Interior point Newton method

interior_point_Newton(M, f, grad_f, Hess_f, p=rand(M); kwargs...)
interior_point_Newton(M, cmo::ConstrainedManifoldObjective, p=rand(M); kwargs...)
interior_point_Newton!(M, f, grad]_f, Hess_f, p; kwargs...)
interior_point_Newton(M, ConstrainedManifoldObjective, p; kwargs...)

perform the interior point Newton method following [LY24].

In order to solve the constrained problem

\[\begin{aligned} \min_{p ∈ \mathcal M} & f(p)\\ \text{subject to}\quad&g_i(p) ≤ 0 \quad \text{ for } i= 1, …, m,\\ \quad & h_j(p)=0 \quad \text{ for } j=1,…,n, \end{aligned}\]

This algorithms iteratively solves the linear system based on extending the KKT system by a slack variable s.

\[\operatorname{J} F(p, μ, λ, s)[X, Y, Z, W] = -F(p, μ, λ, s), \text{ where } X ∈ T_{p}\mathcal M, Y,W ∈ ℝ^m, Z ∈ ℝ^n,\]

see CondensedKKTVectorFieldJacobian and CondensedKKTVectorField, respectively, for the reduced form, this is usually solved in. From the resulting X and Z in the reeuced form, the other two, $Y$, $W$, are then computed.

From the gradient $(X,Y,Z,W)$ at the current iterate $(p, μ, λ, s)$, a line search is performed using the KKTVectorFieldNormSq norm of the KKT vector field (squared) and its gradient KKTVectorFieldNormSqGradient together with the InteriorPointCentralityCondition.

Note that since the vector field $F$ includes the gradients of the constraint functions $g, h$, its gradient or Jacobian requires the Hessians of the constraints.

For that seach direction a line search is performed, that additionally ensures that the constraints are further fulfilled.

Input

• M: a Riemannian manifold $\mathcal M$
• f: a cost function $f: \mathcal M→ ℝ$ implemented as (M, p) -> v
• grad_f: the (Riemannian) gradient $\operatorname{grad}f$: \mathcal M → T_{p}\mathcal M of f as a function (M, p) -> X or a function (M, X, p) -> X computing X in-place
• Hess_f: the (Riemannian) Hessian $\operatorname{Hess}f$: T{p}\mathcal M → T{p}\mathcal M of f as a function (M, p, X) -> Y or a function (M, Y, p, X) -> Y computing Y in-place
• p: a point on the manifold $\mathcal M$

or a ConstrainedManifoldObjective cmo containing f, grad_f, Hess_f, and the constraints

Keyword arguments

The keyword arguments related to the constraints (the first eleven) are ignored if you pass a ConstrainedManifoldObjective cmo

