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.