Tractable problems in estimation and control subject to sparsity and structural constraints

• Online Access: http://hdl.handle.net/2047/D20214120

Recent advances in sensing technology have led to an explosion on the amount of data that can be harvested from systems. However, in order to benefit from the availability of much richer information, engineers must face the "curse of dimensionality". Classical system design techniques are not directly applicable to "data deluged" scenario due to their poor scaling properties, and their inability to handle structural constraints on the information flow. Motivated by these difficulties, in the past few years, many research efforts have been devoted toward developing computationally tractable approaches to handle "Big Data". These ideas include exploiting the so called concentration of measure (inherent underlying sparsity) and self-similarity (high degree of spatio-temporal correlation in the data).