Fréchet derivative

In mathematics, the Fréchet derivative is a derivative defined on normed spaces. Named after Maurice Fréchet, it is commonly used to generalize the derivative of a real-valued function of a single real variable to the case of a vector-valued function of multiple real variables, and to define the functional derivative used widely in the calculus of variations.

Generally, it extends the idea of the derivative from real-valued functions of one real variable to functions on normed spaces. The Fréchet derivative should be contrasted to the more general Gateaux derivative which is a generalization of the classical directional derivative.

The Fréchet derivative has applications to nonlinear problems throughout mathematical analysis and physical sciences, particularly to the calculus of variations and much of nonlinear analysis and nonlinear functional analysis.


Let V and W be normed vector spaces, and   be an open subset of V. A function f : UW is called Fréchet differentiable at   if there exists a bounded linear operator   such that


The limit here is meant in the usual sense of a limit of a function defined on a metric space (see Functions on metric spaces), using V and W as the two metric spaces, and the above expression as the function of argument h in V. As a consequence, it must exist for all sequences   of non-zero elements of V that converge to the zero vector   Equivalently, the first-order expansion holds, in Landau notation


If there exists such an operator A, it is unique, so we write   and call it the Fréchet derivative of f at x. A function f that is Fréchet differentiable for any point of U is said to be C1 if the function


is continuous (  denotes the space of all bounded linear operators from   to  ). Note that this is not the same as requiring that the map   be continuous for each value of   (which is assumed; bounded and continuous are equivalent).

This notion of derivative is a generalization of the ordinary derivative of a function on the real numbers   since the linear maps from   to   are just multiplication by a real number. In this case, Df(x) is the function  .


A function differentiable at a point is continuous at that point.

Differentiation is a linear operation in the following sense: if f and g are two maps VW which are differentiable at x, and c is a scalar (a real or complex number), then the Fréchet derivative obeys the following properties:


The chain rule is also valid in this context: if f : UY is differentiable at xU, and g : YW is differentiable at y = f(x), then the composition gf is differentiable in x and the derivative is the composition of the derivatives:


Finite dimensionsEdit

The Fréchet derivative in finite-dimensional spaces is the usual derivative. In particular, it is represented in coordinates by the Jacobian matrix.

Suppose that f is a map,   with U an open set. If f is Fréchet differentiable at a point aU, then its derivative is


where Jf(a) denotes the Jacobian matrix of f at a.

Furthermore, the partial derivatives of f are given by


where {ei} is the canonical basis of   Since the derivative is a linear function, we have for all vectors   that the directional derivative of f along h is given by


If all partial derivatives of f exist and are continuous, then f is Fréchet differentiable (and, in fact, C1). The converse is not true; the function


is Fréchet differentiable and yet fails to have continuous partial derivatives at  .

Example in infinite dimensionsEdit

One of the simplest (nontrivial) examples in infinite dimensions, is the one where the domain is a Hilbert space ( ) and the function in interest is the norm. So consider  .

First assume that  . Then we claim that the Fréchet derivative of   at   is the linear functional  , defined by




Using continuity of the norm and inner product we obtain:


As   and because of the Cauchy-Bunyakovsky-Schwarz inequality


is bounded by   thus the whole limit vanishes.

Now we show that at   the norm is not differentiable, i.e. there does not exist bounded linear functional   such that the limit in question to be  . Let   be any linear functional. Riesz representation theorem tells us that   could be defined by   for some  . Consider


In order for the norm to be differentiable at   we must have


We will show that this is not true for any  . If   obviously   independently of  , hence this is not the derivative. Assume  . If we take   tending to zero in the direction of   (i.e.  , where  ) then  , hence


(If we take   tending to zero in the direction of   we would even see this limit does not exist since in this case we will obtain  ).

The result just obtained agrees with the results in finite dimensions.

Relation to the Gateaux derivativeEdit

A function f : UVW is called Gateaux differentiable at x ∈ U if f has a directional derivative along all directions at x. This means that there exists a function g : VW such that


for any chosen vector h in V, and where t is from the scalar field associated with V (usually, t is real).[1]

If f is Fréchet differentiable at x, it is also Gateaux differentiable there, and g is just the linear operator A = Df(x).

However, not every Gateaux differentiable function is Fréchet differentiable. This is analogous to the fact that the existence of all directional derivatives at a point does not guarantee total differentiability (or even continuity) at that point. For example, the real-valued function f of two real variables defined by


is continuous and Gateaux differentiable at (0, 0), with its derivative being


The function g is not a linear operator, so this function is not Fréchet differentiable.

More generally, any function of the form  , where r and φ are the polar coordinates of (x,y), is continuous and Gateaux differentiable at (0,0) if g is differentiable at 0 and  , but the Gateaux derivative is only linear and the Fréchet derivative only exists if h is sinusoidal.

In another situation, the function f given by


is Gateaux differentiable at (0, 0), with its derivative there being g(ab) = 0 for all (ab), which is a linear operator. However, f is not continuous at (0, 0) (one can see by approaching the origin along the curve (t, t3)) and therefore f cannot be Fréchet differentiable at the origin.

A more subtle example is


which is a continuous function that is Gateaux differentiable at (0, 0), with its derivative being g(ab) = 0 there, which is again linear. However, f is not Fréchet differentiable. If it were, its Fréchet derivative would coincide with its Gateaux derivative, and hence would be the zero operator; hence the limit


would have to be zero, whereas approaching the origin along the curve (t, t2) shows that this limit does not exist.

