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-Optimizat... - Workshop product allocation https://hackage.haskell.org/package/sbv-6.0/docs/Data-SBV-Examples-Optimizat... - Allocating virtual-machines in data-centers https://hackage.haskell.org/package/sbv-6.0/docs/Data-SBV-Examples-Optimizat... 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.