6,196 research outputs found
Resuelve: A Gauss program for solving computable general equilibrium and disequilibrium models
This paper describes a GAUSS program that can be used, among other things, to find equilibria for computable general equilibrium models, and fix price equilibria for general non Walrasian models. Simple applications for those two cases, as well as for linear and quadratic programming, are also provided.computable general equilibrium, disequilibrium, fix price equilibria, desequilibrio, precios rígidos
A user's manual for the Automatic Synthesis Program /program C/
Digital computer program for numerical solution of problems in system theory involving linear mathematic
Image 100 procedures manual development: Applications system library definition and Image 100 software definition
An outline for an Image 100 procedures manual for Earth Resources Program image analysis was developed which sets forth guidelines that provide a basis for the preparation and updating of an Image 100 Procedures Manual. The scope of the outline was limited to definition of general features of a procedures manual together with special features of an interactive system. Computer programs were identified which should be implemented as part of an applications oriented library for the system
Nonlinear Programming Techniques Applied to Stochastic Programs with Recourse
Stochastic convex programs with recourse can equivalently be formulated as nonlinear convex programming problems. These possess some rather marked characteristics. Firstly, the proportion of linear to nonlinear variables is often large and leads to a natural partition of the constraints and objective. Secondly, the objective function corresponding to the nonlinear variables can vary over a wide range of possibilities; under appropriate assumptions about the underlying stochastic program it could be, for example, a smooth function, a separable polyhedral function or a nonsmooth function whose values and gradients are very expensive to compute. Thirdly, the problems are often large-scale and linearly constrained with special structure in the constraints.
This paper is a comprehensive study of solution methods for stochastic programs with recourse viewed from the above standpoint. We describe a number of promising algorithmic approaches that are derived from methods of nonlinear programming. The discussion is a fairly general one, but the solution of two classes of stochastic programs with recourse are of particular interest. The first corresponds to stochastic linear programs with simple recourse and stochastic right-hand-side elements with given discrete probability distribution. The second corresponds to stochastic linear programs with complete recourse and stochastic right-hand-side vectors defined by a limited number of scenarios, each with given probability. A repeated theme is the use of the MINOS code of Murtagh and Saunders as a basis for developing suitable implementations
A Comparison of the Classification Accuracy of Linear and Quadratic Statistical Discriminant Models versus Linear and Quadratic Programming Discriminant Models
The purpose of this research was to compare the classification accuracy of two mathematical programming models versus traditional statistical discriminant analysis. Monte Carlo techniques were used to compute population 1, population 2, and average misclassification rates for the linear discriminant function (LDF), the quadratic discriminant function (QDF), a linear programming discriminant model (LPDM), and a quadratic programming discriminant model (QPDM) for specific values of several parameters which affect discriminant analysis. This study was restricted to the two group, two variable discriminant problem
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
Many graphics and vision problems can be expressed as non-linear least
squares optimizations of objective functions over visual data, such as images
and meshes. The mathematical descriptions of these functions are extremely
concise, but their implementation in real code is tedious, especially when
optimized for real-time performance on modern GPUs in interactive applications.
In this work, we propose a new language, Opt (available under
http://optlang.org), for writing these objective functions over image- or
graph-structured unknowns concisely and at a high level. Our compiler
automatically transforms these specifications into state-of-the-art GPU solvers
based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate
different variations of the solver, so users can easily explore tradeoffs in
numerical precision, matrix-free methods, and solver approaches. In our
results, we implement a variety of real-world graphics and vision applications.
Their energy functions are expressible in tens of lines of code, and produce
highly-optimized GPU solver implementations. These solver have performance
competitive with the best published hand-tuned, application-specific GPU
solvers, and orders of magnitude beyond a general-purpose auto-generated
solver
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