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Changelog

All notable Changes to the Julia package Manopt.jl will be documented in this file. The file was started with Version 0.4.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.5.5] - unreleased

Added

  • icons upfront external links when they link to another package or wikipedia.

[0.5.4] - November 27, 2024

Added

  • An automated detection whether the tutorials are present if not an also no quarto run is done, an automated --exlcude-tutorials option is added.
  • Support for ManifoldDiff 0.4

[0.5.3] – October 18, 2024

Added

  • StopWhenChangeLess, StopWhenGradientChangeLess and StopWhenGradientLess can now use the new idea (ManifoldsBase.jl 0.15.18) of different outer norms on manifolds with components like power and product manifolds and all others that support this from the Manifolds.jl Library, like Euclidean

Changed

  • stabilize max_Stepzise to also work when injectivity_radius dos not exist. It however would warn new users, that activate tutorial mode.
  • Start a ManoptTestSuite subpackage to store dummy types and common test helpers in.

[0.5.2] – October 5, 2024

Added

  • three new symbols to easier state to record the :Gradient, the :GradientNorm, and the :Stepsize.

Changed

[0.5.1] – September 4, 2024

Changed

  • slightly improves the test for the ExponentialFamilyProjection text on the about page.

Added

  • the proximal_point method.

[0.5.0] – August 29, 2024

This breaking update is mainly concerned with improving a unified experience through all solvers and some usability improvements, such that for example the different gradient update rules are easier to specify.

In general we introduce a few factories, that avoid having to pass the manifold to keyword arguments

Added

  • A ManifoldDefaultsFactory that postpones the creation/allocation of manifold-specific fields in for example direction updates, step sizes and stopping criteria. As a rule of thumb, internal structures, like a solver state should store the final type. Any high-level interface, like the functions to start solvers, should accept such a factory in the appropriate places and call the internal _produce_type(factory, M), for example before passing something to the state.
  • a documentation_glossary.jl file containing a glossary of often used variables in fields, arguments, and keywords, to print them in a unified manner. The same for usual sections, tex, and math notation that is often used within the doc-strings.

Changed

  • Any Stepsize now hase a Stepsize struct used internally as the original structs before. The newly exported terms aim to fit stepsize=... in naming and create a ManifoldDefaultsFactory instead, so that any stepsize can be created without explicitly specifying the manifold.
    • ConstantStepsize is no longer exported, use ConstantLength instead. The length parameter is now a positional argument following the (optonal) manifold. Besides that ConstantLength works as before,just that omitting the manifold fills the one specified in the solver now.
    • DecreasingStepsize is no longer exported, use DecreasingLength instead. ConstantLength works as before,just that omitting the manifold fills the one specified in the solver now.
    • ArmijoLinesearch is now called ArmijoLinesearchStepsize. ArmijoLinesearch works as before,just that omitting the manifold fills the one specified in the solver now.
    • WolfePowellLinesearch is now called WolfePowellLinesearchStepsize, its constant c_1 is now unified with Armijo and called sufficient_decrease, c_2 was renamed to sufficient_curvature. Besides that, WolfePowellLinesearch works as before, just that omitting the manifold fills the one specified in the solver now.
    • WolfePowellBinaryLinesearch is now called WolfePowellBinaryLinesearchStepsize, its constant c_1 is now unified with Armijo and called sufficient_decrease, c_2 was renamed to sufficient_curvature. Besides that, WolfePowellBinaryLinesearch works as before, just that omitting the manifold fills the one specified in the solver now.
    • NonmonotoneLinesearch is now called NonmonotoneLinesearchStepsize. NonmonotoneLinesearch works as before, just that omitting the manifold fills the one specified in the solver now.
    • AdaptiveWNGradient is now called AdaptiveWNGradientStepsize. Its second positional argument, the gradient function was only evaluated once for the gradient_bound default, so it has been replaced by the keyword X= accepting a tangent vector. The last positional argument p has also been moved to a keyword argument. Besides that, AdaptiveWNGradient works as before, just that omitting the manifold fills the one specified in the solver now.
  • Any DirectionUpdateRule now has the Rule in its name, since the original name is used to create the ManifoldDefaultsFactory instead. The original constructor now no longer requires the manifold as a parameter, that is later done in the factory. The Rule is, however, also no longer exported.
    • AverageGradient is now called AverageGradientRule. AverageGradient works as before, but the manifold as its first parameter is no longer necessary and p is now a keyword argument.
    • The IdentityUpdateRule now accepts a manifold optionally for consistency, and you can use Gradient() for short as well as its factory. Hence direction=Gradient() is now available.
    • MomentumGradient is now called MomentumGradientRule. MomentumGradient works as before, but the manifold as its first parameter is no longer necessary and p is now a keyword argument.
    • Nesterov is now called NesterovRule. Nesterov works as before, but the manifold as its first parameter is no longer necessary and p is now a keyword argument.
    • ConjugateDescentCoefficient is now called ConjugateDescentCoefficientRule. ConjugateDescentCoefficient works as before, but can now use the factory in between
    • the ConjugateGradientBealeRestart is now called ConjugateGradientBealeRestartRule. For the ConjugateGradientBealeRestart the manifold is now a first parameter, that is not necessary and no longer the manifold= keyword.
    • DaiYuanCoefficient is now called DaiYuanCoefficientRule. For the DaiYuanCoefficient the manifold as its first parameter is no longer necessary and the vector transport has been unified/moved to the vector_transport_method= keyword.
    • FletcherReevesCoefficient is now called FletcherReevesCoefficientRule. FletcherReevesCoefficient works as before, but can now use the factory in between
    • HagerZhangCoefficient is now called HagerZhangCoefficientRule. For the HagerZhangCoefficient the manifold as its first parameter is no longer necessary and the vector transport has been unified/moved to the vector_transport_method= keyword.
    • HestenesStiefelCoefficient is now called HestenesStiefelCoefficientRule. For the HestenesStiefelCoefficient the manifold as its first parameter is no longer necessary and the vector transport has been unified/moved to the vector_transport_method= keyword.
    • LiuStoreyCoefficient is now called LiuStoreyCoefficientRule. For the LiuStoreyCoefficient the manifold as its first parameter is no longer necessary and the vector transport has been unified/moved to the vector_transport_method= keyword.
    • PolakRibiereCoefficient is now called PolakRibiereCoefficientRule. For the PolakRibiereCoefficient the manifold as its first parameter is no longer necessary and the vector transport has been unified/moved to the vector_transport_method= keyword.
    • the SteepestDirectionUpdateRule is now called SteepestDescentCoefficientRule. The SteepestDescentCoefficient is equivalent, but creates the new factory interims wise.
    • AbstractGradientGroupProcessor is now called AbstractGradientGroupDirectionRule
      • the StochasticGradient is now called StochasticGradientRule. The StochasticGradient is equivalent, but creates the new factory interims wise, so that the manifold is not longer necessary.
    • the AlternatingGradient is now called AlternatingGradientRule. The AlternatingGradient is equivalent, but creates the new factory interims wise, so that the manifold is not longer necessary.
  • quasi_Newton had a keyword scale_initial_operator= that was inconsistently declared (sometimes bool, sometimes real) and was unused. It is now called initial_scale=1.0 and scales the initial (diagonal, unit) matrix within the approximation of the Hessian additionally to the $\frac{1}{\lVert g_k\rVert}$ scaling with the norm of the oldest gradient for the limited memory variant. For the full matrix variant the initial identity matrix is now scaled with this parameter.
  • Unify doc strings and presentation of keyword arguments
    • general indexing, for example in a vector, uses i
    • index for inequality constraints is unified to i running from 1,...,m
    • index for equality constraints is unified to j running from 1,...,n
    • iterations are using now k
  • get_manopt_parameter has been renamed to get_parameter since it is internal, so internally that is clear; accessing it from outside hence reads anyways Manopt.get_parameter
  • set_manopt_parameter! has been renamed to set_parameter! since it is internal, so internally that is clear; accessing it from outside hence reads Manopt.set_parameter!
  • changed the stabilize::Bool= keyword in quasi_Newton to the more flexible project!= keyword, this is also more in line with the other solvers. Internally the same is done within the QuasiNewtonLimitedMemoryDirectionUpdate. To adapt,
    • the previous stabilize=true is now set with (project!)=embed_project! in general, and if the manifold is represented by points in the embedding, like the sphere, (project!)=project! suffices
    • the new default is (project!)=copyto!, so by default no projection/stabilization is performed.
  • the positional argument p (usually the last or the third to last if subsolvers existed) has been moved to a keyword argument p= in all State constructors
  • in NelderMeadState the population moved from positional to keyword argument as well,
  • the way to initialise sub solvers in the solver states has been unified In the new variant
    • the sub_problem is always a positional argument; namely the last one
    • if the sub_state is given as a optional positional argument after the problem, it has to be a manopt solver state
    • you can provide the new ClosedFormSolverState(e::AbstractEvaluationType) for the state to indicate that the sub_problem is a closed form solution (function call) and how it has to be called
    • if you do not provide the sub_state as positional, the keyword evaluation= is used to generate the state ClosedFormSolverState.
    • when previously p and eventually X where positional arguments, they are now moved to keyword arguments of the same name for start point and tangent vector.
    • in detail
      • AdaptiveRegularizationState(M, sub_problem [, sub_state]; kwargs...) replaces the (anyways unused) variant to only provide the objective; both X and p moved to keyword arguments.
      • AugmentedLagrangianMethodState(M, objective, sub_problem; evaluation=...) was added
      • ``AugmentedLagrangianMethodState(M, objective, sub_problem, sub_state; evaluation=...)now hasp=rand(M)` as keyword argument instead of being the second positional one
      • ExactPenaltyMethodState(M, sub_problem; evaluation=...) was added and ExactPenaltyMethodState(M, sub_problem, sub_state; evaluation=...) now has p=rand(M) as keyword argument instead of being the second positional one
      • DifferenceOfConvexState(M, sub_problem; evaluation=...) was added and DifferenceOfConvexState(M, sub_problem, sub_state; evaluation=...) now has p=rand(M) as keyword argument instead of being the second positional one
      • DifferenceOfConvexProximalState(M, sub_problem; evaluation=...) was added and DifferenceOfConvexProximalState(M, sub_problem, sub_state; evaluation=...) now has p=rand(M) as keyword argument instead of being the second positional one
    • bumped Manifolds.jlto version 0.10; this mainly means that any algorithm working on a productmanifold and requiring ArrayPartition now has to explicitly do using RecursiveArrayTools.

Fixed

  • the AverageGradientRule filled its internal vector of gradients wrongly – or mixed it up in parallel transport. This is now fixed.

Removed

  • the convex_bundle_method and its ConvexBundleMethodState no longer accept the keywords k_size, p_estimate nor ϱ, they are superseded by just providing k_max.
  • the truncated_conjugate_gradient_descent(M, f, grad_f, hess_f) has the Hessian now a mandatory argument. To use the old variant, provide ApproxHessianFiniteDifference(M, copy(M, p), grad_f) to hess_f directly.
  • all deprecated keyword arguments and a few function signatures were removed:
    • get_equality_constraints, get_equality_constraints!, get_inequality_constraints, get_inequality_constraints! are removed. Use their singular forms and set the index to : instead.
    • StopWhenChangeLess(ε) is removed, use ``StopWhenChangeLess(M, ε)` instead to fill for example the retraction properly used to determine the change
  • In the WolfePowellLinesearch and WolfeBinaryLinesearchthe linesearch_stopsize= keyword is replaced by stop_when_stepsize_less=
  • DebugChange and RecordChange had a manifold= and a invretr keyword that were replaced by the first positional argument M and inverse_retraction_method=, respectively
  • in the NonlinearLeastSquaresObjective and LevenbergMarquardt the jacB= keyword is now called jacobian_tangent_basis=
  • in particle_swarm the n= keyword is replaced by swarm_size=.
  • update_stopping_criterion! has been removed and unified with set_parameter!. The code adaptions are
    • to set a parameter of a stopping criterion, just replace update_stopping_criterion!(sc, :Val, v) with set_parameter!(sc, :Val, v)
    • to update a stopping criterion in a solver state, replace the old update_stopping_criterion!(state, :Val, v) tat passed down to the stopping criterion by the explicit pass down with set_parameter!(state, :StoppingCriterion, :Val, v)

[0.4.69] – August 3, 2024

Changed

  • Improved performance of Interior Point Newton Method.

[0.4.68] – August 2, 2024

Added

  • an Interior Point Newton Method, the interior_point_newton
  • a conjugate_residual Algorithm to solve a linear system on a tangent space.
  • ArmijoLinesearch now allows for additional additional_decrease_condition and additional_increase_condition keywords to add further conditions to accept additional conditions when to accept an decreasing or increase of the stepsize.
  • add a DebugFeasibility to have a debug print about feasibility of points in constrained optimisation employing the new is_feasible function
  • add a InteriorPointCentralityCondition check that can be added for step candidates within the line search of interior_point_newton
  • Add Several new functors
    • the LagrangianCost, LagrangianGradient, LagrangianHessian, that based on a constrained objective allow to construct the hessian objective of its Lagrangian
    • the CondensedKKTVectorField and its CondensedKKTVectorFieldJacobian, that are being used to solve a linear system within interior_point_newton
    • the KKTVectorField as well as its KKTVectorFieldJacobian and ``KKTVectorFieldAdjointJacobian`
    • the KKTVectorFieldNormSq and its KKTVectorFieldNormSqGradient used within the Armijo line search of interior_point_newton
  • New stopping criteria
    • A StopWhenRelativeResidualLess for the conjugate_residual
    • A StopWhenKKTResidualLess for the interior_point_newton

[0.4.67] – July 25, 2024

Added

  • max_stepsize methods for Hyperrectangle.

Fixed

  • a few typos in the documentation
  • WolfePowellLinesearch no longer uses max_stepsize with invalid point by default.

[0.4.66] June 27, 2024

Changed

  • Remove functions estimate_sectional_curvature, ζ_1, ζ_2, close_point from convex_bundle_method
  • Remove some unused fields and arguments such as p_estimate, ϱ, α, from ConvexBundleMethodState in favor of jut k_max
  • Change parameter R placement in ProximalBundleMethodState to fifth position

[0.4.65] June 13, 2024

Changed

  • refactor stopping criteria to not store a sc.reason internally, but instead only generate the reason (and hence allocate a string) when actually asked for a reason.

[0.4.64] June 4, 2024

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 VectorGradientFunctions 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.

[0.4.63] May 11, 2024

Added

  • :reinitialize_direction_update option for quasi-Newton behavior when the direction is not a descent one. It is now the new default for QuasiNewtonState.
  • Quasi-Newton direction update rules are now initialized upon start of the solver with the new internal function initialize_update!.

Fixed

  • ALM and EPM no longer keep a part of the quasi-Newton subsolver state between runs.

Changed

  • Quasi-Newton solvers: :reinitialize_direction_update is the new default behavior in case of detection of non-descent direction instead of :step_towards_negative_gradient. :step_towards_negative_gradient is still available when explicitly set using the nondescent_direction_behavior keyword argument.

[0.4.62] May 3, 2024

Changed

  • bumped dependency of ManifoldsBase.jl to 0.15.9 and imported their numerical verify functions. This changes the throw_error keyword used internally to a error= with a symbol.

[0.4.61] April 27, 2024

Added

  • Tests use Aqua.jl to spot problems in the code
  • introduce a feature-based list of solvers and reduce the details in the alphabetical list
  • adds a PolyakStepsize
  • added a get_subgradient for AbstractManifoldGradientObjectives since their gradient is a special case of a subgradient.

Fixed

  • get_last_stepsize was defined in quite different ways that caused ambiguities. That is now internally a bit restructured and should work nicer. Internally this means that the interim dispatch on get_last_stepsize(problem, state, step, vars...) was removed. Now the only two left are get_last_stepsize(p, s, vars...) and the one directly checking get_last_stepsize(::Stepsize) for stored values.
  • the accidentally exported set_manopt_parameter! is no longer exported

Changed

  • get_manopt_parameter and set_manopt_parameter! have been revised and better documented, they now use more semantic symbols (with capital letters) instead of direct field access (lower letter symbols). Since these are not exported, this is considered an internal, hence non-breaking change.
    • semantic symbols are now all nouns in upper case letters
    • :active is changed to :Activity

[0.4.60] April 10, 2024

Added

  • RecordWhenActive to allow records to be deactivated during runtime, symbol :WhenActive
  • RecordSubsolver to record the result of a subsolver recording in the main solver, symbol :Subsolver
  • RecordStoppingReason to record the reason a solver stopped
  • made the RecordFactory more flexible and quite similar to DebugFactory, such that it is now also easy to specify recordings at the end of solver runs. This can especially be used to record final states of sub solvers.

Changed

  • being a bit more strict with internal tools and made the factories for record non-exported, so this is the same as for debug.

Fixed

  • The name :Subsolver to generate DebugWhenActive was misleading, it is now called :WhenActive referring to “print debug only when set active, that is by the parent (main) solver”.
  • the old version of specifying Symbol => RecordAction for later access was ambiguous, since it could also mean to store the action in the dictionary under that symbol. Hence the order for access was switched to RecordAction => Symbol to resolve that ambiguity.

[0.4.59] April 7, 2024

Added

  • A Riemannian variant of the CMA-ES (Covariance Matrix Adaptation Evolutionary Strategy) algorithm, cma_es.

Fixed

  • The constructor dispatch for StopWhenAny with Vector had incorrect element type assertion which was fixed.

[0.4.58] March 18, 2024

Added

  • more advanced methods to add debug to the beginning of an algorithm, a step, or the end of the algorithm with DebugAction entries at :Start, :BeforeIteration, :Iteration, and :Stop, respectively.
  • Introduce a Pair-based format to add elements to these hooks, while all others ar now added to :Iteration (no longer to :All)
  • (planned) add an easy possibility to also record the initial stage and not only after the first iteration.

Changed

  • Changed the symbol for the :Step dictionary to be :Iteration, to unify this with the symbols used in recording, and removed the :All symbol. On the fine granular scale, all but :Start debugs are now reset on init. Since these are merely internal entries in the debug dictionary, this is considered non-breaking.
  • introduce a StopWhenSwarmVelocityLess stopping criterion for particle_swarm replacing the current default of the swarm change, since this is a bit more effective to compute

Fixed

  • fixed the outdated documentation of TruncatedConjugateGradientState, that now correctly state that p is no longer stored, but the algorithm runs on TpM.
  • implemented the missing get_iterate for TruncatedConjugateGradientState.

[0.4.57] March 15, 2024

Changed

  • convex_bundle_method uses the sectional_curvature from ManifoldsBase.jl.
  • convex_bundle_method no longer has the unused k_min keyword argument.
  • ManifoldsBase.jl now is running on Documenter 1.3, Manopt.jl documentation now uses DocumenterInterLinks to refer to sections and functions from ManifoldsBase.jl

Fixed

  • fixes a type that when passing sub_kwargs to trust_regions caused an error in the decoration of the sub objective.

[0.4.56] March 4, 2024

Added

  • The option :step_towards_negative_gradient for nondescent_direction_behavior in quasi-Newton solvers does no longer emit a warning by default. This has been moved to a message, that can be accessed/displayed with DebugMessages
  • DebugMessages now has a second positional argument, specifying whether all messages, or just the first (:Once) should be displayed.

[0.4.55] March 3, 2024

Added

  • Option nondescent_direction_behavior for quasi-Newton solvers. By default it checks for non-descent direction which may not be handled well by some stepsize selection algorithms.

Fixed

  • unified documentation, especially function signatures further.
  • fixed a few typos related to math formulae in the doc strings.

[0.4.54] February 28, 2024

Added

  • convex_bundle_method optimization algorithm for non-smooth geodesically convex functions
  • proximal_bundle_method optimization algorithm for non-smooth functions.
  • StopWhenSubgradientNormLess, StopWhenLagrangeMultiplierLess, and stopping criteria.

Fixed

  • Doc strings now follow a vale.sh policy. Though this is not fully working, this PR improves a lot of the doc strings concerning wording and spelling.

[0.4.53] February 13, 2024

Fixed

  • fixes two storage action defaults, that accidentally still tried to initialize a :Population (as modified back to :Iterate 0.4.49).
  • fix a few typos in the documentation and add a reference for the subgradient method.

[0.4.52] February 5, 2024

Added

  • introduce an environment persistent way of setting global values with the set_manopt_parameter! function using Preferences.jl.
  • introduce such a value named :Mode to enable a "Tutorial" mode that shall often provide more warnings and information for people getting started with optimisation on manifolds

[0.4.51] January 30, 2024

Added

  • A StopWhenSubgradientNormLess stopping criterion for subgradient-based optimization.
  • Allow the message= of the DebugIfEntry debug action to contain a format element to print the field in the message as well.

[0.4.50] January 26, 2024

Fixed

  • Fix Quasi Newton on complex manifolds.

[0.4.49] January 18, 2024

Added

  • A StopWhenEntryChangeLess to be able to stop on arbitrary small changes of specific fields
  • generalises StopWhenGradientNormLess to accept arbitrary norm= functions
  • refactor the default in particle_swarm to no longer “misuse” the iteration change, but actually the new one the :swarm entry

[0.4.48] January 16, 2024

Fixed

  • fixes an imprecision in the interface of get_iterate that sometimes led to the swarm of particle_swarm being returned as the iterate.
  • refactor particle_swarm in naming and access functions to avoid this also in the future. To access the whole swarm, one now should use get_manopt_parameter(pss, :Population)

[0.4.47] January 6, 2024

Fixed

  • fixed a bug, where the retraction set in check_Hessian was not passed on to the optional inner check_gradient call, which could lead to unwanted side effects, see #342.

[0.4.46] January 1, 2024

Changed

  • An error is thrown when a line search from LineSearches.jl reports search failure.
  • Changed default stopping criterion in ALM algorithm to mitigate an issue occurring when step size is very small.
  • Default memory length in default ALM subsolver is now capped at manifold dimension.
  • Replaced CI testing on Julia 1.8 with testing on Julia 1.10.

Fixed

  • A bug in LineSearches.jl extension leading to slower convergence.
  • Fixed a bug in L-BFGS related to memory storage, which caused significantly slower convergence.

[0.4.45] December 28, 2023

Added

  • Introduce sub_kwargs and sub_stopping_criterion for trust_regions as noticed in #336

Changed

  • WolfePowellLineSearch, ArmijoLineSearch step sizes now allocate less
  • linesearch_backtrack! is now available
  • Quasi Newton Updates can work in-place of a direction vector as well.
  • Faster safe_indices in L-BFGS.

[0.4.44] December 12, 2023

Formally one could consider this version breaking, since a few functions have been moved, that in earlier versions (0.3.x) have been used in example scripts. These examples are now available again within ManoptExamples.jl, and with their “reappearance” the corresponding costs, gradients, differentials, adjoint differentials, and proximal maps have been moved there as well. This is not considered breaking, since the functions were only used in the old, removed examples. Each and every moved function is still documented. They have been partly renamed, and their documentation and testing has been extended.

Changed

[0.4.43] November 19, 2023

Added

  • vale.sh as a CI to keep track of a consistent documentation

[0.4.42] November 6, 2023

Added

  • add Manopt.JuMP_Optimizer implementing JuMP's solver interface

[0.4.41] November 2, 2023

Changed

  • trust_regions is now more flexible and the sub solver (Steihaug-Toint tCG by default) can now be exchanged.
  • adaptive_regularization_with_cubics is now more flexible as well, where it previously was a bit too much tightened to the Lanczos solver as well.
  • Unified documentation notation and bumped dependencies to use DocumenterCitations 1.3

[0.4.40] October 24, 2023

Added

  • add a --help argument to docs/make.jl to document all available command line arguments
  • add a --exclude-tutorials argument to docs/make.jl. This way, when quarto is not available on a computer, the docs can still be build with the tutorials not being added to the menu such that documenter does not expect them to exist.

Changes

  • Bump dependencies to ManifoldsBase.jl 0.15 and Manifolds.jl 0.9
  • move the ARC CG subsolver to the main package, since TangentSpace is now already available from ManifoldsBase.

[0.4.39] October 9, 2023

Changes

  • also use the pair of a retraction and the inverse retraction (see last update) to perform the relaxation within the Douglas-Rachford algorithm.

[0.4.38] October 8, 2023

Changes

  • avoid allocations when calling get_jacobian! within the Levenberg-Marquard Algorithm.

Fixed

  • Fix a lot of typos in the documentation

[0.4.37] September 28, 2023

Changes

  • add more of the Riemannian Levenberg-Marquard algorithms parameters as keywords, so they can be changed on call
  • generalize the internal reflection of Douglas-Rachford, such that is also works with an arbitrary pair of a reflection and an inverse reflection.

[0.4.36] September 20, 2023

Fixed

  • Fixed a bug that caused non-matrix points and vectors to fail when working with approximate

[0.4.35] September 14, 2023

Added

  • The access to functions of the objective is now unified and encapsulated in proper get_ functions.

[0.4.34] September 02, 2023

Added

  • an ManifoldEuclideanGradientObjective to allow the cost, gradient, and Hessian and other first or second derivative based elements to be Euclidean and converted when needed.
  • a keyword objective_type=:Euclidean for all solvers, that specifies that an Objective shall be created of the new type

[0.4.33] August 24, 2023

Added

  • ConstantStepsize and DecreasingStepsize now have an additional field type::Symbol to assess whether the step-size should be relatively (to the gradient norm) or absolutely constant.

[0.4.32] August 23, 2023

Added

  • The adaptive regularization with cubics (ARC) solver.

[0.4.31] August 14, 2023

Added

  • A :Subsolver keyword in the debug= keyword argument, that activates the new DebugWhenActive`` to de/activate subsolver debug from the main solvers DebugEvery`.

[0.4.30] August 3, 2023

Changed

  • References in the documentation are now rendered using DocumenterCitations.jl
  • Asymptote export now also accepts a size in pixel instead of its default 4cm size and render can be deactivated setting it to nothing.

[0.4.29] July 12, 2023

Fixed

  • fixed a bug, where cyclic_proximal_point did not work with decorated objectives.

[0.4.28] June 24, 2023

Changed

  • max_stepsize was specialized for FixedRankManifold to follow Matlab Manopt.

[0.4.27] June 15, 2023

Added

  • The AdaptiveWNGrad stepsize is available as a new stepsize functor.

Fixed

  • Levenberg-Marquardt now possesses its parameters initial_residual_values and initial_jacobian_f also as keyword arguments, such that their default initialisations can be adapted, if necessary

[0.4.26] June 11, 2023

Added

  • simplify usage of gradient descent as sub solver in the DoC solvers.
  • add a get_state function
  • document indicates_convergence.

[0.4.25] June 5, 2023

Fixed

  • Fixes an allocation bug in the difference of convex algorithm

[0.4.24] June 4, 2023

Added

  • another workflow that deletes old PR renderings from the docs to keep them smaller in overall size.

Changes

  • bump dependencies since the extension between Manifolds.jl and ManifoldsDiff.jl has been moved to Manifolds.jl

[0.4.23] June 4, 2023

Added

  • More details on the Count and Cache tutorial

Changed

  • loosen constraints slightly

[0.4.22] May 31, 2023

Added

  • A tutorial on how to implement a solver

[0.4.21] May 22, 2023

Added

  • A ManifoldCacheObjective as a decorator for objectives to cache results of calls, using LRU Caches as a weak dependency. For now this works with cost and gradient evaluations
  • A ManifoldCountObjective as a decorator for objectives to enable counting of calls to for example the cost and the gradient
  • adds a return_objective keyword, that switches the return of a solver to a tuple (o, s), where o is the (possibly decorated) objective, and s is the “classical” solver return (state or point). This way the counted values can be accessed and the cache can be reused.
  • change solvers on the mid level (form solver(M, objective, p)) to also accept decorated objectives

Changed

  • Switch all Requires weak dependencies to actual weak dependencies starting in Julia 1.9

[0.4.20] May 11, 2023

Changed

  • the default tolerances for the numerical check_ functions were loosened a bit, such that check_vector can also be changed in its tolerances.

[0.4.19] May 7, 2023

Added

  • the sub solver for trust_regions is now customizable and can now be exchanged.

Changed

  • slightly changed the definitions of the solver states for ALM and EPM to be type stable

[0.4.18] May 4, 2023

Added

  • A function check_Hessian(M, f, grad_f, Hess_f) to numerically verify the (Riemannian) Hessian of a function f

[0.4.17] April 28, 2023

Added

  • A new interface of the form alg(M, objective, p0) to allow to reuse objectives without creating AbstractManoptSolverStates and calling solve!. This especially still allows for any decoration of the objective and/or the state using debug=, or record=.

Changed

  • All solvers now have the initial point p as an optional parameter making it more accessible to first time users, gradient_descent(M, f, grad_f) is equivalent to gradient_descent(M, f, grad_f, rand(M))

Fixed

  • Unified the framework to work on manifold where points are represented by numbers for several solvers

[0.4.16] April 18, 2023

Fixed

  • the inner products used in truncated_gradient_descent now also work thoroughly on complex matrix manifolds

[0.4.15] April 13, 2023

Changed

  • trust_regions(M, f, grad_f, hess_f, p) now has the Hessian hess_f as well as the start point p0 as an optional parameter and approximate it otherwise.
  • trust_regions!(M, f, grad_f, hess_f, p) has the Hessian as an optional parameter and approximate it otherwise.

Removed

  • support for ManifoldsBase.jl 0.13.x, since with the definition of copy(M,p::Number), in 0.14.4, that one is used instead of defining it ourselves.

[0.4.14] April 06, 2023

Changed

  • particle_swarm now uses much more in-place operations

Fixed

  • particle_swarm used quite a few deepcopy(p) commands still, which were replaced by copy(M, p)

[0.4.13] April 09, 2023

Added

  • get_message to obtain messages from sub steps of a solver
  • DebugMessages to display the new messages in debug
  • safeguards in Armijo line search and L-BFGS against numerical over- and underflow that report in messages

[0.4.12] April 4, 2023

Added

[0.4.11] March 27, 2023

Changed

  • adapt tolerances in tests to the speed/accuracy optimized distance on the sphere in Manifolds.jl (part II)

[0.4.10] March 26, 2023

Changed

  • adapt tolerances in tests to the speed/accuracy optimized distance on the sphere in Manifolds.jl

[0.4.9] March 3, 2023

Added

[0.4.8] February 21, 2023

Added

  • a status_summary that displays the main parameters within several structures of Manopt, most prominently a solver state

Changed

  • Improved storage performance by introducing separate named tuples for points and vectors
  • changed the show methods of AbstractManoptSolverStates to display their `state_summary
  • Move tutorials to be rendered with Quarto into the documentation.

[0.4.7] February 14, 2023

Changed

  • Bump [compat] entry of ManifoldDiff to also include 0.3

[0.4.6] February 3, 2023

Fixed

  • Fixed a few stopping criteria even indicated to stop before the algorithm started.

[0.4.5] January 24, 2023

Changed

  • the new default functions that include p are used where possible
  • a first step towards faster storage handling

[0.4.4] January 20, 2023

Added

  • Introduce ConjugateGradientBealeRestart to allow CG restarts using Beale‘s rule

Fixed

  • fix a type in HestenesStiefelCoefficient

[0.4.3] January 17, 2023

Fixed

  • the CG coefficient β can now be complex
  • fix a bug in grad_distance

[0.4.2] January 16, 2023

Changed

  • the usage of inner in line search methods, such that they work well with complex manifolds as well

[0.4.1] January 15, 2023

Fixed

  • a max_stepsize per manifold to avoid leaving the injectivity radius, which it also defaults to

[0.4.0] January 10, 2023

Added

  • Dependency on ManifoldDiff.jl and a start of moving actual derivatives, differentials, and gradients there.
  • AbstractManifoldObjective to store the objective within the AbstractManoptProblem
  • Introduce a CostGrad structure to store a function that computes the cost and gradient within one function.
  • started a changelog.md to thoroughly keep track of changes

Changed

  • AbstractManoptProblem replaces Problem
  • the problem now contains a
  • AbstractManoptSolverState replaces Options
  • random_point(M) is replaced by rand(M) from `ManifoldsBase.jl
  • random_tangent(M, p) is replaced by rand(M; vector_at=p)