Describing the cost function

General philosophy

An optimization problem in Manopt is represented as a problem structure. The latter must include a field problem.M which contains a structure describing a manifold, as obtained from a factory. On top of this, the problem structure must include some fields that describe the cost function $$f$$ to be minimized and, possibly, its derivatives. This is done with function handles.

The solvers do not query these function handles directly. Instead, they call core (internal) tools such as getCost, getGradient, getHessian, etc. These tools consider the available fields in the problem structure and “do their best” to return the requested object.

As a result, we gain great flexibility in the cost function description. Indeed, as the needs grow during the life-cycle of the toolbox and new ways of describing the cost function become necessary, it suffices to update the core get* tools to take these new ways into account. This has made it much easier over time to incorporate (and improve) caching. Also, if a solver requests an object that is not available, then Manopt can automatically fall back to an approximation.

If you do not provide the gradient and a solver queries it, then Manopt falls back to finite differences of the cost function to approximate the Riemannian gradient. This requires building an orthonormal basis for the tangent space (that’s expensive) and then querying the cost function value along each basis vector (that’s $$\dim \mathcal{M}$$ calls). Solvers will still run, but this feature is included only for convenience when prototyping.

It’s fine to omit the Hessian

If you do not provide the Hessian and a solver queries it, then Manopt falls back to finite differences of the gradient to approximate the Hessian. This is typically good enough, and often has a computational cost similar to evaluating the true Hessian. You can control this further with approxhessianFD.

Regardless of how you implement the gradient, make sure to check that it is correct by running checkgradient(problem) at least once. Likewise, check the Hessian with checkhessian(problem). See the tools page for more.

One example, five ways

Similarly to the first example, consider minimizing $f(x) = \frac{1}{2} x^\top A x$ for $$x$$ on the unit sphere in $$\mathbb{R}^n$$, where $$A$$ is a symmetric matrix. The base code could look something like this:

n = 1000;
A = randsym(n);
problem.M = spherefactory(n);

% ... define the cost: see below

x = trustregions(problem);

The cost function can be specified by adding fields to the problem structure. Let us go through a few different ways to do that.

The common way: via Euclidean extension

If we think of $$f$$ as a function on $$\mathbb{R}^n$$ (ignoring the restriction to the sphere), then the gradient and Hessian of $$f$$ are easily derived: \begin{align} \nabla f(x) = Ax, && \nabla^2 f(x)[u] = Au. \end{align} We think of this approach as “extending” the function from the sphere to the embedding space, which is the Euclidean space $$\mathbb{R}^n$$. You can tell Manopt what the gradient and Hessian for that Euclidean extension are using the fields egrad and ehess, as follows (mind the e for Euclidean or embedding):

problem.cost = @(x) .5*x'*A*x;
problem.ehess = @(x, u) A*u; % optional

Manopt takes care of converting these to the Riemannian gradient and Hessian of $$f$$ on the sphere, using problem.M.egrad2rgrad and problem.M.ehess2rhess: this is automatic.

• It is fine to omit ehess, but try not to omit egrad (see earlier comments).
• The computation A*x is redundant between cost and egrad: more on this later.
• The input to egrad is a point $$x$$. The output is a vector in the embedding space, corresponding to $$\nabla f(x)$$.
• The inputs to ehess are a point $$x$$ and a tangent vector $$u$$ at $$x$$. The output is a vector in the embedding space, corresponding to $$\nabla^2 f(x)[u]$$.

Mind the distinction between tangent vectors and vectors in the embedding space. For many manifolds, these are numerically represented in the same way, so the distinction does not matter (e.g., for spherefactory and stiefelfactory). However, some manifolds use different numerical representations (e.g., rotationsfactory). For those, you may want to call M.tangent2ambient(x, u) to obtain the equivalent of u in the embedding space. Read the help section of your manifold factory to make sure, and find out more here.

Coding the Riemannian derivatives manually

There are instances where it is more natural or more efficient to describe the Riemannian derivatives directly (as opposed to the Euclidean extension approach), though this is not common.

One example where this is preferred is when computing an intrinsic mean. There, the cost function involves the squared Riemannian distance, whose Riemannian gradient is the logarithmic map.

In our running example, the sphere is a Riemannian submanifold of $$\mathbb{R}^n$$. Therefore,

• The Riemannian gradient is the orthogonal projection of $$\nabla f(x)$$ to the tangent space at $$x$$ (see Proposition 3.61 in this book). The projector is available in problem.M.proj: $\grad f(x) = \Proj_x(\nabla f(x)).$
• The Riemannian Hessian at $$x$$ along $$u$$ is the projection of the derivative of the Riemannian gradient at $$x$$ along $$u$$ (see Corollary 5.16 in the same book): $\Hess f(x)[u] = \Proj_x(\D\grad f(x)[u]).$

For the unit sphere, $$\Proj_x(u) = u - (x^\top u)x$$. It is then an exercise to work out the expressions above.

