Abstract. We present a ( 2 − o(1))-approximation algorithm for the 3 partial latin square extension (PLSE) problem. This improves the current best bound of 1 − 1 due to Gomes, Regis, and Shmoys [5]. We also show e that PLSE is APX-hard. We then consider two new and natural variants of PLSE

We give a condition on the spatial distribution of filled cells in a partial Latin square P that is sufficient to ensure completability, regardless of what symbols are used in the filled cells. For example, if P is of the order mr + t, where m, r are positive integers and t ≥ 0, m is odd

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, is evidence of the above. However, the map-reduce paradigm is too low-level and rigid, and leads to a great deal of custom user code that is hard to maintain, and reuse. We describe a new language called Pig Latin that we have designed to fit in a sweet spot between the declarative style of SQL, and the low

-gradient algorithms, indicating that I~QR is the most reliable algorithm when A is ill-conditioned. Categories and Subject Descriptors: G.1.2 [Numerical Analysis]: ApprorJmation--least squares approximation; G.1.3 [Numerical Analysis]: Numerical Linear Algebra--linear systems (direct and

We propose a new approach to reinforcement learning for control problems which combines value-function approximation with linear architectures and approximate policy iteration. This new approach

serious than in EI Salvador, ” declares one Latin American diplomat. “The oligarchy is that much more reactionary. and the choices are far fewer. “ ‘Zero’: The Guatemalan oligarchs hated Jimmy Carter for cutting off U.S. military aid in 1977 to protest human-rights abuses-and the right-wingers hired

surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees

vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coefficients in the neighborhood, and is thus able to account for the empirically observed correlation between the coefficient amplitudes. Under this model, the Bayesian least squares estimate of each

schoo at a slightly older age, and therefore are eligible to drop out of school after completing fewer years of schooling than individuals born near the end of the year. Our estimates suggest that as many as 25 percent of potential dropouts remain in school because of compulsory schooling laws. We