Lagrangian dual method for solving stochastic linear quadratic optimal control problems with terminal state constraints

A stochastic linear quadratic (LQ) optimal control problem with a pointwise linear equality constraint on the terminal state is considered. A strong Lagrangian duality theorem is proved under a uniform convexity condition on the cost functional and a surjectivity condition on the linear constraint mapping. Based on the Lagrangian duality, two approaches are proposed to solve the constrained stochastic LQ problem. First, a theoretical method is given to construct the closed-form solution by the strong duality. Second, an iterative algorithm, called augmented Lagrangian method (ALM), is proposed. The strong convergence of the iterative sequence generated by ALM is proved. In addition, some sufficient conditions for the surjectivity of the constraint mapping are obtained

A Pilot Study of Top-Down Compulsory eGovernment Systems Success Model: Organizational Users Perspective

When the complexity of the IS evaluation met with the top-down compulsory e-Government systems, new ways of thinking should be reminded. This study takes the perspective of both managers and individual users among the organization, and proposes a success model based on DeLone and McLean’s model and the ERG needs theory. The pilot study reveals organizations significantly stress on the relatedness fulfillment, while the individuals feel efficient work performance