Abstract: The problem of minimizing l2-sensitivity subject to l2-scaling constraints for two-dimensional (2-D) separable-denominator state-space digital filters is investigated. The coefficient sensitivity of the filter is analized by using a pure l2-norm. An iterative algorithm for minimizing an l2-sensitivity measure subject to l2-scaling constraints is then explored by introducing a Lagrange...

We describe a quasi-Monte Carlo method for the simulation of discrete time Markov chains with continuous multi-dimensional state space. The method simulates copies of the chain in parallel. At each step the copies are reordered according to their successive coordinates. We prove the convergence of the method when the number of copies increases. We illustrate the method with numerical examples w...

importance of security and sense of peace is a one of the most important factors of urban environment. therefore, the necessity of security in urban spaces has caused the emergence of authentic theories like defensible spaces and crime prevention through environment design (cpted)*. in this paper, various literatures related to defensible space theories have been reviewed and the related keywor...

This paper is concerned with the problem of Reinforcement Learning (RL) for continuous state space and time stocha.stic control problems. We state the Harnilton-Jacobi-Bellman equation satisfied by the value function and use a Finite-Difference method for designing a convergent approximation scheme. Then we propose a RL algorithm based on this scheme and prove its convergence to the optimal sol...

We present new algorithms for the solution of large structured Markov models whose infinitesimal generator can be expressed as a Kronecker expression of sparse matrices. We then compare them with the shuffle-based method commonly used in this context and show how our new algorithms can be advantageous in dealing with very sparse matrices and in supporting both Jacobi-style and Gauss-Seidel-styl...