You can specify the Riemannian gradient and Hessian using the fields grad and hess (no e), as follows:

problem.cost = @(x) .5*x'*A*x;
problem.hess = @(x, u) problem.M.proj(x, A*u - (x'*A*x)*u); % optional

• Again, it is fine to omit hess, but try not to omit grad (see earlier comments).
• The input to grad is a point $$x$$. The output is a tangent vector at $$x$$, corresponding to $$\grad f(x)$$.
• The inputs to hess are a point $$x$$ and a tangent vector $$u$$ at $$x$$. The output is a tangent vector at $$x$$, corresponding to $$\Hess f(x)[u]$$.

Using automatic differentiation

Automatic differentiation (AD) is a means to obtain gradients and Hessians automatically, without the need to derive and implement formulas for them. This is usually slower than (good) hand-written code, but it can drastically reduce coding time, making it great (at least) for prototyping.

Manopt 7.0 added support for AD by building on Matlab’s Deep Learning Toolbox. To use it, simply define the cost and call manoptAD:

problem.cost = @(x) .5*x'*A*x;
problem = manoptAD(problem);

If it works, then the problem structure now includes access to the gradient and Hessian. If it does not work, check the following:

• Do you have the Deep Learning Toolbox? Type help dlarray.
• Is your Matlab version 2019 or later? Type version.
• Is your Manopt version 7.0 or later? Type manopt_version.
• Does your cost function itself throw an error? Try getCost(problem, problem.M.rand()).
• Was there an error message? AD does not work for all functions. Check out:
• The list of dlarray supported functions, and
• The docs for AD in Manopt, specifically: help manoptAD and help manoptADhelp.
• Keep in mind that dlarray support also depends on your versions of Matlab and the DL toolbox (e.g., support for AD with complex numbers was added in Matlab R2021b).
• If a function you need is not supported (e.g., diag), see help manoptADhelp for a possible replacement (e.g., cdiag and cdiagmat), or try to replace it with a direct implementation (e.g., cdiag corresponds to mydiag = @(A) A(1:size(A,1)+1:end);').
• Are you using the caching system (store) in the definition of the cost function? This does not pair well with AD (and AD has its own caching system), so it is better not to mix the two.

Xiaowen Jiang implemented manoptAD (and the system behind it) during an internship in 2021.

Fewer redundant computations with costgrad

Computing the gradient at $$x$$ often requires going through some of the computations that are also necessary to compute $$f(x).$$ In our example, both $$f(x) = \frac{1}{2}x^\top Ax$$ and $$\grad f(x) = Ax - (x^\top Ax)x$$ require computing $$Ax$$.

Since solvers tend to query both $$f$$ and its gradient at the same point $$x$$, it is beneficial to offer solvers the option to query both at the same time. You can do so with the field costgrad, as follows:

problem.costgrad = @(x) mycostgrad(A, x);
function [f, g] = mycostgrad(A, x)
Ax = A*x; % this product is computed only once
f = .5*x'*Ax;
if nargout == 2 % compute gradient only if requested
g = Ax - 2*f*x;
end
end

The input of costgrad is a point $$x$$. The outputs are the cost and (if requested) the Riemannian gradient at $$x$$. If you prefer to define the Euclidean gradient (as we often do) but still want to avoid redundant computations, read on.

We might also want to provide the Riemannian Hessian with

problem.hess = @(x, u) problem.M.proj(x, A*u - (x'*A*x)*u); % optional

However, now we see that the product A*x could also be reused there. The next section provides a more sophisticated approach which gives users full control over which computations to cache for reuse.

Using the store caching system

Computing anything at a point $$x$$ (e.g., $$f(x)$$) may produce intermediate results that could be reused for other computations at $$x$$ (e.g., the gradient). It may be beneficial to cache (that is, to store) some of those intermediate calculations.

For that purpose, within the run of a solver, Manopt manages a database of store structures, with a class called StoreDB. For each visited point $$x$$, a store structure is created in that database. StoreDB labels the points visited on the manifold with a key (an integer). This key uniquely identifies $$x$$: that is how the toolbox links $$x$$ with its associated store. Only the stores pertaining to the most recently used points are kept in memory.

