Minimum of the objective function using the Nelder Mead simplex algorithm.
tfp.optimizer.nelder_mead_minimize(
objective_function,
initial_simplex=None,
initial_vertex=None,
step_sizes=None,
objective_at_initial_simplex=None,
objective_at_initial_vertex=None,
batch_evaluate_objective=False,
func_tolerance=1e-08,
position_tolerance=1e-08,
parallel_iterations=1,
max_iterations=None,
reflection=None,
expansion=None,
contraction=None,
shrinkage=None,
name=None
)
Used in the notebooks
Performs an unconstrained minimization of a (possibly non-smooth) function
using the Nelder Mead simplex method. Nelder Mead method does not support
univariate functions. Hence the dimensions of the domain must be 2 or greater.
For details of the algorithm, see
[Press, Teukolsky, Vetterling and Flannery(2007)][1].
Points in the domain of the objective function may be represented as a
Tensor of general shape but with rank at least 1. The algorithm proceeds
by modifying a full rank simplex in the domain. The initial simplex may
either be specified by the user or can be constructed using a single vertex
supplied by the user. In the latter case, if v0 is the supplied vertex,
the simplex is the convex hull of the set:
S = {v0} + {v0 + step_i * e_i}
Here e_i is a vector which is 1 along the i-th axis and zero elsewhere
and step_i is a characteristic length scale along the i-th axis. If the
step size is not supplied by the user, a unit step size is used in every axis.
Alternately, a single step size may be specified which is used for every
axis. The most flexible option is to supply a bespoke step size for every
axis.
Usage:
The following example demonstrates the usage of the Nelder Mead minimzation
on a two dimensional problem with the minimum located at a non-differentiable
point.
# The objective function
def sqrt_quadratic(x):
return tf.sqrt(tf.reduce_sum(x ** 2, axis=-1))
start = tf.constant([6.0, -21.0]) # Starting point for the search.
optim_results = tfp.optimizer.nelder_mead_minimize(
sqrt_quadratic, initial_vertex=start, func_tolerance=1e-8,
batch_evaluate_objective=True)
# Check that the search converged
assert(optim_results.converged)
# Check that the argmin is close to the actual value.
np.testing.assert_allclose(optim_results.position, np.array([0.0, 0.0]),
atol=1e-7)
# Print out the total number of function evaluations it took.
print("Function evaluations: %d" % optim_results.num_objective_evaluations)
References:
[1]: William Press, Saul Teukolsky, William Vetterling and Brian Flannery.
Numerical Recipes in C++, third edition. pp. 502-507. (2007).
http://numerical.recipes/cpppages/chap0sel.pdf
[2]: Jeffrey Lagarias, James Reeds, Margaret Wright and Paul Wright.
Convergence properties of the Nelder-Mead simplex method in low dimensions,
Siam J. Optim., Vol 9, No. 1, pp. 112-147. (1998).
http://www.math.kent.edu/~reichel/courses/Opt/reading.material.2/nelder.mead.pdf
[3]: Fuchang Gao and Lixing Han. Implementing the Nelder-Mead simplex
algorithm with adaptive parameters. Computational Optimization and
Applications, Vol 51, Issue 1, pp 259-277. (2012).
https://pdfs.semanticscholar.org/15b4/c4aa7437df4d032c6ee6ce98d6030dd627be.pdf
Args |
|---|
objective_function
|
A Python callable that accepts a point as a
real Tensor and returns a Tensor of real dtype containing
the value of the function at that point. The function
to be minimized. If batch_evaluate_objective is True, the callable
may be evaluated on a Tensor of shape [n+1] + s where n is
the dimension of the problem and s is the shape of a single point
in the domain (so n is the size of a Tensor representing a
single point).
In this case, the expected return value is a Tensor of shape [n+1].
Note that this method does not support univariate functions so the problem
dimension n must be strictly greater than 1.
|
initial_simplex
|
(Optional) Tensor of real dtype. The initial simplex to
start the search. If supplied, should be a Tensor of shape [n+1] + s
where n is the dimension of the problem and s is the shape of a
single point in the domain. Each row (i.e. the Tensor with a given
value of the first index) is interpreted as a vertex of a simplex and
hence the rows must be affinely independent. If not supplied, an axes
aligned simplex is constructed using the initial_vertex and
step_sizes. Only one and at least one of initial_simplex and
initial_vertex must be supplied.
|
initial_vertex
|
(Optional) Tensor of real dtype and any shape that can
be consumed by the objective_function. A single point in the domain that
will be used to construct an axes aligned initial simplex.
|
step_sizes
|
(Optional) Tensor of real dtype and shape broadcasting
compatible with initial_vertex. Supplies the simplex scale along each
axes. Only used if initial_simplex is not supplied. See description
above for details on how step sizes and initial vertex are used to
construct the initial simplex.
|
objective_at_initial_simplex
|
(Optional) Rank 1 Tensor of real dtype
of a rank 1 Tensor. The value of the objective function at the
initial simplex. May be supplied only if initial_simplex is
supplied. If not supplied, it will be computed.
|
objective_at_initial_vertex
|
(Optional) Scalar Tensor of real dtype. The
value of the objective function at the initial vertex. May be supplied
only if the initial_vertex is also supplied.
|
batch_evaluate_objective
|
(Optional) Python bool. If True, the objective
function will be evaluated on all the vertices of the simplex packed
into a single tensor. If False, the objective will be mapped across each
vertex separately. Evaluating the objective function in a batch allows
use of vectorization and should be preferred if the objective function
allows it.
|
func_tolerance
|
(Optional) Scalar Tensor of real dtype. The algorithm
stops if the absolute difference between the largest and the smallest
function value on the vertices of the simplex is below this number.
|
position_tolerance
|
(Optional) Scalar Tensor of real dtype. The
algorithm stops if the largest absolute difference between the
coordinates of the vertices is below this threshold.
|
parallel_iterations
|
(Optional) Positive integer. The number of iterations
allowed to run in parallel.
|
max_iterations
|
(Optional) Scalar positive Tensor of dtype int32.
The maximum number of iterations allowed. If None then no limit is
applied.
|
reflection
|
(Optional) Positive Scalar Tensor of same dtype as
initial_vertex. This parameter controls the scaling of the reflected
vertex. See, [Press et al(2007)][1] for details. If not specified,
uses the dimension dependent prescription of [Gao and Han(2012)][3].
|
expansion
|
(Optional) Positive Scalar Tensor of same dtype as
initial_vertex. Should be greater than 1 and reflection. This
parameter controls the expanded scaling of a reflected vertex.
See, [Press et al(2007)][1] for details. If not specified, uses the
dimension dependent prescription of [Gao and Han(2012)][3].
|
contraction
|
(Optional) Positive scalar Tensor of same dtype as
initial_vertex. Must be between 0 and 1. This parameter controls
the contraction of the reflected vertex when the objective function at
the reflected point fails to show sufficient decrease.
See, [Press et al(2007)][1] for more details. If not specified, uses
the dimension dependent prescription of [Gao and Han(2012][3].
|
shrinkage
|
(Optional) Positive scalar Tensor of same dtype as
initial_vertex. Must be between 0 and 1. This parameter is the scale
by which the simplex is shrunk around the best point when the other
steps fail to produce improvements.
See, [Press et al(2007)][1] for more details. If not specified, uses
the dimension dependent prescription of [Gao and Han(2012][3].
|
name
|
(Optional) Python str. The name prefixed to the ops created by this
function. If not supplied, the default name 'minimize' is used.
|
Returns |
|---|
optimizer_results
|
A namedtuple containing the following items:
converged: Scalar boolean tensor indicating whether the minimum was
found within tolerance.
num_objective_evaluations: The total number of objective
evaluations performed.
position: A Tensor containing the last argument value found
during the search. If the search converged, then
this value is the argmin of the objective function.
objective_value: A tensor containing the value of the objective
function at the position. If the search
converged, then this is the (local) minimum of
the objective function.
final_simplex: The last simplex constructed before stopping.
final_objective_values: The objective function evaluated at the
vertices of the final simplex.
initial_simplex: The starting simplex.
initial_objective_values: The objective function evaluated at the
vertices of the initial simplex.
num_iterations: The number of iterations of the main algorithm body.
|
Raises |
|---|
ValueError
|
If any of the following conditions hold
- If none or more than one of
initial_simplex and initial_vertex are
supplied.
- If
initial_simplex and step_sizes are both specified.
|
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