Hi Mark,

yes thanks. That document describes the total least-square in (4). Would the cvx implementation of this look like:

Assume a simple one dimensional problem in the form of

Yactual+tildeY = (Xactual + tildeX)*a (a being the scalar to be estimated)

Given are vectors of n=1000 measurements X and Y with errors. Then,

```
cvx_begin
variable tildeX(1000,1)
variable tildeY(1000,1)
variable Xactual(1000,1)
variable Yactual(1000,1)
variable a(1,1)
minimize(sum(sum_square([tildeX tildeY])))
subject to
Yactual+tildeY == (Xactual + tildeX)*a
Yactual+tildeY == Y
Xactual + tildeX == X
cvx_end
```