new and improved MILP formulation to optimize observability, redundancy and precision for sensor network problems

• Published in: AIChE journal Vol. 54; no. 5; pp. 1282 - 1291
• Main Authors: Kelly, Jeffrey D, Zyngier, Danielle
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.05.2008; Wiley Subscription Services; American Institute of Chemical Engineers
• Subjects: Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization; Applied sciences; Chemical engineering; error-in-variables; estimability; Exact sciences and technology; implicit models; Instrumentation; Linear programming; Normal distribution; reconciliation; regression; Schur complement; sensor network location; Sensors; variability; error-in-variables; Branch and bound method; Instrumentation; Linear programming; Mixed integer programming; variability; Modeling; reconciliation; sensor network location; Covariance; regression; implicit models; Software; Schur complement; Observability; estimability; Mathematical programming

New mathematical formulations of observability, redundancy, and an improved formulation for precision are provided which can be explicitly and analytically solved using mixed integer linear programming (MILP). By using the Schur complement found at the heart of both Gaussian elimination and Cholesky...