Deep　neural　networks(DNNs)　have　achieved　great　success　in　many　real-world　applications,　but　they　also　had　some　drawbacks　such　as　considerable　storage　requirement,　large　computational　power　consumption　and　delay　for　training　and　inference,　making　it　impracticable　to　deploy　state-of-the-art　models　into　embedded　systems　and　portable　devices.　Thus,　the　demand　of　compressing　DNNs　has　been　taken　into　consideration.　In　this　paper,　we　focus　on　quantized　neural　networks,　which　is　one　scheme　of　compressing　DNNs.　At　first　we　introduce　some　baseline　works　in　quantized　neural　networks　and　then　give　a　review　on　optimization　ways　used　in　quantizing　neural　networks.　In　our　perspective,　these　methods　fall　into　two　categories:　minimizing　quantization　error　and　minimizing　loss　function.　Specialized　introduction　for　each　category　follows　after　baseline　works.　We　also　make　some　comments　to　each　category　and　some　methods.　Finally,　we　discuss　on　some　possible　directions　of　this　area　and　make　a　conclusion.　©　2019　IEEE.