• centrality_condition=missing; an additional condition when to accept a step size. This can be used to ensure that the resulting iterate is still an interior point if you provide a check (N,q) -> true/false, where N is the manifold of the step_problem.
• equality_constraints=nothing: the number $n$ of equality constraints.
• evaluation=AllocatingEvaluation(): specify whether the functions that return an array, for example a point or a tangent vector, work by allocating its result (AllocatingEvaluation) or whether they modify their input argument to return the result therein (InplaceEvaluation). Since usually the first argument is the manifold, the modified argument is the second.
• g=nothing: the inequality constraints
• grad_g=nothing: the gradient of the inequality constraints
• grad_h=nothing: the gradient of the equality constraints
• gradient_range=nothing: specify how gradients are represented, where nothing is equivalent to NestedPowerRepresentation
• gradient_equality_range=gradient_range: specify how the gradients of the equality constraints are represented
• gradient_inequality_range=gradient_range: specify how the gradients of the inequality constraints are represented
• h=nothing: the equality constraints
• Hess_g=nothing: the Hessian of the inequality constraints
• Hess_h=nothing: the Hessian of the equality constraints
• inequality_constraints=nothing: the number $m$ of inequality constraints.
• λ=ones(length(h(M, p))): the Lagrange multiplier with respect to the equality constraints $h$
• μ=ones(length(g(M, p))): the Lagrange multiplier with respect to the inequality constraints $g$
• retraction_method=default_retraction_method(M, typeof(p)): a retraction $\operatorname{retr}$ to use, see the section on retractions
• ρ=μ's / length(μ): store the orthogonality μ's/m to compute the barrier parameter β in the sub problem.
• s=copy(μ): initial value for the slack variables
• σ=calculate_σ(M, cmo, p, μ, λ, s): scaling factor for the barrier parameter β in the sub problem, which is updated during the iterations
• step_objective: a ManifoldGradientObjective of the norm of the KKT vector field KKTVectorFieldNormSq and its gradient KKTVectorFieldNormSqGradient
• step_problem: the manifold $\mathcal M × ℝ^m × ℝ^n × ℝ^m$ together with the step_objective as the problem the linesearch stepsize= employs for determining a step size
• step_state: the StepsizeState with point and search direction
• stepsize=ArmijoLinesearch(): a functor inheriting from Stepsize to determine a step size with the centrality_condtion keyword as additional criterion to accept a step, if this is provided
• stopping_criterion=StopAfterIteration(200)|StopWhenKKTResidualLess(1e-8): a functor indicating that the stopping criterion is fulfilled a stopping criterion, by default depending on the residual of the KKT vector field or a maximal number of steps, which ever hits first.
• sub_kwargs=(;): keyword arguments to decorate the sub options, for example debug, that automatically respects the main solvers debug options (like sub-sampling) as well
• sub_objective: The SymmetricLinearSystemObjective modelling the system of equations to use in the sub solver, includes the CondensedKKTVectorFieldJacobian $\mathcal A(X)$ and the CondensedKKTVectorField $b$ in $\mathcal A(X) + b = 0$ we aim to solve. This is used to define the sub_problem= keyword and has hence no effect, if you set sub_problem directly.
• sub_stopping_criterion=StopAfterIteration(manifold_dimension(M))|StopWhenRelativeResidualLess(c,1e-8), where $c = \lVert b \rVert_{}$ from the system to solve. This is used to define the sub_state= keyword and has hence no effect, if you set sub_state directly.
• sub_problem=DefaultManoptProblem(M, sub_objective): specify a problem for a solver or a closed form solution function, which can be allocating or in-place.
• sub_state=ConjugateResidualState: a state to specify the sub solver to use. For a closed form solution, this indicates the type of function.
• vector_space=Rn a function that, given an integer, returns the manifold to be used for the vector space components $ℝ^m,ℝ^n$
• X=zero_vector(M,p): th initial gradient with respect to p.
• Y=zero(μ): the initial gradient with respct to μ
• Z=zero(λ): the initial gradient with respct to λ
• W=zero(s): the initial gradient with respct to s

As well as internal keywords used to set up these given keywords like _step_M, _step_p, _sub_M, _sub_p, and _sub_X, that should not be changed.

All other keyword arguments are passed to decorate_state! for state decorators or decorate_objective! for objective, respectively.

Output

The obtained approximate constrained minimizer $p^*$. To obtain the whole final state of the solver, see get_solver_return for details, especially the return_state= keyword.

interior_point_Newton(M, f, grad_f, Hess_f, p=rand(M); kwargs...)
interior_point_Newton(M, cmo::ConstrainedManifoldObjective, p=rand(M); kwargs...)
interior_point_Newton!(M, f, grad]_f, Hess_f, p; kwargs...)
interior_point_Newton(M, ConstrainedManifoldObjective, p; kwargs...)

perform the interior point Newton method following [LY24].

In order to solve the constrained problem

\[\begin{aligned} \min_{p ∈ \mathcal M} & f(p)\\ \text{subject to}\quad&g_i(p) ≤ 0 \quad \text{ for } i= 1, …, m,\\ \quad & h_j(p)=0 \quad \text{ for } j=1,…,n, \end{aligned}\]

This algorithms iteratively solves the linear system based on extending the KKT system by a slack variable s.

\[\operatorname{J} F(p, μ, λ, s)[X, Y, Z, W] = -F(p, μ, λ, s), \text{ where } X ∈ T_{p}\mathcal M, Y,W ∈ ℝ^m, Z ∈ ℝ^n,\]

see CondensedKKTVectorFieldJacobian and CondensedKKTVectorField, respectively, for the reduced form, this is usually solved in. From the resulting X and Z in the reeuced form, the other two, $Y$, $W$, are then computed.

