Existing　representation　learning　approaches　lie　predominantly　in　designing　models　empirically　without　rigorous　mathematical　guidelines,　neglecting　interpretation　in　terms　of　modeling.　In　this　work,　we　propose　an　optimization-derived　representation　learning　network　that　embraces　both　interpretation　and　extensibility.　To　ensure　interpretability　at　the　design　level,　we　adopt　a　transparent　approach　in　customizing　the　representation　learning　network　from　an　optimization　perspective.　This　involves　modularly　stitching　together　components　to　meet　specific　requirements,　enhancing　flexibility　and　generality.　Then,　we　convert　the　iterative　solution　of　the　convex　optimization　objective　into　the　corresponding　feed-forward　network　layers　by　embedding　learnable　modules.　These　above　optimization-derived　layers　are　seamlessly　integrated　into　a　deep　neural　network　architecture,　allowing　for　training　in　an　end-to-end　fashion.　Furthermore,　extra　view-wise　weights　are　introduced　for　multi-view　learning　to　discriminate　the　contributions　of　representations　from　different　views.　The　proposed　method　outperforms　several　advanced　approaches　on　semi-supervised　classification　tasks,　demonstrating　its　feasibility　and　effectiveness.