A　number　of　machine　learning　domains,　such　as　information　retrieval,　recommender　systems,　kernel　learning,　neural　network-biological　systems　etc,　deal　with　importance　scores.　Very　often,　there　exist　some　prior　knowledge　that　could　help　improve　the　performance.　In　many　cases,　these　prior　knowledge　manifest　themselves　in　the　rank　ordering　constraints.　These　inequality　constraints　are　usually　very　difficult　to　deal　with　in　optimization.　In　this　paper,　we　provide　a　slack　variable　transformation　methods,　which　effectively　eliminates　the　rank　ordering　inequality　constraints,　and　thus　simplify　the　learning　task　significantly.　We　apply　this　transformation　in　kernel　learning　problem,　and　also　provide　an　efficient　algorithm　to　solved　the　transformed　system.　On　seven　datasets,　our　approach　reduces　the　computational　time　by　orders　of　magnitudes　as　compared　to　the　current　standard　quadratically　constrained　quadratic　programming(QCQP)　optimization　approach.　Copyright　©　2017,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.