Developer Guide

Contents

Computation

The limited-memory BFGS algorithm is a special case of an iterative solver. For parameters, input, and output of iterative solvers, see Iterative Solver > Computation .

Algorithm Input

In addition to the input of the iterative solver, the limited-memory BFGS algorithm accepts the following optional input:
OptionalDataID
Input
correctionPairs
Numeric table of size 2
m
x
p
where the rows represent correction pairs
s
and
y
. The row
correctionPairs
[
j
], 0 ≤
j
<
m
, is a correction vector
s
j
, and the row
correctionPairs
[
j
],
m
j
< 2
m
, is a correction vector
y
j
.
correctionIndices
Numeric table of size 1 x 2 with 32-bit integer indexes. The first value is the index of correction pair
t
, the second value is the index of last iteration
k
from the previous run.
averageArgumentLIterations
Numeric table of size 2 x
p
, where row 0 represents average arguments for previous
L
iterations, and row 1 represents average arguments for last
L
iterations. These values are required to compute
s
correction vectors in the next step. See step 6.b.iii of the limited-memory BFGS algorithm.

Algorithm Parameters

In addition to parameters of the iterative solver, the limited-memory BFGS algorithm has the following parameters:
Parameter
Default Value
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
method
defaultDense
Performance-oriented computation method.
batchIndices
NULL
The numeric table of size
nIterations
x
batchSize
with 32-bit integer indices of terms in the objective function to be used in step 2 of the limited-memory BFGS algorithm. If no indices are provided, the implementation generates random indices.
This parameter can be an object of any class derived from
NumericTable
, except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
batchSize
10
The number of observations to compute the stochastic gradient. The implementation of the algorithm ignores this parameter if the
batchIndices
numeric table is provided.
If
BatchSize
equals the number of terms in the objective function, no random sampling is performed and all terms are used to calculate the gradient.
correctionPairBatchSize
100
The number of observations to compute the sub-sampled Hessian for correction pairs computation in step 6.b.ii of the limited-memory BFGS algorithm. The implementation of the algorithm ignores this parameter if the
correctionPairIndices
numeric table is provided.
If
correctionPairBatchSize
equals the number of terms in the objective function, no random sampling is performed and all terms are used to calculate the Hessian matrix.
correctionPairIndices
NULL
The numeric table of size (
nIterations
/
L
) x
correctionPairBatchSize
with 32-bit integer indices to be used instead of random values in step 6.b.i of the limited-memory BFGS algorithm. If no indices are provided, the implementation generates random indices.
This parameter can be an object of any class derived from
NumericTable
, except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
If the algorithm runs with no optional input data, (
nIterations
/
L
- 1) rows of the table are used. Otherwise, it can use one more row, (
nIterations
/
L
) in total.
m
10
The memory parameter. The maximum number of correction pairs that define the approximation of the Hessian matrix.
L
10
The number of iterations between calculations of the curvature estimates.
stepLengthSequence
Numeric table of size 1 x 1 that contains the default step length equal to 1.
The numeric table of size 1 x
nIterations
or 1 x 1. The contents of the table depend on its size:
  • size = 1 x
    nIterations
    : values of the step-length sequence α
    k
    for
    k
    = 1, …,
    nIterations
    .
  • size = 1 x 1: the value of step length at each iteration α
    1
    = ... = α
    nIterations
This parameter can be an object of any class derived from
NumericTable
, except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
The recommended data type for storing the step-length sequence is the floating-point type, either
float
or
double
, that the algorithm uses in intermediate computations.
engine
SharePtr< engines:: mt19937:: Batch>()
Pointer to the random number generator engine that is used internally for random choosing terms from the objective function.

Algorithm Output

In addition to the output of the iterative solver, the limited-memory BFGS algorithm calculates the following optional results:
OptionalDataID
Output
correctionPairs
Numeric table of size 2
m
x
p
where the rows represent correction pairs
s
and
y
. The row
correctionPairs
[
j
], 0 ≤
j
<
m
, is a correction vector
s
j
, and the row
correctionPairs
[
j
],
m
j
< 2
m
, is a correction vector
y
j
.
correctionIndices
Numeric table of size 1 x 2 with 32-bit integer indexes. The first value is the index of correction pair
t
, the second value is the index of last iteration
k
from the previous run.
averageArgumentLIterations
Numeric table of size 2 x
p
, where row 0 represents average arguments for previous
L
iterations, and row 1 represents average arguments for last
L
iterations. These values are required to compute
s
correction vectors in the next step. See step 6.b.iii of the limited-memory BFGS algorithm.

Product and Performance Information

1

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Notice revision #20110804