Plugin type: minimizer/singlecost
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## Plugin type: minimizer/singlecost

These plug-ins provide optimizers of many-to-one functions

#### Plugins:

gdas gdsq gsl nlopt

##### gdas

Gradient descent with automatic step size correction.. Supported parameters are:

Name | Type | Default | Description
--- | --- | --- | ---
ftolr | double in [0, inf) | 0 | Stop if the relative change of the criterion is below.
max-step | double in (0, inf) | 2 | Maximal absolute step size
maxiter | uint in [1, inf) | 200 | Stopping criterion: the maximum number of iterations
min-step | double in (0, inf) | 0.1 | Minimal absolute step size
xtola | double in [0, inf) | 0.01 | Stop if the inf-norm of the change applied to x is below this value.

##### gdsq

Gradient descent with quadratic step estimation. Supported parameters are:

Name | Type | Default | Description
--- | --- | --- | ---
ftolr | double in [0, inf) | 0 | Stop if the relative change of the criterion is below.
gtola | double in [0, inf) | 0 | Stop if the inf-norm of the gradient is below this value.
maxiter | uint in [1, inf) | 100 | Stopping criterion: the maximum number of iterations
scale | double in (1, inf) | 2 | Fallback fixed step size scaling
step | double in (0, inf) | 0.1 | Initial step size
xtola | double in [0, inf) | 0 | Stop if the inf-norm of x-update is below this value.

##### gsl

optimizer plugin based on the multimin optimizers of the GNU Scientific Library (GSL) https://www.gnu.org/software/gsl/. Supported parameters are:

Name | Type | Default | Description
--- | --- | --- | ---
eps | double in (0, inf) | 0.01 | gradient based optimizers: stop when |grad| < eps, simplex: stop when simplex size < eps.
iter | uint in [1, inf) | 100 | maximum number of iterations

opt

dict

gd

Specific optimizer to be used.
simplex: | Simplex algorithm of Nelder and Mead
cg-fr: | Flecher-Reeves conjugate gradient algorithm
cg-pr: | Polak-Ribiere conjugate gradient algorithm
bfgs: | Broyden-Fletcher-Goldfarb-Shann
bfgs2: | Broyden-Fletcher-Goldfarb-Shann (most efficient version)
gd: | Gradient descent.

step | double in (0, inf) | 0.001 | initial step size
tol | double in (0, inf) | 0.1 | some tolerance parameter

##### nlopt

Minimizer algorithms using the NLOPT library, for a description of the optimizers please see 'http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms'. Supported parameters are:

Name | Type | Default | Description
--- | --- | --- | ---
ftola | double in [0, inf) | 0 | Stopping criterion: the absolute change of the objective value is below this value
ftolr | double in [0, inf) | 0 | Stopping criterion: the relative change of the objective value is below this value
higher | double | inf | Higher boundary (equal for all parameters)

local-opt

dict

none

local minimization algorithm that may be required for the main minimization algorithm.
gn-direct: | Dividing Rectangles
gn-direct-l: | Dividing Rectangles (locally biased)
gn-direct-l-rand: | Dividing Rectangles (locally biased, randomized)
gn-direct-noscal: | Dividing Rectangles (unscaled)
gn-direct-l-noscal: | Dividing Rectangles (unscaled, locally biased)
gn-direct-l-rand-noscale: | Dividing Rectangles (unscaled, locally biased, randomized)
gn-orig-direct: | Dividing Rectangles (original implementation)
gn-orig-direct-l: | Dividing Rectangles (original implementation, locally biased)
ld-lbfgs-nocedal: |
ld-lbfgs: | Low-storage BFGS
ln-praxis: | Gradient-free Local Optimization via the Principal-Axis Method
ld-var1: | Shifted Limited-Memory Variable-Metric, Rank 1
ld-var2: | Shifted Limited-Memory Variable-Metric, Rank 2
ld-tnewton: | Truncated Newton
ld-tnewton-restart: | Truncated Newton with steepest-descent restarting
ld-tnewton-precond: | Preconditioned Truncated Newton
ld-tnewton-precond-restart: | Preconditioned Truncated Newton with steepest-descent restarting
gn-crs2-lm: | Controlled Random Search with Local Mutation
ld-mma: | Method of Moving Asymptotes
ln-cobyla: | Constrained Optimization BY Linear Approximation
ln-newuoa: | Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
ln-newuoa-bound: | Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
ln-neldermead: | Nelder-Mead simplex algorithm
ln-sbplx: | Subplex variant of Nelder-Mead
ln-bobyqa: | Derivative-free Bound-constrained Optimization
gn-isres: | Improved Stochastic Ranking Evolution Strategy
none: | don't specify algorithm