From the gradient $(X,Y,Z,W)$ at the current iterate $(p, μ, λ, s)$, a line search is performed using the KKTVectorFieldNormSq norm of the KKT vector field (squared) and its gradient KKTVectorFieldNormSqGradient together with the InteriorPointCentralityCondition.

Note that since the vector field $F$ includes the gradients of the constraint functions $g, h$, its gradient or Jacobian requires the Hessians of the constraints.

For that seach direction a line search is performed, that additionally ensures that the constraints are further fulfilled.

Input

• M: a Riemannian manifold $\mathcal M$
• f: a cost function $f: \mathcal M→ ℝ$ implemented as (M, p) -> v
• grad_f: the (Riemannian) gradient $\operatorname{grad}f$: \mathcal M → T_{p}\mathcal M of f as a function (M, p) -> X or a function (M, X, p) -> X computing X in-place
• Hess_f: the (Riemannian) Hessian $\operatorname{Hess}f$: T{p}\mathcal M → T{p}\mathcal M of f as a function (M, p, X) -> Y or a function (M, Y, p, X) -> Y computing Y in-place
• p: a point on the manifold $\mathcal M$

or a ConstrainedManifoldObjective cmo containing f, grad_f, Hess_f, and the constraints

Keyword arguments

The keyword arguments related to the constraints (the first eleven) are ignored if you pass a ConstrainedManifoldObjective cmo

• centrality_condition=missing; an additional condition when to accept a step size. This can be used to ensure that the resulting iterate is still an interior point if you provide a check (N,q) -> true/false, where N is the manifold of the step_problem.
• equality_constraints=nothing: the number $n$ of equality constraints.
• evaluation=AllocatingEvaluation(): specify whether the functions that return an array, for example a point or a tangent vector, work by allocating its result (AllocatingEvaluation) or whether they modify their input argument to return the result therein (InplaceEvaluation). Since usually the first argument is the manifold, the modified argument is the second.
• g=nothing: the inequality constraints
• grad_g=nothing: the gradient of the inequality constraints
• grad_h=nothing: the gradient of the equality constraints
• gradient_range=nothing: specify how gradients are represented, where nothing is equivalent to NestedPowerRepresentation
• gradient_equality_range=gradient_range: specify how the gradients of the equality constraints are represented
• gradient_inequality_range=gradient_range: specify how the gradients of the inequality constraints are represented
• h=nothing: the equality constraints
• Hess_g=nothing: the Hessian of the inequality constraints
• Hess_h=nothing: the Hessian of the equality constraints
• inequality_constraints=nothing: the number $m$ of inequality constraints.
• λ=ones(length(h(M, p))): the Lagrange multiplier with respect to the equality constraints $h$
• μ=ones(length(g(M, p))): the Lagrange multiplier with respect to the inequality constraints $g$
• retraction_method=default_retraction_method(M, typeof(p)): a retraction $\operatorname{retr}$ to use, see the section on retractions
• ρ=μ's / length(μ): store the orthogonality μ's/m to compute the barrier parameter β in the sub problem.
• s=copy(μ): initial value for the slack variables
• σ=calculate_σ(M, cmo, p, μ, λ, s): scaling factor for the barrier parameter β in the sub problem, which is updated during the iterations
• step_objective: a ManifoldGradientObjective of the norm of the KKT vector field KKTVectorFieldNormSq and its gradient KKTVectorFieldNormSqGradient
• step_problem: the manifold $\mathcal M × ℝ^m × ℝ^n × ℝ^m$ together with the step_objective as the problem the linesearch stepsize= employs for determining a step size
• step_state: the StepsizeState with point and search direction
• stepsize=ArmijoLinesearch(): a functor inheriting from Stepsize to determine a step size with the centrality_condtion keyword as additional criterion to accept a step, if this is provided
• stopping_criterion=StopAfterIteration(200)|StopWhenKKTResidualLess(1e-8): a functor indicating that the stopping criterion is fulfilled a stopping criterion, by default depending on the residual of the KKT vector field or a maximal number of steps, which ever hits first.
• sub_kwargs=(;): keyword arguments to decorate the sub options, for example debug, that automatically respects the main solvers debug options (like sub-sampling) as well
• sub_objective: The SymmetricLinearSystemObjective modelling the system of equations to use in the sub solver, includes the CondensedKKTVectorFieldJacobian $\mathcal A(X)$ and the CondensedKKTVectorField $b$ in $\mathcal A(X) + b = 0$ we aim to solve. This is used to define the sub_problem= keyword and has hence no effect, if you set sub_problem directly.
• sub_stopping_criterion=StopAfterIteration(manifold_dimension(M))|StopWhenRelativeResidualLess(c,1e-8), where $c = \lVert b \rVert_{}$ from the system to solve. This is used to define the sub_state= keyword and has hence no effect, if you set sub_state directly.
• sub_problem=DefaultManoptProblem(M, sub_objective): specify a problem for a solver or a closed form solution function, which can be allocating or in-place.
• sub_state=ConjugateResidualState: a state to specify the sub solver to use. For a closed form solution, this indicates the type of function.
• vector_space=Rn a function that, given an integer, returns the manifold to be used for the vector space components $ℝ^m,ℝ^n$
• X=zero_vector(M,p): th initial gradient with respect to p.
• Y=zero(μ): the initial gradient with respct to μ
• Z=zero(λ): the initial gradient with respct to λ
• W=zero(s): the initial gradient with respct to s

