bfgs_method¶
-
odl.solvers.smooth.newton.bfgs_method(f, x, line_search=1.0, maxiter=1000, tol=1e-15, num_store=None, hessinv_estimate=None, callback=None)[source]¶ Quasi-Newton BFGS method to minimize a differentiable function.
Can use either the regular BFGS method, or the limited memory BFGS method.
- Parameters
- f
Functional Functional with
f.gradient.- x
f.domainelement Starting point of the iteration
- line_searchfloat or
LineSearch, optional Strategy to choose the step length. If a float is given, uses it as a fixed step length.
- maxiterint, optional
Maximum number of iterations.
- tolfloat, optional
Tolerance that should be used for terminating the iteration.
- num_storeint, optional
Maximum number of correction factors to store. For
None, the method is the regular BFGS method. For an integer, the method becomes the Limited Memory BFGS method.- hessinv_estimate
Operator, optional Initial estimate of the inverse of the Hessian operator. Needs to be an operator from
f.domaintof.domain. Default: Identity onf.domain- callbackcallable, optional
Object executing code per iteration, e.g. plotting each iterate.
- x
- f
Goldfarb%E2%80%93Shanno_algorithm>`_