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Hi Im fairly new to SDE theory and am struggling with the difference between a weak ( or martingale ) solution and a strong solution to an SDE :

$$ d(X_{t})=b(t,X_{t})dt + \sigma(t,X_{t})dW_{t} $$

Are these two differences and what do they really mean in detail?

  1. For a strong solution we are given an initial value, whereas for weak solutions only a probability law?

  2. For strong solutions we know what probability space we are working in and have a Brownian Motion $W$ in that space. For a weak solution we can only say that there exists some probability space where the SDE holds (with a new brownian motion in the space).

As you can tell I am confused with this topic some clarifications would be amazing.

saz
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Monty
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1 Answers1

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The main difference between weak and strong solutions is indeed that for strong solutions we are given a Brownian motion on a given probability space whereas for weak solutions we are free to choose the Brownian motion and the probability space.

Definition: Let $(B_t)_{t \geq 0}$ be a Brownian motion with admissible filtration $(\mathcal{F}_t)_{t \geq 0}$. A progressively measurable process $(X_t,\mathcal{F}_t)$ is a strong solution with initial condition $\xi$ if $$X_t-X_0 = \int_0^t \sigma(s,X_s) \, dB_s + \int_0^t b(s,X_s) \, ds, \qquad X_0 =\xi \tag{1}$$ holds almost surely for all $t \geq 0$.

Definition: A stochastic process $(X_t,\mathcal{F}_t)$ on some probability space $(\Omega,\mathcal{F},\mathbb{P})$ is called a weak solution with initial distribution $\mu$ if there exists a Brownian motion $(B_t)_{t \geq 0}$ on $(\Omega,\mathcal{F},\mathbb{P})$ such that $(\mathcal{F}_t)_{t \geq 0}$ is an admissible filtration, $\mathbb{P}(X_0 \in \cdot) = \mu(\cdot)$ and $$X_t-X_0 = \int_0^t \sigma(s,X_s) \, dB_s + \int_0^t b(s,X_s) \, ds$$ holds almost surely for all $t \geq 0$.

As a consequence of these definitions, we have to consider different notions of uniqueness. For strong solutions we are typically looking for pathwise unique solutions, i.e. if $(X_t^{(1)})_{t \geq 0}$ and $(X_t^{(2)})_{t \geq 0}$ are strong solutions to $(1)$ with the same initial condition, then pathwise uniqueness means $$\mathbb{P} \left( \sup_{t \geq 0} |X_t^{(1)}-X_t^{(2)}|=0 \right)=1.$$ As the following simple example shows it doesn't make sense to talk about pathwise uniqueness of weak solutions.

Example 1: Let $(W_t^{(1)})_{t \geq 0}$ and $(W_t^{(2)})_{t \geq 0}$ be two Brownian motions (possibly defined on different probability spaces), then both $X_t^{(1)} := W_t^{(1)}$ and $X_t^{(2)} := W_t^{(2)}$ are weak solutions to the SDE $$dX_t = dB_t, \qquad X_0 = 0$$ Why? According to the definition we are free choose the driving Brownian motion, so we can set $B_t^{(1)} := W_t^{(1)}$ and $B_t^{(2)} := W_t^{(2)}$, respectively, and then $$dX_t^{(i)} = dB_t^{(i)} \quad \text{for $i=1,2$}.$$

What do we learn from this? Since weak solutions might be defined on different probability spaces, there is no (immediate) way to compute probabilities of the form $\mathbb{P}(X_t^{(1)}=X_t^{(2)})$ for two weak solutions $(X_t^{(1)})_{t \geq 0}$ and $(X_t^{(2)})_{t \geq 0}$, and therefore we cannot even attempt to talk about pathwise uniqueness. For the same reason, it doesn't make sense to talk about pointwise initial conditions $\xi$ for weak solutions (... for this we would need to fix some probability space on which $\xi$ lives...); instead we only prescribe the initial distribution of $X_0$.

The next example shows that we cannot expect to have pathwise uniqueness even if the weak solutions are defined on the same probability space.

Example 2: Let $(W_t)_{t \geq 0}$ be a Brownian motion. It follows from Example 1 that $X_t^{(1)} := W_t$ and $X_t^{(2)} := -W_t$ are weak solutions to the SDE $$dX_t = dB_t, \qquad X_0 =0.$$ Clearly, $\mathbb{P}(X_t^{(1)} = X_t^{(2)}) = \mathbb{P}(W_t=0)=0$.

The "good" notion of uniqueness for weak solutions is weak uniqueness, i.e. uniqueness in distribution (= the solutions have the same finite-dimensional distributions).

Typically it is much easier to prove the existence (and/or uniqueness of) a weak solution the the existence (and/or uniqueness) of a strong solution.

Example 3: The SDE $$dX_t = - \text{sgn}\,(X_t) \, dB_t, \qquad X_0 = 0 \tag{2}$$ has a weak solution but no strong solution.

Let's prove that the SDE has a weak solution. Let $(X_t,\mathcal{F}_t)_{t \geq 0}$ be some Brownian motion and define $$W_t := -\int_0^t \text{sgn} \, (X_s) \, dX_s.$$ It follows from Lévy's characterization that $(W_t,\mathcal{F}_t)$ is also a Brownian motion. Since $$dW_t = - \text{sgn} \, (X_t) \, dX_t$$ implies $$dX_t = - \text{sgn} \, (X_t) \, dW_t$$ this means that $(X_t)_{t \geq 0}$ is a weak solution to $(2)$. For a proof that a strong solution does not exist see e.g. Example 19.16 in the book by Schilling & Partzsch on Brownian motion.

Let me finally mention that weak solutions are closely related to martingale problems; in this answer I tried to give some insights on the connection between the two notions.

saz
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  • Why is the text after your first two definitions. not formatting properly... Thanks for your answer by the way. – Monty Nov 13 '18 at 08:47
  • @Monty Thank you; I fixed it. – saz Nov 13 '18 at 08:54
  • I've seen that some authors use the notions of weak and strong solutions in a slightly different way. Sometimes $X$ is only called a strong solution if it is adapted with respect to the filtration generated by $B$. And usually the probability space and filtration are part of a weak solution (If I understand your definiton correctly, you assume that at least the probability space is given beforehand). – 0xbadf00d Feb 21 '19 at 21:38
  • Oh, and are you implicitly assuming the filtrations to be complete? Otherwise, there might be a problem in defininig the stochastic integral as a local martingale. (Just asking cause I'm currently trying to figure out how the notions of weak/storng solutions are commonly used in the literature.) – 0xbadf00d Feb 21 '19 at 21:45
  • @0xbadf00d 1. No, for weak solutions the probability space is not given beforehand. As I mention in my answer, weak solutions might be defined on different probability spaces. 2. Yes, let's assume that the filtration is complete. (I did write this answer to give the OP an idea what the difference between the two notions is; not to puzzle about the technical details.) – saz Feb 22 '19 at 07:46
  • Thank you for your comment. What about strong solutions? Sometimes this only means that the filtered probability space and Brownian motion are given beforehand, but some authors require a strong solution to be adapted to the filtration generated by the Brownian motion. – 0xbadf00d Feb 22 '19 at 08:04
  • @0xbadf00d Sorry, I don't understand your question. You mean whether a strong solution is adapted to the canonical filtration or just to some admissible filtration...? Obviously, some assumptions on the measurability are needed to ensure that all the appearing integrals are well-defined. – saz Feb 22 '19 at 09:18
  • I mean adapted to the canonical filtration. – 0xbadf00d Feb 22 '19 at 10:48
  • @0xbadf00d So what's your question....? From my point of view, a strong solution does not need to be measurable wrt to the canonical filtration; it's enough if it is measurable wrt to an admissible filtration. Obviously, other books/authors may use different definitions... – saz Feb 22 '19 at 10:54
  • The question is: Even when your solution is only required to be adapted to an admissible filtration, isn't it always adapted to the canonical filtration (as long as $X_0=\xi$ is independent of $B$ or assumed to be $\sigma(B_0)$-measurable)? – 0xbadf00d Feb 22 '19 at 11:08
  • @0xbadf00d No, why should it? If $X_t := \xi$ for some $\xi$ which is independent of $B$, then $X$ fails, in general, to be adapted to the canonical filtration but trivially satisfies the SDE $dX_t = 0 , dB_t + 0 , dt$. Similarly,you can consider $X_t := t \xi$ which has a $\sigma(B_0)$-measurable initial conditon, satisfies the ODE $dX_t = \xi , dt$ and fails, in general, to be measurable wrt to the canonical filtration. – saz Feb 22 '19 at 11:15
  • I see, but isn't that what is proved here on page 55 after "it remains to prove …"? Am I missing something or is their proof wrong? (Ah, I guess the point is that their initial condition is non-random) – 0xbadf00d Feb 22 '19 at 12:31
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    @0xbadf00d If you have (deterministic) Lipschitz coefficients and a deterministic initial condition, then the solution is measurable wrt to the canonical filtration... that's what they show there. It doesn't contradict what I was saying.. – saz Feb 22 '19 at 12:33
  • Why is their $Y^n$ adapted to the canonical filtration? see https://math.stackexchange.com/questions/3048457/is-the-strong-solution-of-a-sde-adapted-to-the-filtration-generated-by-the-drivi – 0xbadf00d Feb 22 '19 at 16:49
  • @0xbadf00d Why do you ask me? I'm not familiar with the source you are using and I'm also not familiar with this particular proof... – saz Feb 22 '19 at 17:11
  • Since you've said that the solution is measurable wrt the canonical filtration, if the initial condition is deterministic. And I don't know how we see that. – 0xbadf00d Feb 22 '19 at 18:49
  • @saz I've been reading through your discussion and actually I do agree with 0xbadf00d , for instance in Rebuz&Yor the definition of "strong solution" is a pair $(X;B)$ on some filtered probability space such that $X$ is measurable with respect to the canonical filtration. – Chaos Mar 13 '20 at 09:14
  • @0xbadf00d regarding your last question "the solution is measurable wrt the canonical filtration if the initial condition is deterministic". Take a look at Ikeda&Watanabe's definition $IV.1.6$. – Chaos Mar 13 '20 at 09:29
  • @saz Very nice example with $-\operatorname{sgn}(X_s)dX_s$ I've never seen this one before; but so cute and clear! – ABIM Feb 09 '23 at 19:15
  • It may seem from this answer that a weak solution is in fact a strong solution for the right Brownian motion, but this is not the case. Indeed, the process $X_t$ in the weak solution need not be adapted to the filtration generated by the Brownian motion and initial condition. I think this is a key difference which deserves emphasis. – Roberto Rastapopoulos Jul 25 '23 at 12:53