Whenever a solver calls, say, the cost function at some point $$x$$, the toolbox searches for a store structure associated to that $$x$$ in the database (using its key). If there is one and if problem.cost (for example) admits store as an input and as an output, the store is passed to the cost function. The cost function then performs its duty and gets to modify the store structure at will.

The next time a function is called at the same point $$x$$ (say, problem.egrad), the same store structure is passed along, possibly modified, and stored again.

As soon as the solver goes on to explore a new point $$x'$$, a different store structure is created and maintained in the same way. If the solver later decides to return to the previous $$x$$, we get access to that earlier store again (unless it was purged from memory).

For our running example, the code below shows how we can use the caching system to implement the cost, gradient and Hessian without redundant computations. The principle is this:

• Write a function prepare which computes all the things you want to cache at a given point.
• Have cost, egrad, ehess etc. call prepare before they proceed with their own computations.

The code is given in two versions (use the tabs to switch):

• One version nests the functions prepare, cost, egrad and ehess within a top function (with a common scope).
• One version allows for those functions to be defined in independent scopes.

You may prefer one or the other depending on your use case.

function x = rayleighmin(A)

problem.M = spherefactory(size(A, 1));

problem.cost = @cost;
problem.ehess = @ehess; % optional

x = trustregions(problem);

% The functions below are nested:
% they can see the matrix A from the top scope.

function store = prepare(x, store)
if ~isfield(store, 'Ax')
store.Ax = A*x;
end
end
function [f, store] = cost(x, store)
store = prepare(x, store);
Ax = store.Ax;
f = .5*x'*Ax;
end
function [g, store] = egrad(x, store)
store = prepare(x, store);
Ax = store.Ax;
g = Ax;
end
function [h, store] = ehess(x, u, store)
% In general we would call prepare()
% here too, but for this example the
% Hessian is so simple that we don't
% need to.
% store = prepare(x, store);
h = A*u;
end

end
clear; clc; clf;

n = 1000;
A = randsym(n);
problem.M = spherefactory(n);

problem.cost = @(x, store) cost(A, x, store);
problem.ehess = @(x, u, store) ehess(A, x, u, store); % optional

x = trustregions(problem);

% The functions below appear at the end of the script
% (they could also be defined in separate files).
% They do not see A, so they need to receive it as an input.
% The function handles above are created in a part of the
% script that can see A.

function store = prepare(A, x, store)
if ~isfield(store, 'Ax')
store.Ax = A*x;
end
end
function [f, store] = cost(A, x, store)
store = prepare(A, x, store);
Ax = store.Ax;
f = .5*x'*Ax;
end
function [g, store] = egrad(A, x, store)
store = prepare(A, x, store);
Ax = store.Ax;
g = Ax;
end
function [h, store] = ehess(A, x, u, store)
% In general we would call prepare()
% here too, but for this example the
% Hessian is so simple that we don't
% need to.
% store = prepare(A, x, store);
h = A*u;
end

It is instructive to execute such code with the profiler activated and to look at how many times each line of code is executed. You should find that the matrix-vector products $$Ax$$ (computed only in prepare) are executed exactly as often as they should be. You can use Manopt counters to track these products and more.

The store structure also includes a field store.shared. The contents of that field are shared among all points the solver visited so far.

Cache reusable computations, not end results

Manopt automatically caches the value and the gradient of $$f$$ (both Euclidean and Riemannian) at each queried point $$x$$. There is no need to cache those manually. Rather, use the store to cache the most expensive (and reusable) intermediate computations.

Caching and the Hessian

Each store is associated to a point $$x$$. Thus, calls to ehess(x, u, store)and ehess(x, v, store) with the same x and two different tangent vectors u, v receive access to the same store. Therefore, only cache quantities that depend on x, not on u.

Lifespan of the cache

Solvers take care of deleting older information when it is no longer relevant. This should be good enough, but you can also cap the maximum number of store structures kept in memory with options.storedepth.

Caching and statsfun

The store structure is readable (but not writable) by options.statsfun. The store.shared mechanism was originally used together with statsfun to keep track of function calls. As of Manopt 5.0, it is much better to use Manopt counters for this purpose.

When you have access to storedb and a key associated to $$x$$ rather than to a specific store, the store of $$x$$ can be obtained as store = storedb.getStore(key). Put the modified store back into the database with storedb.set(store, key).

Access the shared memory directly as storedb.shared, not via store.shared. This is important: store might have a store.shared field, but when storedb and key are explicitly used, store.shared will not be populated or read on get/set.

If you are developing a solver and hence are managing the StoreDB object yourself:

Each point $$x$$ should be associated to a key, which is obtained by calling storedb.getNewKey(). From time to time, call storedb.purge() to reduce memory usage. Even better, as soon as you know that the store associated to a certain point is no longer useful, call storedb.remove(key) or storedb.removefirstifdifferent(key1, key2).

