44 results about "Linear solver" patented technology

A computer implemented method of determining a latent image from an observed image is disclosed. The method comprises implementing a plurality of image processing operations within a single optimization framework, wherein the single optimization framework comprises solving a linear minimization expression. The method further comprises mapping the linear minimization expression onto at least one non-linear solver. Further, the method comprises using the non-linear solver, iteratively solving the linear minimization expression in order to extract the latent image from the observed image, wherein the linear minimization expression comprises: a data term, and a regularization term, and wherein the regularization term comprises a plurality of non-linear image priors.

A linear solver methodology is applied to reservoir data to solve for large system of equations arising from high-resolution reservoir simulation of giant oil fields with minimal upscaling using either structured grids or unstructured grids. Full geologic complexity and discontinuities at the resolution desired for accurate simulation results may be taken into account. A general unstructured method is provided, so that very complex flow geometry near multi-lateral wells can be modeled.

A system for solving linear equations comprises a first circuit including a first multiplication module for multiplying a first row of a matrix by a first instance of a vector variable to generate a first product, and a first linear solver module for calculating an updated first element of the vector variable using the first product. A second circuit includes a second multiplication module for multiplying a second row of the matrix by a second instance of the vector variable to generate a second product, and a second linear solver module for calculating an updated second element of the vector variable using the second product. An interface module updates the second instance of the vector variable with the first updated element, and updates the first instance of the vector variable with the second updated element.

A computer implemented method of determining a latent image from an observed image is disclosed. The method comprises implementing a plurality of image processing operations within a single optimization framework, wherein the single optimization framework comprises solving a linear minimization expression. The method further comprises mapping the linear minimization expression onto at least one non-linear solver. Further, the method comprises using the non-linear solver, iteratively solving the linear minimization expression in order to extract the latent image from the observed image, wherein the linear minimization expression comprises: a data term, and a regularization term, and wherein the regularization term comprises a plurality of non-linear image priors.

A system for evaluating the convergence to a solution for a matrix equation comprises at least one reconfigurable computing device such as a field programmable gate array (FPGA), an update storage element, a conversion element, a summation unit, and a comparator. The FPGA includes a plurality of configurable logic elements and a plurality of configurable storage elements, which are utilized to form the update storage element, the conversion element, the summation unit, and the comparator. The update storage element is configured to store a plurality of updates. The conversion element determines the absolute value of the updates. The summation unit accumulates the absolute values of the updates to produce a total sum, which is compared to a convergence factor by the comparator. Convergence is signaled when the total sum is less than the convergence factor.

Embodiments of the present invention provide an acceleration method for a linear solver, a storage database, and a graphics processor system, wherein the storage database includes: a first storage unit for storing grid unit identifiers of task blocks; a second storage unit , which is used to store the identification of the inner face and the inner face data corresponding to the inner face identification, the inner face is a plane connecting two grid cells, and is mapped to the inner face identification of the same grid cell identification. The internal surface data is stored in the adjacent storage area of ​​the second storage unit; the index unit is used to construct and store the index range of the grid unit identifier, and the index range includes the grid unit identifier and the Mapping relationship of internal face IDs. The acceleration method for a linear solver, a storage database, and a graphics processing system provided by the embodiments of the present invention can realize the accelerated operation of the linear solver and improve the calculation efficiency.

A system and method for deciding the satisfiability of a non-linear real decision problem is disclosed. Linear and non-linear constraints associated with the problem are separated. The feasibility of the linear constraints is determined using a linear solver. The feasibility of the non-linear constraints is determined using a non-linear solver which employs interval constraint propagation. The interval solutions obtained from the non-linear solver are validated using the linear solver. If the solutions cannot be validated, linear constraints are learned to refine a search space associated with the problem. The learned constraints and the non-linear constraints are iteratively solved using the non-linear solver until either a feasible solution is obtained or no solution is possible.

A method, system, a program storage device and apparatus are disclosed for conducting a reservoir simulation, using a reservoir model of a region of interest, wherein the region of interest has been gridded into cells. Each cell has one or more unknown variable. Each cell has a node. A graph of the nodes is represented by a sparse matrix. The graph is an initially decomposed into a pre-specified number of domains, such that each cell exists in at least one domain. The cells and domains are numbered. Each cell has a key, the key of each cell is the set of domain numbers to which the cell belongs. Each cell has a class, the class of each cell being the number of elements in the cell. The cells are grouped into connectors, each connector being the set of cells that share the same key. Each connector having a connector class, the connector class being the number of elements in the key of the connector. Each connector having only one higher-order neighbor connector is merged with such higher-order neighbor connector. The class of all locally maximum class connectors is reset to the maximum class of held by any connector. The maximum class connector is forced to contain only one cell. The connectors are ordered in increasing order of class. Interpolation operator and restriction operator are constructed from the ordered connectors. The interpolation operator and restriction operator are used to construct a coarse grid. The coarse grid may be used to determine the unknown variables of the cells.

The invention relates to a method to solve parabolic catadioptric camera parameters through one straight line in space. The method is that a calibration object composed of the straight line in the space is used for self-calibration of a parabolic catadioptric camera. Three images of the calibration object are shot by the parabolic catadioptric camera from different directions. A parabolic catadioptric image of a straight line is a quadric curve. Boundary pixel point coordinates and image point coordinates of the three calibration objects images are extracted from the images to respectively fit curvilinear equations. Images of circular ring points on image planes are obtained according to a polarity principle. Camera parameters are solved by utilizing the linear constraint of the images on the circular ring points on an image of an absolute conic. By utilizing the method, the calibration object can be fully and automatically calibrated, and errors caused by measurement in the calibration process are reduced. Because a straight line is a more concise and more global element, calibration precision of the camera in the calibration process is improved.