code{n > 39}, sample n points, \code{n < 40}, sample \code{n} `nneed` points, where `nneed` is the number of points needed to reach the prescribed accuracy, as estimated by Divonne from the results of the partitioning phase.
`key3` | integer that sets the strategy for the refinement phase: `key3 = 0`, do not treat the subregion any further. `key3 = 1`, split the subregion up once more. Otherwise, the subregion is sampled a third time with `key3` specifying the sampling parameters exactly as `key2` above.
`maxPass` | integer that controls the thoroughness of the partitioning phase: The partitioning phase terminates when the estimated total number of integrand evaluations (partitioning plus final integration) does not decrease for `maxPass` successive iterations. A decrease in points generally indicates that Divonne discovered new structures of the integrand and was able to find a more effective partitioning. `maxPass` can be understood as the number of “safety” iterations that are performed before the partition is accepted as final and counting consequently restarts at zero whenever new structures are found.
`border` | the relative width of the border of the integration region. Points falling into the border region will not be sampled directly, but will be extrapolated from two samples from the interior. Use a non-zero `border` if the integrand subroutine cannot produce values directly on the integration boundary. The relative width of the border is identical in all the dimensions. For example, set `border=0.1` for a border of width equal to 10\ width of the integration region.
`maxChisq` | the maximum Chi2 value a single subregion is allowed to have in the final integration phase. Regions which fail this Chi2 test and whose sample averages differ by more than `min.deviation` move on to the refinement phase.
`minDeviation` | a bound, given as the fraction of the requested error of the entire integral, which determines whether it is worthwhile further examining a region that failed the Chi2 test. Only if the two sampling averages obtained for the region differ by more than this bound is the region further treated.
`xGiven` | a matrix ( `nDim`, `nGiven`). A list of `nGiven` points where the integrand might have peaks. Divonne will consider these points when partitioning the integration region. The idea here is to help the integrator find the extrema of the integrand in the presence of very narrow peaks. Even if only the approximate location of such peaks is known, this can considerably speed up convergence.
`nExtra` | the maximum number of extra points the peak-finder subroutine will return. If `nextra` is zero, `peakfinder` is not called and an arbitrary object may be passed in its place, e.g. just 0.
`peakFinder` | the peak-finder subroutine. This R function is called whenever a region is up for subdivision and is supposed to point out possible peaks lying in the region, thus acting as the dynamic counterpart of the static list of points supplied in `xgiven`. It is expected to be declared as `peakFinder <- function(bounds, nMax)` where `bounds` is a matrix of dimension (`2, nDim`) which contains the lower (row 1) and upper (row 2) bounds of the subregion. The returned value should be a matrix (`nX, nDim`) where `nX` is the actual number of points (should be less or equal to `nMax`).
`stateFile` | the name of an external file. Vegas can store its entire internal state (i.e. all the information to resume an interrupted integration) in an external file. The state file is updated after every iteration. If, on a subsequent invocation, Vegas finds a file of the specified name, it loads the internal state and continues from the point it left off. Needless to say, using an existing state file with a different integrand generally leads to wrong results. Once the integration finishes successfully, i.e. the prescribed accuracy is attained, the state file is removed. This feature is useful mainly to define ‘check-points’ in long-running integrations from which the calculation can be restarted.

### Details

Divonne uses stratified sampling for variance reduction, that is, it partitions the integration region such that all subregions have an approximately equal value of a quantity called the spread (volume times half-range).

See details in the documentation.

### Value

A list with components:

nregions

  the actual number of subregions needed

neval

  the actual number of integrand evaluations needed

returnCode

  if zero, the desired accuracy was reached, if -1, dimension out of range, if 1, the accuracy goal was not met within the allowed maximum number of integrand evaluations.

integral

  vector of length `nComp`; the integral of `integrand` over the hypercube

error

  vector of length `nComp`; the presumed absolute error of `integral`

prob

  vector of length `nComp`; the Chi2-probability (not the Chi2-value itself!) that `error` is not a reliable estimate of the true integration error.

### References

J. H. Friedman, M. H. Wright (1981) A nested partitioning procedure for numerical multiple integration. ACM Trans. Math. Software, 7(1), 76-92.

J. H. Friedman, M. H. Wright (1981) User's guide for DIVONNE. SLAC Report CGTM-193-REV, CGTM-193, Stanford University.

T. Hahn (2005) CUBA-a library for multidimensional numerical integration. Computer Physics Communications, 168, 78-95.

### See Also

`cuhre`, `suave`, `vegas`

### Examples

    integrand <- function(arg, phase) {
      x <- arg[1]
      y <- arg[2]
      z <- arg[3]
      ff <- sin(x)*cos(y)*exp(z);
    return(ff)
    }
    divonne(integrand, relTol=1e-3,  absTol=1e-12, lowerLimit = rep(0, 3), upperLimit = rep(1, 3),
            flags=list(verbose = 2),  key1= 47)

    # Example with a peak-finder function
    nDim <- 3L
    peakf <- function(bounds, nMax) {
    #  print(bounds) # matrix (ndim,2)
      x <- matrix(0, ncol = nMax, nrow = nDim)
       pas <- 1 / (nMax - 1)
       # 1ier point
       x[, 1] <- rep(0, nDim)
       # Les autres points
       for (i in 2L:nMax) {
          x[, i] <- x[, (i - 1)] + pas
        }
      x
    } #end peakf

    divonne(integrand, relTol=1e-3,  absTol=1e-12,
            lowerLimit = rep(0, 3), upperLimit = rep(1, 3),
            flags=list(verbose = 2),  peakFinder = peakf, nExtra = 4L)

[Package cubature version 2.0.4.2 Index]