5.3 GP in a convex form -- 5.4 Condensation method -- 5.4.1 Successive GP approximation -- 5.4.2 Physical-layer secret communications -- 5.5 Summary and discussion -- 6: Linear Programming and Quadratic Programming -- 6.1 Linear program (LP) -- 6.2 Examples using LP -- 6.2.1 Diet problem -- 6.2.2 Chebyshev center -- 6.2.3 l?-norm approximation -- 6.2.4 l1-norm approximation -- 6.2.5 Maximization/minimization of matrix determinant -- 6.3 Applications in blind source separation using LP/convex geometry -- 6.3.1 nBSS of dependent sources using LP -- 6.3.2 Hyperspectral unmixing using LP -- 6.3.3 Hyperspectral unmixing by simplex geometry -- 6.4 Quadratic program (QP) -- 6.5 Applications of QP and convex geometry in hyperspectral image anal-ysis -- 6.5.1 GENE-CH algorithm for endmember number estimation -- 6.5.2 GENE-AH algorithm for endmember number estimation -- 6.6 Quadratically constrained QP (QCQP) -- 6.7 Applications of QP and QCQP in beamformer design -- 6.7.1 Receive beamforming: Average sidelobe energy minimization -- 6.7.2 Receive beamforming: Worst-case sidelobe energy minimization -- 6.7.3 Transmit beamforming in cognitive radio using QCQP -- 6.8 Summary and discussion -- 7: Second-order Cone Programming -- 7.1 Second-order cone program (SOCP) -- 7.2 Robust linear program -- 7.3 Chance constrained linear program -- 7.4 Robust least-squares approximation -- 7.5 Robust receive beamforming via SOCP -- 7.5.1 Minimum-variance beamformer -- 7.5.2 Robust beamforming via SOCP -- 7.6 Transmit downlink beamforming via SOCP -- 7.6.1 Power minimization beamforming -- 7.6.2 Max-Min-Fair beamforming -- 7.6.3 Multicell beamforming -- 7.6.4 Femtocell beamforming -- 7.7 Summary and discussion -- 8: Semidefinite Programming -- 8.1 Semidefinite program (SDP) -- 8.2 QCQP and SOCP as SDP via Schur complement -- 8.3 S-Procedure