StoreDB is a handle class: its instances are passed by reference. This means that when a storedb object is passed as input to a function, and that function modifies the storedb object, the calling function sees the changes too (without the need to explicitly return the storedb object). Thus, each storedb object exists only once in memory. This makes for cleaner calling patterns and avoids unnecessary copies. This is not the case for the store structures though, which are passed by copy and thus must be returned if the changes are to be permanent.

All the ways to describe the cost

Manopt offers many ways to implement $$f$$, its gradient, its Hessian and more. This is done by adding function handles as fields in the problem structure. We list them all below.

You can mix and match what you include. If you provide more than one way to compute, say, the gradient, then the toolbox may use any and all of them: it makes an educated guess of which may be most efficient in context. Still, it is good practice to avoid redundancies.

Each function can be provided with one of three different calling patterns, as indicated below. The first one is the simplest and is perfectly fine for prototyping. The other calling patterns give explicit access to Manopt’s caching system, in two flavors:

• The normal way, with the store structure of the point x as an input and (possibly after modifications) as an output.
• The advanced way, with the storedb database and the key associated to x: see the collapsible note above.
Table of all the ways one can describe the cost function in Manopt, including its derivatives, approximations, preconditioners and a line-search hint.
Field name (problem."...") Description
cost $$f = f(x)$$
f = cost(x)
[f, store] = cost(x, store)
f = cost(x, storedb, key)
grad $$g = \grad f(x)$$
g = grad(x)
[g, store] = grad(x, store)
g = grad(x, storedb, key)
costgrad Computes both $$f = f(x)$$ and $$g = \grad f(x)$$.
[f, g] = costgrad(x)
[f, g, store] = costgrad(x, store)
[f, g] = costgrad(x, storedb, key)
egrad For submanifolds of a Euclidean space and for quotient spaces with a total space embedded in a Euclidean space, computes $$eg = \nabla f(x)$$: the gradient of $$f$$ “as if” it were defined in that Euclidean space. This is passed to M.egrad2rgradand is automatically cached for use with ehess.
eg = egrad(x)
[eg, store] = egrad(x, store)
eg = egrad(x, storedb, key)
partialgrad Assume the cost function problem.cost is a sum of many terms, as $$f(x) = \sum_{i=1}^{d} f_i(x)$$ where $$d$$ is specified as problem.ncostterms = d. For a subset $$I$$ of $$1\ldots d$$, partialgrad(x, I) computes the Riemannian gradient of the partial cost function $$f_I(x) = \sum_{i \in I} f_i(x)$$.
pg = partialgrad(x, I)
[pg, store] = partialgrad(x, I, store)
pg = partialgrad(x, I, storedb, key)
partialegrad Same as partialgrad but computes the Euclidean partial gradient. This is automatically transformed into a Riemannian partial gradient by Manopt.
peg = partialegrad(x, I)
[peg, store] = partialegrad(x, I, store)
peg = partialegrad(x, I, storedb, key)
approxgrad Approximation for the gradient of the cost at $$x$$. Solvers asking for the gradient when one is not provided automatically fall back to this approximation. If it is not provided either, a standard finite-difference approximation of the gradient based on the cost is built-in. This is slow because it involves generating an orthonormal basis of the tangent space at $$x$$ and computing a finite difference of the cost along each basis vector. This is useful almost exclusively for prototyping. Because of the limited accuracy, it may be necessary to increase options.tolgradnorm when using this feature. See /solvers/gradientapproximations.
g = approxgrad(x)
[g, store] = approxgrad(x, store)
g = approxgrad(x, storedb, key)
subgrad Returns a Riemannian subgradient of the cost function at $$x$$, with a tolerance tol which is a nonnegative real number. If you wish to return the minimal norm subgradient (which may help solvers), see smallestinconvexhull on the tools page.
g = subgrad(x, tol)
[g, store] = subgrad(x, tol, store)
g = subgrad(x, tol, storedb, key)
diff $$d = \D f(x)[u]$$ defines directional derivatives. If the gradient exists, it can be computed from this too (slowly.)
d = diff(x, u)
[d, store] = diff(x, u, store)
d = diff(x, u, storedb, key)
hess $$h = \Hess f(x)[u]$$, where $$u$$ represents a tangent vector.
h = hess(x, u)
[h, store] = hess(x, u, store)
h = hess(x, u, storedb, key)
