ABSTRACT
Semidefinite programming, SDP, refers to optimization problems where variables X in the objective function and/or constraints can be symmetric matrices restricted to the cone of positive semidefinite matrices. (We restrict ourselves to real symmetric matrices, S n, since the vast majority of applications are for the real case. The complex case requires using the complex inner-product space.) An example of a simple linear SDP is p * = min tr CX ( SDP ) subject to TX = b X ≽ 0 , https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429138492/cc3be78c-f644-49c2-b40d-db06d778c1a5/content/eq7342.tif"/>
