  This paper concerns singular value decomposition (SVD)-based computable
formulas and bounds for the condition number of the Total Least Squares (TLS)
problem. For the TLS problem with the coefficient matrix $A$ and the right-hand
side $b$, a new closed formula is presented for the condition number. Unlike an
important result in the literature that uses the SVDs of both $A$ and $[A,\
b]$, our formula only requires the SVD of $[A,\ b]$. Based on the closed
formula, both lower and upper bounds for the condition number are derived. It
is proved that they are always sharp and estimate the condition number
accurately. A few lower and upper bounds are further established that involve
at most the smallest two singular values of $A$ and of $[A,\ b]$. Tightness of
these bounds is discussed, and numerical experiments are presented to confirm
our theory and to demonstrate the improvement of our upper bounds over the two
upper bounds due to Golub and Van Loan as well as Baboulin and Gratton. Such
lower and upper bounds are particularly useful for large scale TLS problems
since they require the computation of only a few singular values of $A$ and
$[A, \ b]$ other than all the singular values of them.
