If you have computed a gradient or differential and you are not sure whether it is correct.

Manopt.check_HessianFunction
check_Hessian(M, f, grad_f, Hess_f, p=rand(M), X=rand(M; vector_at=p), Y=rand(M, vector_at=p); kwargs...)

Verify numerically whether the Hessian $\operatorname{Hess} f(M,p, X)$ of f(M,p) is correct.

For this either a second-order retraction or a critical point $p$ of f is required. The approximation is then

$$$f(\operatorname{retr}_p(tX)) = f(p) + t⟨\operatorname{grad} f(p), X⟩ + \frac{t^2}{2}⟨\operatorname{Hess}f(p)[X], X⟩ + \mathcal O(t^3)$$$

or in other words, that the error between the function $f$ and its second order Taylor behaves in error $\mathcal O(t^3)$, which indicates that the Hessian is correct, cf. also [Bou23, Section 6.8].

Note that if the errors are below the given tolerance and the method is exact, no plot is generated.

Keyword arguments

• check_grad: (true) verify that $\operatorname{grad} f(p) ∈ T_p\mathcal M$.

• check_linearity: (true) verify that the Hessian is linear, see is_Hessian_linear using a, b, X, and Y

• check_symmetry: (true) verify that the Hessian is symmetric, see is_Hessian_symmetric

• check_vector: (false) verify that $\operatorname{Hess} f(p)[X] ∈ T_p\mathcal M$ using is_vector.

• mode: (:Default) specify the mode for the verification; the default assumption is, that the retraction provided is of second order. Otherwise one can also verify the Hessian if the point p is a critical point. THen set the mode to :CritalPoint to use gradient_descent to find a critical point. Note: this requires (and evaluates) new tangent vectors X and Y

• atol, rtol: (same defaults as isapprox) tolerances that are passed down to all checks

• a, b two real values to verify linearity of the Hessian (if check_linearity=true)

• N: (101) number of points to verify within the log_range default range $[10^{-8},10^{0}]$

• exactness_tol: (1e-12) if all errors are below this tolerance, the verification is considered to be exact

• io: (nothing) provide an IO to print the result to

• gradient: (grad_f(M, p)) instead of the gradient function you can also provide the gradient at p directly

• Hessian: (Hess_f(M, p, X)) instead of the Hessian function you can provide the result of $\operatorname{Hess} f(p)[X]$ directly. Note that evaluations of the Hessian might still be necessary for checking linearity and symmetry and/or when using :CriticalPoint mode.

• limits: ((1e-8,1)) specify the limits in the log_range

• log_range: (range(limits[1], limits[2]; length=N)) specify the range of points (in log scale) to sample the Hessian line

• N: (101) number of points to use within the log_range default range $[10^{-8},10^{0}]$

• plot: (false) whether to plot the resulting verification (requires Plots.jl to be loaded). The plot is in log-log-scale. This is returned and can then also be saved.

• retraction_method: (default_retraction_method(M, typeof(p))) retraction method to use for

• slope_tol: (0.1) tolerance for the slope (global) of the approximation

• error: (:none) how to handle errors, possible values: :error, :info, :warn

• window: (nothing) specify window sizes within the log_range that are used for the slope estimation. the default is, to use all window sizes 2:N.

The kwargs... are also passed down to the check_vector and the check_gradient call, such that tolerances can easily be set.

While check_vector is also passed to the inner call to check_gradient as well as the retraction_method, this inner check_gradient is meant to be just for inner verification, so it does not throw an error nor produce a plot itself.

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Manopt.check_differentialFunction
check_differential(M, F, dF, p=rand(M), X=rand(M; vector_at=p); kwargs...)

Check numerically whether the differential dF(M,p,X) of F(M,p) is correct.

This implements the method described in [Bou23, Section 4.8].