These cases can occur because the definition of the Gateaux derivative only requires that the difference quotients converge along each direction individually, without making requirements about the rates of convergence for different directions. Thus, for a given ε, although for each direction the difference quotient is within ε of its limit in some neighborhood of the given point, these neighborhoods may be different for different directions, and there may be a sequence of directions for which these neighborhoods become arbitrarily small. If a sequence of points is chosen along these directions, the quotient in the definition of the Fréchet derivative, which considers all directions at once, may not converge. Thus, in order for a linear Gateaux derivative to imply the existence of the Fréchet derivative, the difference quotients have to converge uniformly for all directions.

The following example only works in infinite dimensions. Let X be a Banach space, and φ a linear functional on X that is discontinuous at x = 0 (a discontinuous linear functional). Let


Then f(x) is Gateaux differentiable at x = 0 with derivative 0. However, f(x) is not Fréchet differentiable since the limit


does not exist.

Higher derivativesEdit

If f : UW is a differentiable function at all points in an open subset U of V, it follows that its derivative


is a function from U to the space L(V, W) of all bounded linear operators from V to W. This function may also have a derivative, the second order derivative of f, which, by the definition of derivative, will be a map


To make it easier to work with second-order derivatives, the space on the right-hand side is identified with the Banach space L2(V × V, W) of all continuous bilinear maps from V to W. An element φ in L(V, L(V, W)) is thus identified with ψ in L2(V × V, W) such that for all x and y in V,


(Intuitively: a function φ linear in x with φ(x) linear in y is the same as a bilinear function ψ in x and y).

One may differentiate


again, to obtain the third order derivative, which at each point will be a trilinear map, and so on. The n-th derivative will be a function


taking values in the Banach space of continuous multilinear maps in n arguments from V to W. Recursively, a function f is n + 1 times differentiable on U if it is n times differentiable on U and for each x in U there exists a continuous multilinear map A of n + 1 arguments such that the limit


exists uniformly for h1, h2, ..., hn in bounded sets in V. In that case, A is the (n + 1)st derivative of f at x.

Moreover, we may obviously identify a member of the space   with a linear map   through the identification  , thus viewing the derivative as a linear map.

Partial Fréchet derivativesEdit

In this section, we extend the usual notion of partial derivatives which is defined for functions of the form  , to functions whose domains and target spaces are arbitrary (real or complex) Banach spaces. To do this, let   and   be Banach spaces (over the same field of scalars), and let   be a given function, and fix a point  . We say that   has an i-th partial differential at the point   if the function   defined by


is Fréchet differentiable at the point   (in the sense described above). In this case, we define  , and we call   the i-th partial derivative of   at the point  . It is important to note that   is a linear transformation from   into  . Heuristically, if   has an i-th partial differential at  , then   linearly approximates the change in the function   when we fix all of its entries to be   for  , and we only vary the i-th entry. We can express this in the Landau notation as


Generalization to topological vector spacesEdit

The notion of the Fréchet derivative can be generalized to arbitrary topological vector spaces (TVS) X and Y. Letting U be an open subset of X that contains the origin and given a function   such that   we first define what it means for this function to have 0 as its derivative. We say that this function f is tangent to 0 if for every open neighborhood of 0,   there exists an open neighborhood of 0,   and a function   such that


and for all t in some neighborhood of the origin,  

We can now remove the constraint that   by defining f to be Fréchet differentiable at a point   if there exists a continuous linear operator   such that  , considered as a function of h, is tangent to 0. (Lang p. 6)

If the Fréchet derivative exists then it is unique. Furthermore, the Gateaux derivative must also exist and be equal the Fréchet derivative in that for all  ,


where   is the Fréchet derivative. A function that is Fréchet differentiable at a point is necessarily continuous there and sums and scalar multiples of Fréchet differentiable functions are differentiable so that the space of functions that are Fréchet differentiable at a point form a subspace of the functions that are continuous at that point. The chain rule also holds as does the Leibniz rule whenever Y is an algebra and a TVS in which multiplication is continuous.

See alsoEdit


  1. ^ It is common to include in the definition that the resulting map g must be a continuous linear operator. We avoid adopting this convention here to allow examination of the widest possible class of pathologies.


  • Cartan, Henri (1967), Calcul différentiel, Paris: Hermann, MR 0223194.
  • Dieudonné, Jean (1969), Foundations of modern analysis, Boston, MA: Academic Press, MR 0349288.
  • Lang, Serge (1995), Differential and Riemannian Manifolds, Springer, ISBN 0-387-94338-2.
  • Munkres, James R. (1991), Analysis on manifolds, Addison-Wesley, ISBN 978-0-201-51035-5, MR 1079066.
  • Previato, Emma, ed. (2003), Dictionary of applied math for engineers and scientists, Comprehensive Dictionary of Mathematics, London: CRC Press, ISBN 978-1-58488-053-0, MR 1966695.
  • Coleman, Rodney, ed. (2012), Calculus on Normed Vector Spaces, Universitext, Springer, ISBN 978-1-4614-3894-6.

External linksEdit

  • B. A. Frigyik, S. Srivastava and M. R. Gupta, Introduction to Functional Derivatives, UWEE Tech Report 2008-0001.
  • This webpage is mostly about basic probability and measure theory, but there is nice chapter about Frechet derivative in Banach spaces (chapter about Jacobian formula). All the results are given with proof.