Global　placement　dominates　the　circuit　placement　process　in　its　solution　quality　and　efficiency.　With　increasing　design　complexity　and　various　design　constraints,　it　is　desirable　to　develop　an　efficient,　high-quality　global　placement　algorithm　for　modern　large-scale　circuit　designs.　In　this　paper,　we　first　analyze　the　properties　of　four　nonlinear　optimization　methods　(the　quadratic　penalty　method,　the　Lagrange　multiplier　method,　and　two　augmented　Lagrangian　methods)　for　global　placement,　and　then　develop　a　generalized　augmented　Lagrangian　method　to　solve　this　problem.　Our　proposed　method　preserves　the　advantages　of　the　quadratic　penalty　method　and　the　augmented　Lagrangian　method,　and　provides　a　smooth　progress　from　the　quadratic　penalty　method　to　the　augmented　Lagrangian　method.　We　prove　that　the　proposed　generalized　augmented　Lagrangian　method　is　globally　convergent　for　the　original　global　placement　problem,　even　with　different　constraints.　Compared　with　the　other　four　popular　optimization　methods,　experimental　results　show　that　our　method　achieves　the　best　quality　and　is　robust　for　handling　different　objectives.　In　particular,　our　generalized　augmented　Lagrangian　formulation　is　theoretically　sound　and　can　solve　generic　large-scale　constrained　nonlinear　optimization　problems,　which　are　widely　used　in　many　fields.　©　2018　Association　for　Computing　Machinery.