Note that if the errors are below the given tolerance and the method is exact, no plot is generated,

Keyword arguments

• exactness_tol: (1e-12) if all errors are below this tolerance, the differential is considered to be exact
• io: (nothing) provide an IO to print the result to
• limits: ((1e-8,1)) specify the limits in the log_range
• log_range: (range(limits[1], limits[2]; length=N)) specify the range of points (in log scale) to sample the differential line
• N: (101) number of points to verify within the log_range default range $[10^{-8},10^{0}]$
• name: ("differential") name to display in the plot
• plot: (false) whether to plot the result (if Plots.jl is loaded). The plot is in log-log-scale. This is returned and can then also be saved.
• retraction_method: (default_retraction_method(M, typeof(p))) retraction method to use
• slope_tol: (0.1) tolerance for the slope (global) of the approximation
• throw_error: (false) throw an error message if the differential is wrong
• window: (nothing) specify window sizes within the log_range that are used for the slope estimation. the default is, to use all window sizes 2:N.
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Manopt.check_gradientFunction
check_gradient(M, F, gradF, p=rand(M), X=rand(M; vector_at=p); kwargs...)

Verify numerically whether the gradient gradF(M,p) of F(M,p) is correct, that is whether

$$$f(\operatorname{retr}_p(tX)) = f(p) + t⟨\operatorname{grad} f(p), X⟩ + \mathcal O(t^2)$$$

or in other words, that the error between the function $f$ and its first order Taylor behaves in error $\mathcal O(t^2)$, which indicates that the gradient is correct, cf. also [Bou23, Section 4.8].

Note that if the errors are below the given tolerance and the method is exact, no plot is generated.

Keyword arguments

• check_vector: (true) verify that $\operatorname{grad} f(p) ∈ T_p\mathcal M$ using is_vector.
• exactness_tol: (1e-12) if all errors are below this tolerance, the gradient is considered to be exact
• io: (nothing) provide an IO to print the result to
• gradient: (grad_f(M, p)) instead of the gradient function you can also provide the gradient at p directly
• limits: ((1e-8,1)) specify the limits in the log_range
• log_range: (range(limits[1], limits[2]; length=N)) - specify the range of points (in log scale) to sample the gradient line
• N: (101) number of points to verify within the log_range default range $[10^{-8},10^{0}]$
• plot: (false) whether to plot the result (if Plots.jl is loaded). The plot is in log-log-scale. This is returned and can then also be saved.
• retraction_method: (default_retraction_method(M, typeof(p))) retraction method to use
• slope_tol: (0.1) tolerance for the slope (global) of the approximation
• atol, rtol: (same defaults as isapprox) tolerances that are passed down to is_vector if check_vector is set to true
• error: (:none) how to handle errors, possible values: :error, :info, :warn
• window: (nothing) specify window sizes within the log_range that are used for the slope estimation. the default is, to use all window sizes 2:N.

The remaining keyword arguments are also passed down to the check_vector call, such that tolerances can easily be set.

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Manopt.is_Hessian_linearFunction
is_Hessian_linear(M, Hess_f, p,
X=rand(M; vector_at=p), Y=rand(M; vector_at=p), a=randn(), b=randn();
error=:none, io=nothing, kwargs...
)

Verify whether the Hessian function Hess_f fulfills linearity,

$$$\operatorname{Hess} f(p)[aX + bY] = b\operatorname{Hess} f(p)[X] + b\operatorname{Hess} f(p)[Y]$$$

which is checked using isapprox and the keyword arguments are passed to this function.

Optional arguments

• error: (:none) how to handle errors, possible values: :error, :info, :warn
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Manopt.is_Hessian_symmetricFunction
is_Hessian_symmetric(M, Hess_f, p=rand(M), X=rand(M; vector_at=p), Y=rand(M; vector_at=p);
error=:none, io=nothing, atol::Real=0, rtol::Real=atol>0 ? 0 : √eps

)

Verify whether the Hessian function Hess_f fulfills symmetry, which means that

$$$⟨\operatorname{Hess} f(p)[X], Y⟩ = ⟨X, \operatorname{Hess} f(p)[Y]⟩$$$

which is checked using isapprox and the kwargs... are passed to this function.

Optional arguments

• atol, rtol with the same defaults as the usual isapprox
• error: (:none) how to handle errors, possible values: :error, :info, :warn
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