22,092 research outputs found
Biproportional Techniques in Input-Output Analysis: Table Updating and Structural Analysis
This paper is dedicated to the contributions of Sir Richard Stone, Michael Bacharach, and Philip Israilevich. It starts out with a brief history of biproportional techniques and related matrix balancing algorithms. We then discuss the RAS algorithm developed by Sir Richard Stone and others. We follow that by evaluating the interpretability of the product of the adjustment parameters, generally known as R and S. We then move on to discuss the various formal formulations of other biproportional approaches and discuss what defines an algorithm as “biproportionalâ€. After mentioning a number of competing optimization algorithms that cannot fall under the rubric of being biproportional, we reflect upon how some of their features have been included into the biproportional setting (the ability to fix the value of interior cells of the matrix being adjusted and of incorporating data reliability into the algorithm). We wind up the paper by pointing out some areas that could use further investigation.Input-Output Economics; RAS; data raking; iterative proportional fitting; estimating missing data
Combinatorics and Geometry of Transportation Polytopes: An Update
A transportation polytope consists of all multidimensional arrays or tables
of non-negative real numbers that satisfy certain sum conditions on subsets of
the entries. They arise naturally in optimization and statistics, and also have
interest for discrete mathematics because permutation matrices, latin squares,
and magic squares appear naturally as lattice points of these polytopes.
In this paper we survey advances on the understanding of the combinatorics
and geometry of these polyhedra and include some recent unpublished results on
the diameter of graphs of these polytopes. In particular, this is a thirty-year
update on the status of a list of open questions last visited in the 1984 book
by Yemelichev, Kovalev and Kravtsov and the 1986 survey paper of Vlach.Comment: 35 pages, 13 figure
Regularized Decomposition of High-Dimensional Multistage Stochastic Programs with Markov Uncertainty
We develop a quadratic regularization approach for the solution of
high-dimensional multistage stochastic optimization problems characterized by a
potentially large number of time periods/stages (e.g. hundreds), a
high-dimensional resource state variable, and a Markov information process. The
resulting algorithms are shown to converge to an optimal policy after a finite
number of iterations under mild technical assumptions. Computational
experiments are conducted using the setting of optimizing energy storage over a
large transmission grid, which motivates both the spatial and temporal
dimensions of our problem. Our numerical results indicate that the proposed
methods exhibit significantly faster convergence than their classical
counterparts, with greater gains observed for higher-dimensional problems
Integrated risk/cost planning models for the US Air Traffic system
A prototype network planning model for the U.S. Air Traffic control system is described. The model encompasses the dual objectives of managing collision risks and transportation costs where traffic flows can be related to these objectives. The underlying structure is a network graph with nonseparable convex costs; the model is solved efficiently by capitalizing on its intrinsic characteristics. Two specialized algorithms for solving the resulting problems are described: (1) truncated Newton, and (2) simplicial decomposition. The feasibility of the approach is demonstrated using data collected from a control center in the Midwest. Computational results with different computer systems are presented, including a vector supercomputer (CRAY-XMP). The risk/cost model has two primary uses: (1) as a strategic planning tool using aggregate flight information, and (2) as an integrated operational system for forecasting congestion and monitoring (controlling) flow throughout the U.S. In the latter case, access to a supercomputer is required due to the model's enormous size
A Decomposition Algorithm for Nested Resource Allocation Problems
We propose an exact polynomial algorithm for a resource allocation problem
with convex costs and constraints on partial sums of resource consumptions, in
the presence of either continuous or integer variables. No assumption of strict
convexity or differentiability is needed. The method solves a hierarchy of
resource allocation subproblems, whose solutions are used to convert
constraints on sums of resources into bounds for separate variables at higher
levels. The resulting time complexity for the integer problem is , and the complexity of obtaining an -approximate
solution for the continuous case is , being
the number of variables, the number of ascending constraints (such that ), a desired precision, and the total resource. This
algorithm attains the best-known complexity when , and improves it when
. Extensive experimental analyses are conducted with four
recent algorithms on various continuous problems issued from theory and
practice. The proposed method achieves a higher performance than previous
algorithms, addressing all problems with up to one million variables in less
than one minute on a modern computer.Comment: Working Paper -- MIT, 23 page
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