As well as internal keywords used to set up these given keywords like _step_M, _step_p, _sub_M, _sub_p, and _sub_X, that should not be changed.

All other keyword arguments are passed to decorate_state! for state decorators or decorate_objective! for objective, respectively.

Output

The obtained approximate constrained minimizer $p^*$. To obtain the whole final state of the solver, see get_solver_return for details, especially the return_state= keyword.

InteriorPointNewtonState{P,T} <: AbstractHessianSolverState

Fields

• λ: the Lagrange multiplier with respect to the equality constraints
• μ: the Lagrange multiplier with respect to the inequality constraints
• p::P: a point on the manifold $\mathcal M$storing the current iterate
• s: the current slack variable
• sub_problem::Union{AbstractManoptProblem, F}: specify a problem for a solver or a closed form solution function, which can be allocating or in-place.
• sub_state::Union{AbstractManoptProblem, F}: a state to specify the sub solver to use. For a closed form solution, this indicates the type of function.
• X: the current gradient with respect to p
• Y: the current gradient with respect to μ
• Z: the current gradient with respect to λ
• W: the current gradient with respect to s
• ρ: store the orthogonality μ's/m to compute the barrier parameter β in the sub problem
• σ: scaling factor for the barrier parameter β in the sub problem
• stop::StoppingCriterion: a functor indicating that the stopping criterion is fulfilled
• retraction_method::AbstractRetractionMethod: a retraction $\operatorname{retr}$ to use, see the section on retractions
• stepsize::Stepsize: a functor inheriting from Stepsize to determine a step size
• step_problem: an AbstractManoptProblem storing the manifold and objective for the line search
• step_state: storing iterate and search direction in a state for the line search, see StepsizeState

Constructor

InteriorPointNewtonState(
    M::AbstractManifold,
    cmo::ConstrainedManifoldObjective,
    sub_problem::Pr,
    sub_state::St;
    kwargs...
)

Initialize the state, where both the AbstractManifold and the ConstrainedManifoldObjective are used to fill in reasonable defaults for the keywords.

Input

• M::AbstractManifold: a Riemannian manifold $\mathcal M$
• cmo: a ConstrainedManifoldObjective
• sub_problem: specify a problem for a solver or a closed form solution function, which can be allocating or in-place.
• sub_state: a state to specify the sub solver to use. For a closed form solution, this indicates the type of function.

Keyword arguments

Let m and n denote the number of inequality and equality constraints, respectively

• p=rand(M): a point on the manifold $\mathcal M$to specify the initial value
• μ=ones(m)
• X=zero_vector(M,p)
• Y=zero(μ)
• λ=zeros(n)
• Z=zero(λ)
• s=ones(m)
• W=zero(s)
• ρ=μ's/m
• σ=calculate_σ(M, cmo, p, μ, λ, s)
• stopping_criterion=StopAfterIteration(200)|StopWhenChangeLess(1e-8): a functor indicating that the stopping criterion is fulfilled
• retraction_method=default_retraction_method(M, typeof(p)): a retraction $\operatorname{retr}$ to use, see the section on retractions
• step_objective=ManifoldGradientObjective(KKTVectorFieldNormSq(cmo), KKTVectorFieldNormSqGradient(cmo); evaluation=InplaceEvaluation())
• vector_space=Rn: a function that, given an integer, returns the manifold to be used for the vector space components $ℝ^m,ℝ^n$
• step_problem: wrap the manifold $\mathcal M × ℝ^m × ℝ^n × ℝ^m$
• step_state: the StepsizeState with point and search direction
• stepsize=ArmijoLinesearch(): a functor inheriting from Stepsize to determine a step size with the InteriorPointCentralityCondition as additional condition to accept a step

and internally _step_M and _step_p for the manifold and point in the stepsize.

