p-variation

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In mathematical analysis, p-variation is a collection of seminorms on functions from an ordered set to a metric space, indexed by a real number p1. p-variation is a measure of the regularity or smoothness of a function. Specifically, if f:I(M,d), where (M,d) is a metric space and I a totally ordered set, its p-variation is:

fp-var=(supDtkDd(f(tk),f(tk1))p)1/p

where D ranges over all finite partitions of the interval I.

The p variation of a function decreases with p. If f has finite p-variation and g is an α-Hölder continuous function, then gf has finite pα-variation.

The case when p is one is called total variation, and functions with a finite 1-variation are called bounded variation functions.

This concept should not be confused with the notion of p-th variation along a sequence of partitions, which is computed as a limit along a given sequence (Dn) of time partitions:[1]

[f]p=(limntknDnd(f(tkn),f(tk1n))p)


For example for p=2, this corresponds to the concept of quadratic variation, which is different from 2-variation.

One can interpret the p-variation as a parameter-independent version of the Hölder norm, which also extends to discontinuous functions.

If f is αHölder continuous (i.e. its α–Hölder norm is finite) then its 1α-variation is finite. Specifically, on an interval [a,b], f1α-varfα(ba)α.

If p is less than q then the space of functions of finite p-variation on a compact set is continuously embedded with norm 1 into those of finite q-variation. I.e. fq-varfp-var. However unlike the analogous situation with Hölder spaces the embedding is not compact. For example, consider the real functions on [0,1] given by fn(x)=xn. They are uniformly bounded in 1-variation and converge pointwise to a discontinuous function f but this not only is not a convergence in p-variation for any p but also is not uniform convergence.

Application to Riemann–Stieltjes integration

If f and g are functions from [ab] to with no common discontinuities and with f having finite p-variation and g having finite q-variation, with 1p+1q>1 then the Riemann–Stieltjes Integral

abf(x)dg(x):=lim|D|0tkDf(tk)[g(tk+1)g(tk)]

is well-defined. This integral is known as the Young integral because it comes from Template:Harvtxt.[2] The value of this definite integral is bounded by the Young-Loève estimate as follows

|abf(x)dg(x)f(ξ)[g(b)g(a)]|Cfp-vargq-var

where C is a constant which only depends on p and q and ξ is any number between a and b.[3] If f and g are continuous, the indefinite integral F(w)=awf(x)dg(x) is a continuous function with finite q-variation: If astb then Fq-var;[s,t], its q-variation on [s,t], is bounded by Cgq-var;[s,t](fp-var;[s,t]+f;[s,t])2Cgq-var;[s,t](fp-var;[a,b]+f(a)) where C is a constant which only depends on p and q.[4]

Differential equations driven by signals of finite p-variation, p < 2

A function from d to e × d real matrices is called an e-valued one-form on d.

If f is a Lipschitz continuous e-valued one-form on d, and X is a continuous function from the interval [ab] to d with finite p-variation with p less than 2, then the integral of f on X, abf(X(t))dX(t), can be calculated because each component of f(X(t)) will be a path of finite p-variation and the integral is a sum of finitely many Young integrals. It provides the solution to the equation dY=f(X)dX driven by the path X.

More significantly, if f is a Lipschitz continuous e-valued one-form on e, and X is a continuous function from the interval [ab] to d with finite p-variation with p less than 2, then Young integration is enough to establish the solution of the equation dY=f(Y)dX driven by the path X.[5]

Differential equations driven by signals of finite p-variation, p ≥ 2

The theory of rough paths generalises the Young integral and Young differential equations and makes heavy use of the concept of p-variation.

For Brownian motion

p-variation should be contrasted with the quadratic variation which is used in stochastic analysis, which takes one stochastic process to another. In particular the definition of quadratic variation looks a bit like the definition of p-variation, when p has the value 2. Quadratic variation is defined as a limit as the partition gets finer, whereas p-variation is a supremum over all partitions. Thus the quadratic variation of a process could be smaller than its 2-variation. If Wt is a standard Brownian motion on [0, T], then with probability one its p-variation is infinite for p2 and finite otherwise. The quadratic variation of W is [W]T=T.

Computation of p-variation for discrete time series

For a discrete time series of observations X0,...,XN it is straightforward to compute its p-variation with complexity of O(N2). Here is an example C++ code using dynamic programming:

double p_var(const std::vector<double>& X, double p) {
	if (X.size() == 0)
		return 0.0;
	std::vector<double> cum_p_var(X.size(), 0.0);   // cumulative p-variation
	for (size_t n = 1; n < X.size(); n++) {
		for (size_t k = 0; k < n; k++) {
			cum_p_var[n] = std::max(cum_p_var[n], cum_p_var[k] + std::pow(std::abs(X[n] - X[k]), p));
		}
	}
	return std::pow(cum_p_var.back(), 1./p);
}

There exist much more efficient, but also more complicated, algorithms for -valued processes[6] [7] and for processes in arbitrary metric spaces.[7]

References

Template:Reflist