[Haskell-cafe] [ANN] New release of SBV, now with optimization

[Levent Erkok]

I'm happy to announce a new release of SBV (v6.0); a library for
seamlessly integrating SMT solvers with Haskell:

      http://hackage.haskell.org/package/sbv
<https://hackage.haskell.org/package/sbv>

This version brings optimization features to SBV, where arithmetic goals
can be minimized or maximized. The algorithms employed are particularly
suitable for linear-optimization problems, over bounded/unbounded integers
and reals. Multiple objective functions with optimization priorities are
also supported:


http://hackage.haskell.org/package/sbv-6.0/docs/Data-SBV.html#g:44

Objective values that assume their optimal values at infinity and epsilon
are also supported, allowing the user to express arbitrary combinations of
goals. Furthermore, soft-goals (i.e., those that can be violated with a
user-specified penalty) are included as well. Such goals can be used to
expressed "nice-to-have" constraints, for instance as they appear in
classic scheduling problems.

(Optimization is currently only supported by the z3-backend, and leverages
the features of the z3 SMT solver. Please make sure you get a fresh copy of
it from github <https://github.com/Z3Prover/z3>.)

A few basic examples:

   - Basic linear optimization over reals
   <https://hackage.haskell.org/package/sbv-6.0/docs/Data-SBV-Examples-Optimization-LinearOpt.html>
   - Workshop product allocation
   <https://hackage.haskell.org/package/sbv-6.0/docs/Data-SBV-Examples-Optimization-Production.html>
   - Allocating virtual-machines in data-centers
   <https://hackage.haskell.org/package/sbv-6.0/docs/Data-SBV-Examples-Optimization-VM.html>

This release also comes with a number of other changes/features, including
support for user given tactics, mainly aimed for advanced users. See here
<https://hackage.haskell.org/package/sbv-6.0/changelog> for details.

Bug reports, as always, are most welcome.

-Levent.
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