ehess For the same settings as with egrad, this computes $$eh = \nabla^2 f(x)[u]$$: the Hessian of $$f$$ along $$u$$ “as if” it were defined in the embedding Euclidean space. This is passed to M.ehess2rhess and thus requires the Euclidean gradient to be accessible too (egrad). Input $$u$$ is a representation of the tangent vector. You may want to call M.tangent2ambient(x, u) to obtain the ambient space equivalent of $$u$$. The output eh should be a vector in the ambient space.
eh = ehess(x, u)
[eh, store] = ehess(x, u, store)
eh = ehess(x, u, storedb, key)
approxhess This can be any mapping from the tangent space at $$x$$ to itself. Often, one would like for it to be a linear, symmetric operator. Solvers asking for the Hessian when one is not provided automatically fall back to this approximate Hessian. If it is not provided either, a standard finite-difference approximation of the Hessian based on the gradient is built-in. See /solvers/hessianapproximations.
h = approxhess(x, u)
[h, store] = approxhess(x, u, store)
h = approxhess(x, u, storedb, key)
precon $$v = \operatorname{Prec}(x)[u]$$, where $$\operatorname{Prec}(x)$$ is a preconditioner for the Hessian $$\Hess f(x)$$, that is, $$\operatorname{Prec}(x)$$ is a symmetric, positive-definite linear operator (w.r.t. the Riemannian metric) on the tangent space at $$x$$. Ideally, it is cheap to compute and such that solving a linear system in $\operatorname{Prec}^{1/2}(x) \circ \Hess f(x) \circ \operatorname{Prec}^{1/2}(x)$ is easier than without the preconditioner, i.e., it should approximate the inverse of the Hessian. See /solvers/preconditioners.
v = precon(x, u)
[v, store] = precon(x, u, store)
v = precon(x, u, storedb, key)
sqrtprecon $$v = \operatorname{Prec}^{1/2}(x)[u]$$, where $$\operatorname{Prec}^{1/2}(x)$$ is an (operator) square root of a preconditioner for the Hessian $$\Hess f(x)$$, that is, $$\operatorname{Prec}^{1/2}(x)$$ is a symmetric, positive-definite linear operator (w.r.t. the Riemannian metric) on the tangent space at $$x$$, and applying it twice should amount to applying $$\operatorname{Prec}(x)$$ once. Solvers typically use precon rather than sqrtprecon, but some tools (such as hessianspectrum) can use sqrtprecon to speed up computations.
v = sqrtprecon(x, u)
[v, store] = sqrtprecon(x, u, store)
v = sqrtprecon(x, u, storedb, key)
linesearch Given a point $$x$$ and a tangent vector $$u$$ at $$x$$, assume $$u$$ is a descent direction. This means there exists $$t > 0$$ such that $$\phi(t) < \phi(0)$$ with $\phi(t) = f(\Retr_x(td)).$ Line-search algorithms, which are used by some solvers such as steepestdescent and conjugategradient are designed to (approximately) minimize $$\phi$$ at each iteration. There are built-in, generic ways of doing this. If you have additional structure in your problem that enables you to take a good guess at what $$t$$ should be, then you can specify it here, in this function handle. This (very much optional) function should return a positive $$t > 0$$ such that $$t$$ is a good guess of where to look for a minimizer of $$\phi$$. The line-search algorithm (if it decides to use this information) starts by looking at the step $$td$$, and decides to accept it or not based on its internal rules. See the linesearch option on the solvers page for details on available line-search algorithms and how to pick one. See low_rank_matrix_completion for an example from the literature.
t = linesearch(x, u)
[t, store] = linesearch(x, u, store)
t = linesearch(x, u, storedb, key)

Accessing the cost, gradient and Hessian

Given a problem structure with a manifold problem.M and some description of the cost function $$f$$, Manopt provides access to $$f$$ and its derivatives with the tools getCost, getGradient and getHessian. Here is an example:

A = randsym(10);
problem.M = spherefactory(10);
problem.cost = @(x) .5*x'*A*x;

% For illustration, pick a random point on M
x = problem.M.rand();
% Compute f(x)
f = getCost(problem, x);
% The Riemannian norm of the gradient is:
problem.M.norm(x, g)

% Pick a random tangent vector at x
u = problem.M.randvec(x);
% Compute Hess f(x)[u] (Riemannian)
Hu = getHessian(problem, x, u);
% The Hessian quadratic form <u, Hess f(x)[u]>_x is:
problem.M.inner(x, u, Hu)

There are more functions of this type: see the core tools page.

All of these tools also accept calls with store and (storedb, key) (from the caching system described above). This is mostly useful inside the code for a solver.

To compute the eigenvalues of the Hessian, check out the tools hessianspectrum, hessianextreme and hessianmatrix on the tools page.