CondensedKKTVectorField{O<:ConstrainedManifoldObjective,T,R} <: AbstractConstrainedSlackFunctor{T,R}

Given the constrained optimization problem

\[\begin{aligned} \min_{p ∈\mathcal{M}} &f(p)\\ \text{subject to } &g_i(p)\leq 0 \quad \text{ for } i= 1, …, m,\\ \quad &h_j(p)=0 \quad \text{ for } j=1,…,n, \end{aligned}\]

Then reformulating the KKT conditions of the Lagrangian from the optimality conditions of the Lagrangian

\[\mathcal L(p, μ, λ) = f(p) + \sum_{j=1}^n λ_jh_j(p) + \sum_{i=1}^m μ_ig_i(p)\]

in a perturbed / barrier method in a condensed form using a slack variable $s ∈ ℝ^m$ and a barrier parameter $β$ and the Riemannian gradient of the Lagrangian with respect to the first parameter $\operatorname{grad}_p L(p, μ, λ)$.

Let $\mathcal N = \mathcal M × ℝ^n$. We obtain the linear system

\[\mathcal A(p,λ)[X,Y] = -b(p,λ),\qquad \text{where } (X,Y) ∈ T_{(p,λ)}\mathcal N\]

where $\mathcal A: T_{(p,λ)}\mathcal N → T_{(p,λ)}\mathcal N$ is a linear operator and this struct models the right hand side $b(p,λ) ∈ T_{(p,λ)}\mathcal M$ given by

\[b(p,λ) = \begin{pmatrix} \operatorname{grad} f(p) + \displaystyle\sum_{j=1}^n λ_j \operatorname{grad} h_j(p) + \displaystyle\sum_{i=1}^m μ_i \operatorname{grad} g_i(p) + \displaystyle\sum_{i=1}^m \frac{μ_i}{s_i}\bigl( μ_i(g_i(p)+s_i) + β - μ_is_i \bigr)\operatorname{grad} g_i(p)\\ h(p) \end{pmatrix}\]

Fields

• cmo the ConstrainedManifoldObjective
• μ::T the vector in $ℝ^m$ of coefficients for the inequality constraints
• s::T the vector in $ℝ^m$ of sclack variables
• β::R the barrier parameter $β∈ℝ$

Constructor

CondensedKKTVectorField(cmo, μ, s, β)

CondensedKKTVectorFieldJacobian{O<:ConstrainedManifoldObjective,T,R}  <: AbstractConstrainedSlackFunctor{T,R}

Given the constrained optimization problem

\[\begin{aligned} \min_{p ∈\mathcal{M}} &f(p)\\ \text{subject to } &g_i(p)\leq 0 \quad \text{ for } i= 1, …, m,\\ \quad &h_j(p)=0 \quad \text{ for } j=1,…,n, \end{aligned}\]

we reformulate the KKT conditions of the Lagrangian from the optimality conditions of the Lagrangian

\[\mathcal L(p, μ, λ) = f(p) + \sum_{j=1}^n λ_jh_j(p) + \sum_{i=1}^m μ_ig_i(p)\]

in a perturbed / barrier method enhanced as well as condensed form as using $\operatorname{grad}_o L(p, μ, λ)$ the Riemannian gradient of the Lagrangian with respect to the first parameter.

