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From "Gilles (JIRA)" <>
Subject [jira] [Updated] (MATH-874) New API for optimizers
Date Tue, 04 Dec 2012 16:21:58 GMT


Gilles updated MATH-874:

    Fix Version/s: 3.1

In a [discussion on the "dev" ML|], one point
raised again was the odd naming of the subpackages under "o.a.c.m.optimization".

Although no agreement was reached there about how the layout should look like, I propose to
create one based on the immediate API similarities (i.e. what the caller is required as input
to the "optimize" methods). Thus the package name and contents of "direct" and "general" would
be reorganized as follows:
* the first sub-package ("scalar", "vector") indicates the kind of return value of the (objective
or model) function argument passed to "optimize", and
* the second sub-package ("noderiv", "gradient", "jacobian") indicates the kind of return
value of a second function argument passed to "optimize" referring to the need to provide
derivatives or not.

I'm still wondering whether we should add another level that would accommodate the "linear"
sub-package e.g.:
optim.nonlinear.scalar. ...
optim.nonlinear.vector. ...

And where should the "fitting" sub-package go?
I could be viewed as a "client" of the "optim" package and since "PolynomialFitter" also uses
classes from another package, I'd suggest to create a top-level (i.e. under "o.a.c.math3")
package for it.

> New API for optimizers
> ----------------------
>                 Key: MATH-874
>                 URL:
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 3.0
>            Reporter: Gilles
>            Assignee: Gilles
>            Priority: Minor
>              Labels: api-change
>             Fix For: 3.1, 4.0
>         Attachments: optimizers.patch
> I suggest to change the signatures of the "optimize" methods in
> * {{UnivariateOptimizer}}
> * {{MultivariateOptimizer}}
> * {{MultivariateDifferentiableOptimizer}}
> * {{MultivariateDifferentiableVectorOptimizer}}
> * {{BaseMultivariateSimpleBoundsOptimizer}}
> Currently, the arguments are
> * the allowed number of evaluations of the objective function
> * the objective function
> * the type of optimization (minimize or maximize)
> * the initial guess
> * optionally, the lower and upper bounds
> A marker interface:
> {code}
> public interface OptimizationData {}
> {code}
> would in effect be implemented by all input data so that the signature would become (for
> {code}
> public PointValuePair optimize(MultivariateFunction f,
>                                OptimizationData... optData);
> {code}
> A [thread|] was started on the "dev" ML.
> Initially, this proposal aimed at avoiding to call some optimizer-specific methods. An
example is the "setSimplex" method in "": it must
be called before the call to "optimize". Not only this departs form the common API, but the
definition of the simplex also fixes the dimension of the problem; hence it would be more
natural to pass it together with the other parameters (i.e. in "optimize") that are also dimension-dependent
(initial guess, bounds).
> Eventually, the API will be simpler: users will
> # construct an optimizer (passing dimension-independent parameters at construction),
> # call "optimize" (passing any dimension-dependent parameters).

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