Prove that floating point addition is not associative. For illustration purposes, we will require one of the inputs to be a NaN. We have:

>>> prove $ assocPlus (0/0)
Falsifiable. Counter-example:
  s0 = 0.0 :: Float
  s1 = 0.0 :: Float

Indeed:

>>> let i = 0/0 :: Float
>>> i + (0.0 + 0.0)
NaN
>>> ((i + 0.0) + 0.0)
NaN

But keep in mind that NaN does not equal itself in the floating point world! We have:

>>> let nan = 0/0 :: Float in nan == nan
False

Prove that addition is not associative, even if we ignore NaN/Infinity values. To do this, we use the predicate fpIsPoint, which is true of a floating point number (SFloat or SDouble) if it is neither NaN nor Infinity. (That is, it's a representable point in the real-number line.)

We have:

>>> assocPlusRegular
Falsifiable. Counter-example:
  x =  1.3067223e-25 :: Float
  y = -1.7763568e-15 :: Float
  z =  1.7762754e-15 :: Float

Indeed, we have:

>>> let x =  1.3067223e-25 :: Float
>>> let y = -1.7763568e-15 :: Float
>>> let z =  1.7762754e-15 :: Float
>>> x + (y + z)
-8.142091e-20
>>> (x + y) + z
-8.142104e-20

Note the difference in the results!

Demonstrate that a+b = a does not necessarily mean b is 0 in the floating point world, even when we disallow the obvious solution when a and b are Infinity. We have:

>>> nonZeroAddition
Falsifiable. Counter-example:
  a = 7.486071e12 :: Float
  b =    188.8646 :: Float

Indeed, we have:

>>> let a = 7.486071e12 :: Float
>>> let b =    188.8646 :: Float
>>> a + b == a
True
>>> b == 0
False

This example illustrates that a * (1/a) does not necessarily equal 1. Again, we protect against division by 0 and NaN/Infinity.

We have:

>>> multInverse
Falsifiable. Counter-example:
  a = 8.590978e-39 :: Float

Indeed, we have:

>>> let a = 8.590978e-39 :: Float
>>> a * (1/a)
0.99999994

One interesting aspect of floating-point is that the chosen rounding-mode can effect the results of a computation if the exact result cannot be precisely represented. SBV exports the functions fpAdd, fpSub, fpMul, fpDiv, fpFMA and fpSqrt which allows users to specify the IEEE supported RoundingMode for the operation. This example illustrates how SBV can be used to find rounding-modes where, for instance, addition can produce different results. We have:

>>> roundingAdd
Satisfiable. Model:
  rm = RoundTowardPositive :: RoundingMode
  x  =       -2.240786e-38 :: Float
  y  =        -1.10355e-39 :: Float

(Note that depending on your version of Z3, you might get a different result.) Unfortunately Haskell floats do not allow computation with arbitrary rounding modes, but SBV's SFloatingPoint type does. We have:

>>> fpAdd sRoundNearestTiesToEven (-2.240786e-38) (-1.10355e-39) :: SFPSingle
-2.35114116e-38 :: SFloatingPoint 8 24
>>> fpAdd sRoundTowardPositive    (-2.240786e-38) (-1.10355e-39) :: SFPSingle
-2.35114088e-38 :: SFloatingPoint 8 24

We can see why these two results are indeed different: The RoundTowardPositive (which rounds towards positive infinity from zero) produces a larger result. Indeed, if we treat these numbers as Double values, we get:

>>> -2.240786e-38 + (-1.10355e-39) :: Double
-2.351141e-38

we see that the "more precise" result is larger than what the Float value is, justifying the larger value with RoundTowardPositive. A more detailed study is beyond our current scope, so we'll merely note that floating point representation and semantics is indeed a thorny subject, and point to http://ece.uwaterloo.ca/~dwharder/NumericalAnalysis/02Numerics/Double/paper.pdf as an excellent guide.

Arbitrary precision floating-point numbers. SBV can talk about floating point numbers with arbitrary exponent and significand sizes as well. Here is a simple example demonstrating the minimum non-zero positive and maximum floating point values with exponent width 5 and significand width 4, which is actually 3 bits for the significand explicitly stored, includes the hidden bit. We have:

>>> fp54Bounds
Objective "max": Optimal model:
  x   = 61400 :: FloatingPoint 5 4
  max =   503 :: WordN 9
  min =   503 :: WordN 9
Objective "min": Optimal model:
  x   = 0.00000763 :: FloatingPoint 5 4
  max =        257 :: WordN 9
  min =        257 :: WordN 9

The careful reader will notice that the numbers 61400 and 0.00000763 are quite suspicious, but the metric space equivalents are correct. The reason for this is due to the sparcity of floats. The "computed" value of the maximum in this bound is actually 61440, however in FloatingPoint 5 4 representation all numbers between 57344 and 61440 collapse to the same bit-pattern, and the pretty-printer picks a string representation in decimal that falls within range that it considers is the "simplest." (Printing floats precisely is a thorny subject!) Likewise, the minimum value we're looking for is actually 2^-17, but any number between 2^-16 and 2^-17 will map to this number. It turns out that 0.00000763 in decimal is one such value. Moral of the story is that when reading floating-point numbers in decimal notation one should be very careful about the printed representation and the numeric value; while they will match in vsalue (if there are no bugs!), they can print quite differently! (Also keep in mind the rounding modes that impact how the conversion is done.)