New version: Manopt v0.4.64

[JuliaRegistrator]

• Registering package: Manopt
• Repository: https://github.com/JuliaManifolds/Manopt.jl
• Created by: @kellertuer
• Version: v0.4.64
• Commit: 2943e0d7098bfef31ef25411ada25fbe90d773b5
• Reviewed by: @kellertuer
• Reference: JuliaManifolds/Manopt.jl@2943e0d#commitcomment-142927796
• Description: 🏔️Manopt. jl – Optimization on Manifolds in Julia
• Release notes:

### Added

* Remodel the constraints and their gradients into separate `VectorGradientFunctions`
  to reduce code duplication and encapsulate the inner model of these functions and their gradients
* Introduce a `ConstrainedManoptProblem` to model different ranges for the gradients in the
  new `VectorGradientFunction`s beyond the default `NestedPowerRepresentation`
* introduce a `VectorHessianFunction` to also model that one can provide the vector of Hessians
  to constraints
* introduce a more flexible indexing beyond single indexing, to also include arbitrary ranges
  when accessing vector functions and their gradients and hence also for constraints and
  their gradients.

### Changed

* Remodel `ConstrainedManifoldObjective` to store an `AbstractManifoldObjective`
  internally instead of directly `f` and `grad_f`, allowing also Hessian objectives
  therein and implementing access to this Hessian
* Fixed a bug that Lanczos produced NaNs when started exactly in a minimizer, since we divide by the gradient norm.

### Deprecated

* deprecate `get_grad_equality_constraints(M, o, p)`, use `get_grad_equality_constraint(M, o, p, :)`
  from the more flexible indexing instead.

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