Let $\mathcal N = \mathcal M × ℝ^n$. We obtain the linear system

\[\mathcal A(p,λ)[X,Y] = -b(p,λ),\qquad \text{where } X ∈ T_p\mathcal M, Y ∈ ℝ^n\]

where $\mathcal A: T_{(p,λ)}\mathcal N → T_{(p,λ)}\mathcal N$ is a linear operator on $T_{(p,λ)}\mathcal N = T_p\mathcal M × ℝ^n$ given by

\[\mathcal A(p,λ)[X,Y] = \begin{pmatrix} \operatorname{Hess}_p\mathcal L(p, μ, λ)[X] + \displaystyle\sum_{i=1}^m \frac{μ_i}{s_i}⟨\operatorname{grad} g_i(p), X⟩\operatorname{grad} g_i(p) + \displaystyle\sum_{j=1}^n Y_j \operatorname{grad} h_j(p) \\ \Bigl( ⟨\operatorname{grad} h_j(p), X⟩ \Bigr)_{j=1}^n \end{pmatrix}\]

Fields

• cmo the ConstrainedManifoldObjective
• μ::V the vector in $ℝ^m$ of coefficients for the inequality constraints
• s::V the vector in $ℝ^m$ of slack variables
• β::R the barrier parameter $β∈ℝ$

Constructor

CondensedKKTVectorFieldJacobian(cmo, μ, s, β)

KKTVectorField{O<:ConstrainedManifoldObjective}

Implement the vectorfield $F$ KKT-conditions, inlcuding a slack variable for the inequality constraints.

Given the LagrangianCost

\[\mathcal L(p; μ, λ) = f(p) + \sum_{i=1}^m μ_ig_i(p) + \sum_{j=1}^n λ_jh_j(p)\]

the LagrangianGradient

\[\operatorname{grad}\mathcal L(p, μ, λ) = \operatorname{grad}f(p) + \sum_{j=1}^n λ_j \operatorname{grad} h_j(p) + \sum_{i=1}^m μ_i \operatorname{grad} g_i(p),\]

and introducing the slack variables $s=-g(p) ∈ ℝ^m$ the vector field is given by

\[F(p, μ, λ, s) = \begin{pmatrix} \operatorname{grad}_p \mathcal L(p, μ, λ)\\ g(p) + s\\ h(p)\\ μ ⊙ s \end{pmatrix}, \text{ where } p \in \mathcal M, μ, s \in ℝ^m\text{ and } λ \in ℝ^n,\]

where $⊙$ denotes the Hadamard (or elementwise) product

Fields

• cmo the ConstrainedManifoldObjective

While the point p is arbitrary and usually not needed, it serves as internal memory in the computations. Furthermore Both fields together also calrify the product manifold structure to use.

Constructor

KKTVectorField(cmo::ConstrainedManifoldObjective)

Example

Define F = KKTVectorField(cmo) for some ConstrainedManifoldObjective cmo and let N be the product manifold of $\mathcal M×ℝ^m×ℝ^n×ℝ^m$. Then, you can call this cost as F(N, q) or as the in-place variant F(N, Y, q), where q is a point on N and Y is a tangent vector at q for the result.

KKTVectorFieldJacobian{O<:ConstrainedManifoldObjective}

Implement the Jacobian of the vector field $F$ of the KKT-conditions, inlcuding a slack variable for the inequality constraints, see KKTVectorField and KKTVectorFieldAdjointJacobian..

\[\operatorname{J} F(p, μ, λ, s)[X, Y, Z, W] = \begin{pmatrix} \operatorname{Hess}_p \mathcal L(p, μ, λ)[X] + \displaystyle\sum_{i=1}^m Y_i \operatorname{grad} g_i(p) + \displaystyle\sum_{j=1}^n Z_j \operatorname{grad} h_j(p)\\ \Bigl( ⟨\operatorname{grad} g_i(p), X⟩ + W_i\Bigr)_{i=1}^m\\ \Bigl( ⟨\operatorname{grad} h_j(p), X⟩ \Bigr)_{j=1}^n\\ μ ⊙ W + s ⊙ Y \end{pmatrix},\]

where $⊙$ denotes the Hadamard (or elementwise) product

See also the LagrangianHessian $\operatorname{Hess}_p \mathcal L(p, μ, λ)[X]$.

Fields

• cmo the ConstrainedManifoldObjective

Constructor

KKTVectorFieldJacobian(cmo::ConstrainedManifoldObjective)

Generate the Jacobian of the KKT vector field related to some ConstrainedManifoldObjective cmo.