lower | double | -inf | Lower boundary (equal for all parameters)
maxiter | int in [1, inf) | 100 | Stopping criterion: the maximum number of iterations

opt

dict

ld-lbfgs

main minimization algorithm
gn-direct: | Dividing Rectangles
gn-direct-l: | Dividing Rectangles (locally biased)
gn-direct-l-rand: | Dividing Rectangles (locally biased, randomized)
gn-direct-noscal: | Dividing Rectangles (unscaled)
gn-direct-l-noscal: | Dividing Rectangles (unscaled, locally biased)
gn-direct-l-rand-noscale: | Dividing Rectangles (unscaled, locally biased, randomized)
gn-orig-direct: | Dividing Rectangles (original implementation)
gn-orig-direct-l: | Dividing Rectangles (original implementation, locally biased)
ld-lbfgs-nocedal: |
ld-lbfgs: | Low-storage BFGS
ln-praxis: | Gradient-free Local Optimization via the Principal-Axis Method
ld-var1: | Shifted Limited-Memory Variable-Metric, Rank 1
ld-var2: | Shifted Limited-Memory Variable-Metric, Rank 2
ld-tnewton: | Truncated Newton
ld-tnewton-restart: | Truncated Newton with steepest-descent restarting
ld-tnewton-precond: | Preconditioned Truncated Newton
ld-tnewton-precond-restart: | Preconditioned Truncated Newton with steepest-descent restarting
gn-crs2-lm: | Controlled Random Search with Local Mutation
ld-mma: | Method of Moving Asymptotes
ln-cobyla: | Constrained Optimization BY Linear Approximation
ln-newuoa: | Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
ln-newuoa-bound: | Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
ln-neldermead: | Nelder-Mead simplex algorithm
ln-sbplx: | Subplex variant of Nelder-Mead
ln-bobyqa: | Derivative-free Bound-constrained Optimization
gn-isres: | Improved Stochastic Ranking Evolution Strategy
auglag: | Augmented Lagrangian algorithm
auglag-eq: | Augmented Lagrangian algorithm with equality constraints only
g-mlsl: | Multi-Level Single-Linkage (require local optimization and bounds)
g-mlsl-lds: | Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds)
ld-slsqp: | Sequential Least-Squares Quadratic Programming

step | double in [0, inf) | 0 | Initial step size for gradient free methods
stop | double | -inf | Stopping criterion: function value falls below this value
xtola | double in [0, inf) | 0 | Stopping criterion: the absolute change of all x-values is below this value
xtolr | double in [0, inf) | 0 | Stopping criterion: the relative change of all x-values is below this value

#### Plugin consumers:

mia-2dgroundtruthreg mia-2dimageregistration mia-2dmultiimageregistration mia-2dmyoica-full mia-2dmyoica-nonrigid mia-2dmyoica-nonrigid2 mia-2dmyoica-nonrigid-parallel mia-2dmyoicapgt mia-2dmyomilles mia-2dmyoperiodic-nonrigid mia-2dmyopgt-nonrigid mia-2dmyoserial-nonrigid mia-3dnonrigidreg mia-3dnonrigidreg-alt mia-3dprealign-nonrigid mia-3drigidreg mia-3dserial-nonrigid

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