Globalization, Economic Reform, and Structural Price Transmission: SAM Decomposition Techniques with an empirical application to Vietnam

Globalization poses special challenges for economies in transition, particularly those which have been slow to reform systems of administered prices. Such allocation mechanisms now encounter significant friction from external market forces, and it is vital for policymakers to better anticipate the incidence of external price transmission. In this paper, we propose a novel variation of multiplier decomposition methods; make use of an up-to-date social accounting matrix (SAM) for Vietnam; and demonstrate how this kind of information can help identify adverse incentive and wealth effects that might undermine reform and structural adjustments efforts in this important emerging Asian economy.Price transmission, SAMs, multiplier decomposition, Vietnam

Low-Rank Boolean Matrix Approximation by Integer Programming

Low-rank approximations of data matrices are an important dimensionality reduction tool in machine learning and regression analysis. We consider the case of categorical variables, where it can be formulated as the problem of finding low-rank approximations to Boolean matrices. In this paper we give what is to the best of our knowledge the first integer programming formulation that relies on only polynomially many variables and constraints, we discuss how to solve it computationally and report numerical tests on synthetic and real-world data

An efficient null space inexact Newton method for hydraulic simulation of water distribution networks

Null space Newton algorithms are efficient in solving the nonlinear equations arising in hydraulic analysis of water distribution networks. In this article, we propose and evaluate an inexact Newton method that relies on partial updates of the network pipes' frictional headloss computations to solve the linear systems more efficiently and with numerical reliability. The update set parameters are studied to propose appropriate values. Different null space basis generation schemes are analysed to choose methods for sparse and well-conditioned null space bases resulting in a smaller update set. The Newton steps are computed in the null space by solving sparse, symmetric positive definite systems with sparse Cholesky factorizations. By using the constant structure of the null space system matrices, a single symbolic factorization in the Cholesky decomposition is used multiple times, reducing the computational cost of linear solves. The algorithms and analyses are validated using medium to large-scale water network models.Comment: 15 pages, 9 figures, Preprint extension of Abraham and Stoianov, 2015 (https://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0001089), September 2015. Includes extended exposition, additional case studies and new simulations and analysi