Fractional | Swift fractional number type for precise representations

In a list comprehension, a binding like x <- foo expects foo to be a list. But you wrote a single number there! Instead of <- binding, you can use let:

If I'm not mistaken, the problem can be solved using easy math, which implies multiplying both sides with the bottom equation and solving it. For this, the total algorithm would be

• ((Percentage)(Cost A + Cost B) - (Cost B)) / ((Cost per B)(1-Percentage)).

If this case were solved logically without the algorithm, the result would be 200+100x = (600 + 100x)(0.7). 200 + 100x = 420+70x. 100x - 70x = 420 - 200. 30x = 220. x = 22/3. With this the algorithm can be deduced.

The value that we get is the maximum value, but since it's less than we then know x < 22/3, or the value that we would get from the equation.

As @SOS touches on in their comment (not sure why they did not make it an "answer"?), the issue is not the conversion. The issue is that ColdFusion is displaying 69.35 * 100 as equalling 6935, which it isn't. And even ColdFusion doesn't really think it is.

As far as most computing languages are concerned, 69.35 * 100 is 6934.999999999999 (check on JS, Python, Ruby etc if you like), due to issues with the inherent inaccuracy of representing decimal fractional values in a system that stores stuff in binary. I've written about this before: Floating point arithmetic with decimals.

Internally ColdFusion is storing the result as 6934.999999999999:

You can use labelExpr to add arbitrary characters to the axis labels:

A few points:

1. Don't mix up numpy and sympy like this unless you know exactly what you are doing. There is no need to use numpy at all here so use e.g. sym.eye(2) and sym.conjugate(val)

2. Don't use floats unless you have a good reason - use sym.I instead of 1j. Using numpy can potentially introduce floats so don't do that unless you know what you are doing.

3. Although eigenvals returns a dict in this case you only care about the values of the dict so you can just do list(M.eigenvals()).

4. Although you declare all symbols as real you are using sqrt(u) which is real only if u is positive. Unless you intend for sqrt(u) to be potentially imaginary then u should be declared as positive.

With the above changes your code looks like this:

This is not yet directly provided by docplex, we'll keep this in mind for future versions. In the meantime you can use this code here:

https://github.com/PhilippeCouronne/docplex_contribs/blob/master/docplex_contribs/src/numcuts.py

Beware this uses non-documented classes and does not check that the model is actually a MIP. Anyway, it returns a dictionary of cut_name : number of cuts used, e.g.:

Not sure what you tried, but the naive thing works for me:

The value 8.2 can't be exactly represented in binary floating point. The actual value is closer to 8.19999999999999929.

Because of this, you're forced to round:

I suppose that FFT expects ap_fixed<16,15>, where MSB is the sign bit. In your example you have signed samples (because sinusoidal between -1.0 and 1.0), so your casting must be int (signed int). But if you need a two-complement representation of signed int, it's right if you cast with uint. In both cases, cast with 16 bits is enough.