1) Sage seems unable to reduce `erf(x) == erf(y)` to `x == y`. How can I help this along?
solve(erf(x) == erf(y), x)[0].simplify_full() Actual output: x == inverse_erf(erf(y)) Expected output: x == y I had expected that sage would trivially reduce `inverse_erf(erf(y))` to `y`. 2) This output references 'inverse_erf', which doesn't seem to be importable t from anywhere in sage. Am I correct? --- My concrete problem is re-deriving the formula for the normal-distribution cdf. I get a good solution from sage, but fail in showing that it's equivalent to a known solution because: var('x sigma mu') assume(sigma > 0) eq3 = (-erf((sqrt(2)*mu - sqrt(2)*x)/(2*sigma)) == -erf((sqrt(2)*(mu - x))/(2*sigma))) bool(eq3) Actual output: False Expected output: True However this quite similar formula works fine: eq3 = (-erf(sqrt(2)*mu - sqrt(2)*x) == -erf(sqrt(2)*(mu - x))) bool(eq3) Output: True --- Include: Platform (CPU) -- x86_64 Operating System -- Ubuntu 13.10 Exact version of Sage (command: "version()") -- 'Sage Version 5.13, Release Date: 2013-12-15' -- You received this message because you are subscribed to the Google Groups "sage-support" group. To unsubscribe from this group and stop receiving emails from it, send an email to sage-support+unsubscr...@googlegroups.com. To post to this group, send email to sage-support@googlegroups.com. Visit this group at http://groups.google.com/group/sage-support. For more options, visit https://groups.google.com/groups/opt_out.