Can $R^2$ be greater than 1? The Wikipedia page on R2 says $R^2$ can take on a value greater than 1. I don't see how this is possible. 

Values of $R^2$ outside the range 0 to 1 can occur where it is used to measure the agreement between observed and modeled values and where the "modeled" values are not obtained by linear regression and depending on which formulation of $R^2$ is used. If the first formula above is used, values can be less than zero. If the second expression is used, values can be greater than one. 

That quote refers to the "second expression" but I don't see a second expression on the page. 
Is there any scenario where $R^2$ can be greater than 1? I am thinking about this question for nonlinear regression, but would like to get a general answer.
[For someone looking at this page with the opposite question in mind: Yes; $R^2$ can be negative. This happens when you fit a model that fits the data worse than a horizontal line. This would usually be due to a mistake in selecting a model or constraints.]
 A: By definition, $R^2 = 1 - SS_e/SS_t$ where both SS-terms are a sum of squares and thus nonnegative. The maximum is attained at $SS_e=0$ resulting in $R^2=1$.
A: I found the answer, so will post the answer to my question. As Martijn pointed out, with linear regression you can compute $R^2$ by two equivalent expressions:
$R^2 = 1- SS_e/SS_t = SS_m/SS_t$
With nonlinear regression, you cannot sum the sum-of-squares of residuals and sum-of-squares of the regression to obtain the total sum-of-squares. That equation is simply not true. So the equation above is not right. Those two experessions compute two different values for $R^2$. 
The only equation that makes sense and is (I think) universally used is:
$R^2 = 1- SS_e/SS_t$
Its value is never greater than 1.0, but it can be negative when you fit the wrong model (or wrong constraints) so the $SS_e$ (sum-of-squares of residuals) is greater than $SS_t$ (sum of squares of the difference between actual and mean Y values). 
The other equation is not used with nonlinear regression:
$R^2 = SS_m/SS_t$
But if this equation were used, it results in $R^2$ greater than 1.0 in cases where the model fits the data really poorly so $SS_m$ is larger than $SS_t$. This happens when the fit of the model is worse than the fit of a horizontal line, the same cases that lead to $R^2$<0 with the other equation. 
Bottom line: $R^2$ can be greater than 1.0 only when an invalid (or nonstandard) equation is used to compute $R^2$ and when the chosen model (with constraints, if any) fits the data really poorly, worse than the fit of a horizontal line.