Example

Define JF = KKTVectorFieldJacobian(cmo) for some ConstrainedManifoldObjective cmo and let N be the product manifold of $\mathcal M×ℝ^m×ℝ^n×ℝ^m$. Then, you can call this cost as JF(N, q, Y) or as the in-place variant JF(N, Z, q, Y), where q is a point on N and Y and Z are a tangent vector at q.

KKTVectorFieldAdjointJacobian{O<:ConstrainedManifoldObjective}

Implement the Adjoint of the Jacobian of the vector field $F$ of the KKT-conditions, inlcuding a slack variable for the inequality constraints, see KKTVectorField and KKTVectorFieldJacobian.

\[\operatorname{J}^* F(p, μ, λ, s)[X, Y, Z, W] = \begin{pmatrix} \operatorname{Hess}_p \mathcal L(p, μ, λ)[X] + \displaystyle\sum_{i=1}^m Y_i \operatorname{grad} g_i(p) + \displaystyle\sum_{j=1}^n Z_j \operatorname{grad} h_j(p)\\ \Bigl( ⟨\operatorname{grad} g_i(p), X⟩ + s_iW_i\Bigr)_{i=1}^m\\ \Bigl( ⟨\operatorname{grad} h_j(p), X⟩ \Bigr)_{j=1}^n\\ μ ⊙ W + Y \end{pmatrix},\]

where $⊙$ denotes the Hadamard (or elementwise) product

See also the LagrangianHessian $\operatorname{Hess}_p \mathcal L(p, μ, λ)[X]$.

Fields

• cmo the ConstrainedManifoldObjective

Constructor

KKTVectorFieldAdjointJacobian(cmo::ConstrainedManifoldObjective)

Generate the Adjoint Jacobian of the KKT vector field related to some ConstrainedManifoldObjective cmo.

Example

Define AdJF = KKTVectorFieldAdjointJacobian(cmo) for some ConstrainedManifoldObjective cmo and let N be the product manifold of $\mathcal M×ℝ^m×ℝ^n×ℝ^m$. Then, you can call this cost as AdJF(N, q, Y) or as the in-place variant AdJF(N, Z, q, Y), where q is a point on N and Y and Z are a tangent vector at q.

KKTVectorFieldNormSq{O<:ConstrainedManifoldObjective}

Implement the square of the norm of the vectorfield $F$ of the KKT-conditions, inlcuding a slack variable for the inequality constraints, see KKTVectorField, where this functor applies the norm to. In [LY24] this is called the merit function.

Fields

• cmo the ConstrainedManifoldObjective

Constructor

KKTVectorFieldNormSq(cmo::ConstrainedManifoldObjective)

Example

Define f = KKTVectorFieldNormSq(cmo) for some ConstrainedManifoldObjective cmo and let N be the product manifold of $\mathcal M×ℝ^m×ℝ^n×ℝ^m$. Then, you can call this cost as f(N, q), where q is a point on N.

KKTVectorFieldNormSqGradient{O<:ConstrainedManifoldObjective}

Compute the gradient of the KKTVectorFieldNormSq $φ(p,μ,λ,s) = \lVert F(p,μ,λ,s)\rVert^2$, that is of the norm squared of the KKTVectorField $F$.

This is given in [LY24] as the gradient of their merit function, which we can write with the adjoint $J^*$ of the Jacobian

\[\operatorname{grad} φ = 2\operatorname{J}^* F(p, μ, λ, s)[F(p, μ, λ, s)],\]

and hence is computed with KKTVectorFieldAdjointJacobian and KKTVectorField.

For completeness, the gradient reads, using the LagrangianGradient $L = \operatorname{grad}_p \mathcal L(p,μ,λ) ∈ T_p\mathcal M$, for a shorthand of the first component of $F$, as

\[\operatorname{grad} φ = 2 \begin{pmatrix} \operatorname{grad}_p \mathcal L(p,μ,λ)[L] + (g_i(p) + s_i)\operatorname{grad} g_i(p) + h_j(p)\operatorname{grad} h_j(p)\\ \Bigl( ⟨\operatorname{grad} g_i(p), L⟩ + s_i\Bigr)_{i=1}^m + μ ⊙ s ⊙ s\\ \Bigl( ⟨\operatorname{grad} h_j(p), L⟩ \Bigr)_{j=1}^n\\ g + s + μ ⊙ μ ⊙ s \end{pmatrix},\]

where $⊙$ denotes the Hadamard (or elementwise) product.

Fields

• cmo the ConstrainedManifoldObjective

Constructor

KKTVectorFieldNormSqGradient(cmo::ConstrainedManifoldObjective)

Example

Define grad_f = KKTVectorFieldNormSqGradient(cmo) for some ConstrainedManifoldObjective cmo and let N be the product manifold of $\mathcal M×ℝ^m×ℝ^n×ℝ^m$. Then, you can call this cost as grad_f(N, q) or as the in-place variant grad_f(N, Y, q), where q is a point on N and Y is a tangent vector at q returning the resulting gradient at.

InteriorPointCentralityCondition{CO,R}

A functor to check the centrality condition.

In order to obtain a step in the linesearch performed within the interior_point_Newton, Section 6 of [LY24] propose the following additional conditions to hold inspired by the Euclidean case described in Section 6 [ETTZ96]:

For a given ConstrainedManifoldObjective assume consider the KKTVectorField $F$, that is we are at a point $q = (p, λ, μ, s)$ on $\mathcal M × ℝ^m × ℝ^n × ℝ^m$and a search direction $V = (X, Y, Z, W)$.

Then, let

\[τ_1 = \frac{m⋅\min\{ μ ⊙ s\}}{μ^{\mathrm{T}}s} \quad\text{ and }\quad τ_2 = \frac{μ^{\mathrm{T}}s}{\lVert F(q) \rVert}\]

where $⊙$ denotes the Hadamard (or elementwise) product.

For a new candidate $q(α) = \bigl(p(α), λ(α), μ(α), s(α)\bigr) := (\operatorname{retr}_p(αX), λ+αY, μ+αZ, s+αW)$, we then define two functions

\[c_1(α) = \min\{ μ(α) ⊙ s(α) \} - \frac{γτ_1 μ(α)^{\mathrm{T}}s(α)}{m} \quad\text{ and }\quad c_2(α) = μ(α)^{\mathrm{T}}s(α) – γτ_2 \lVert F(q(α)) \rVert.\]

While the paper now states that the (Armijo) linesearch starts at a point $\tilde α$, it is easier to include the condition that $c_1(α) ≥ 0$ and $c_2(α) ≥ 0$ into the linesearch as well.

The functor InteriorPointCentralityCondition(cmo, γ, μ, s, normKKT)(N,qα) defined here evaluates this condition and returns true if both $c_1$ and $c_2$ are nonnegative.

Fields

• cmo: a ConstrainedManifoldObjective
• γ: a constant
• τ1, τ2: the constants given in the formula.

Constructor

InteriorPointCentralityCondition(cmo, γ)
InteriorPointCentralityCondition(cmo, γ, τ1, τ2)

Initialise the centrality conditions. The parameters τ1, τ2 are initialise to zero if not provided.

calculate_σ(M, cmo, p, μ, λ, s; kwargs...)

Compute the new $σ$ factor for the barrier parameter in interior_point_Newton as

\[\min\{\frac{1}{2}, \lVert F(p; μ, λ, s)\rVert^{\frac{1}{2}} \},\]

where $F$ is the KKT vector field, hence the KKTVectorFieldNormSq is used.

Keyword arguments

• vector_space=Rn a function that, given an integer, returns the manifold to be used for the vector space components $ℝ^m,ℝ^n$
• N the manifold $\mathcal M × ℝ^m × ℝ^n × ℝ^m$ the vector field lives on (generated using vector_space)
• q provide memory on N for interims evaluation of the vector field

StopWhenKKTResidualLess <: StoppingCriterion

Stop when the KKT residual

r^2
= \lVert \operatorname{grad}_p \mathcal L(p, μ, λ) \rVert^2
+ \sum_{i=1}^m [μ_i]_{-}^2 + [g_i(p)]_+^2 + \lvert \mu_ig_i(p)^2
+ \sum_{j=1}^n \lvert h_i(p)\rvert^2.

is less than a given threshold $r < ε$. We use $[v]_+ = \max\{0,v\}$ and $[v]_- = \min\{0,t\}$ for the positive and negative part of $v$, respectively

Fields

• ε: a threshold
• residual: store the last residual if the stopping criterion is hit.
• at_iteration: