Abstract: Methods for evaluating robustness of solutions to constraint problems include: (a) determining one or more inputs for the constraint problem; (b) determining bias, uncertainty, or bias and uncertainty of the one or more inputs; (c) randomly perturbating at least one of the one or more inputs based on the bias, uncertainty, or bias and uncertainty of the one or more inputs to determine one or more modified inputs; (d) generating a solution to the constraint problem based on the one or more modified inputs; (e) storing the solution; (f) repeating (c) through (e) until multiple solutions are stored; and/or (g) determining the robustness of the stored multiple solutions by comparison of the stored multiple solutions to each other. The one or more modified inputs include the randomly perturbated input or inputs and/or the non-perturbated input or inputs.

Abstract: A deterministic technique is used for assigning locations to object replicas in a data center, while taking the various constraints into account that guarantee high availability and fast recovery. Example constraints include not putting the replicas on the same machine and not on the same rack, in case the rack or the network switch goes down. Also, because power distribution units (PDUs) can fail, the two replicas are not put on the same PDU. A solution is guaranteed to be found, if one exists.

Abstract: A method of scheduling network resources in a first domain by transforming the problem to a second domain, solving the problem and transforming back to the first domain.

Abstract: An approach for solving a global optimization problem is described. Specifically, one embodiment of the disclosure sets forth a method, which includes the steps of receiving a quantitative initial solution, generating a quantitative feasible solution, mapping the quantitative feasible solution to a qualitative feasible solution, determining whether to accept the qualitative feasible solution based on a first predetermined rule, wherein the qualitative feasible solution that is accepted is reverse mapped to the quantitative feasible solution, and transmitting a result of the determining step.

Abstract: A device for optimizing a modeled fluidized bed combustion power plant (10) includes a model (112) of a fluidized bed combustion system and an optimizer (114). The model (112) of the fluidized bed combustion system provides at least one simulated output parameter of the fluidized bed combustion power plant (10) in response to a user selected parameter of the fluidized bed combustion power plant (10). The optimizer (114) provides at least one optimized simulated output parameter of the fluidized bed combustion power plant (10) in response to at least one user selected optimization setting (126) and the at least one simulated output parameter.

Abstract: A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.

Abstract: An information processing apparatus includes an algorithm configuring section that configures an algorithm for performing discrimination on an input signal by using a genetic search technique, and a tradeoff analyzing section that determines Pareto optimal solutions of the algorithm with respect to evaluation indices by performing tradeoff analysis on the basis of the algorithm.

Abstract: A Bayesian treatment of mixture models is based on individual components having Student distributions, which have heavier tails compared to the exponentially decaying tails of Gaussians. The mixture of Student distribution components is characterized by a set of modeling parameters. Tractable approximations of the posterior distributions of individual modeling parameters are optimized and used to generate a data model for a set of input data.

Abstract: For the prognosis of a value of a characteristic of a product which is to be produced with the aid of a neuronal network, the history of the product is taken into account when determining an input variable of an input neuron of the neuronal network.

Abstract: A structural optimization engine generates a response profile based on a vibrational analysis of a three-dimensional (3D) structure. The structural optimization engine determines whether the 3D structure complies with one or more design goals set for the 3D structure based on the response profile. When the 3D structure does not comply with the design goals, the structural optimization engine retrieves dependency data from a structure database. The dependency data indicates various dependencies between response characteristics included in the response profile and specific regions of the 3D model. Based on the dependency data, the structural optimization engine determines structural modifications that can be made to the 3D structure to bring the 3D structure into compliance with the design goals. A multi-axis computer-aided manufacturing unit then makes the structural modifications to the 3D structure.

Abstract: In industrial applications, the invention relates to various algorithms for determining optimal resources or assets allocations under various equality and inequality constraints. In particular, constrained quadratic or conic optimization problems of unique importance for portfolio asset allocation are seamlessly solved in analytic and efficient ways. In addition, by providing exact or analytic optimum expressions, robustness can be readily ascertained.

Abstract: A configuration that minimizes cost subject to the constraints is selected. A Simple Genetic Algorithm (SGA) is modified to incorporate the handling of constraints. The SGA is further modified to incorporate Optimal-Storage-Profiling to produce an increased number of fit individuals in each generation, developing a policy which will consider configurations of all storage-consumption levels and favor those configurations whose storage-consumption indicate they more likely to be strong candidates. An ideal-distribution of configurations, based on their storage-consumption, for each generation is developed. Different elitist policies are also incorporated to achieve greater scalability without sacrificing the quality of the solution.

Abstract: The present disclosure describes a computer-implemented constraint propagation system that supports a variety of different constraint propagation and/or constraint retraction algorithms, including monotonic and/or non-monotonic algorithms. In one embodiment, the system selects particular constraint propagation and/or retraction methods based on the nature of the combinatorial optimization problem (COP) being solved and the attributes of the particular COP application involved. The system may also enable new methods for constraint propagation and/or retraction to be added with relatively little disruptive effect on other components of the system. Embodiments of the system allow the propagation of constraints to be tuned to the semantics of each constraint, the likelihood of significant variable domain reduction, and other problem specific properties. The constraint propagation system is capable of being used as part of a reconfigurable search engine.

Abstract: A computer implemented method for solving linear arithmetic constraints that combines a solver for difference constraints with a general linear arithmetic constraint solver. When used to solve sparse linear arithmetic constraints, the time and space complexity of the process is determined by the difference constraint component.

Abstract: A method of combinatorial multimodal optimization uses a genetic algorithm to find simultaneous global optimal solutions to combinatorial problems. Each individual within the population is associated not only with a fitness value but with a fitness vector, using which the persistence of all of the best individuals into the next generation can be guaranteed. Phenotype as well as genotype analysis is an integral part of the method.

Abstract: A computer-implemented method for solving a constraint satisfaction problem (CSP), which is defined by variables and constraints applicable to the variables, and which has states corresponding to respective sets of values of the variables. The method includes assigning cost functions to the constraints so that the states have respective costs determined by application of the cost functions to the respective sets of values of the variables, the respective costs defining a problem topography of the CSP having global extrema corresponding to solutions of the CSP. The constraints of the CSP are reformulated so as to perform at least one of increasing a density of the solutions in the problem topography and smoothing a gradient of the problem topography. One or more of the solutions of the CSP are found by applying a stochastic CSP solver to the reformulated constraints.

Abstract: The solution of a constrained optimization problem is easily solved by linearly approximating a point of interest to a first hypersurface given as a constraint and finding the extreme value of the objective function by moving a point r along a curved line having second-order osculation with the geodetic line of a hypersurface S(r)=c and passing through the point r.

Abstract: A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

Abstract: A method, system and computer program product for generating an optimized solution for a process in a manufacturing under a set of constraints. A solution is evaluated under the set of constraints and first violation metrics and first states corresponding to one or more solution elements are generated, based on at least one constraint. At least one candidate solution is generated from the solution. Thereafter, the candidate solution is evaluated at one or more change-points and the solution elements positioned subsequent to the change points. Subsequently, the candidate solution is accepted based on the acceptance criterion. The selected candidate solution is then evaluated again a pre-defined number of times to generate the optimized solution.

Abstract: In order to provide for more efficient QBF satisfiability determination, the formula to be checked is transformed into one formula which is equi-satisfiable, and one which is equi-tautological. The conjunction or disjunction of these two formulas, then, is used to determine satisfiability, with the result being that a determination of satisfiability is more easily achieved. A conjunctive normal form transformation of the initial formula yields a group of clauses, only one of which must be unsatisfiable for the formula to be unsatisfiable. A disjunctive normal form transformation of the initial formula yields a group of cubes, only one of which must be satisfiable in order for the formula to be determined to be satisfiable.

Abstract: The invention relates to a method for performing acid fracturing operations of an oilfield. The method includes obtaining a plurality of historical data of acid fracturing treatments of the oilfield, generating a neural network based on the plurality of historical data, identifying a stimulation parameter, in the neural network, associated with optimal performance of the acid fracturing treatments, and establishing a best practice procedure for performing the acid fracturing operations based on the stimulation parameter.

Abstract: A logical expression indicating a logical relation between arbitrary two or three objective functions, of a plurality of mathematically approximated objective functions is computed. A feasible region/sensitivity information display unit displays the feasible region in arbitrary objective space according to it. An inverse image computation unit computes a point or area in design space corresponding to arbitrary design parameters in relation to a point or area specified by a user in the feasible region of the objective space. A feasible region/sensitivity information display unit displays the distribution state of the corresponding point or area as sensitivity information in relation to the specified point or area in the feasible region.

Abstract: The present invention relates to a system and method for intelligent computer-implemented transmittal of data, the system determining and using the best available method of transmission for the data In determining the best available transmission method, the system considers at least one of available transmission methods at a particular site, transmission cost, data type, and data security.

Abstract: A method for solving a constraint satisfaction problem (CSP) comprises identifying a first illegal combination associated with at least one variable, wherein the first illegal combination contradicts at least one constraint; grouping the first illegal combination with a second illegal combination, in response to determining that the first and second illegal combinations contradict the same constraint; identifying at least one failure explanation for each group of illegal combinations, wherein the failure explanation is associated with at least one illegal combination in said group to provide a reason for a contradicted constraint associated with the illegal combination; assigning a value to each variable in the given domain to solve the CSP within the limitations defined by the constraints in a solution path; and generating a result, in response to determining that at least one assigned value to a variable contradicts a constraint.

Abstract: Solver state merging in parallel constraint satisfaction problem (CSP) solvers. Solver state during processing of a computational thread of parallel CSP solvers is represented as a set of support graphs. The support graphs are merged in a pairwise fashion, yielding a new conflict-free graph. The merge process is free of cycles, conflicts are removed, and thread processing is lock-free. The architecture can be applied, generally, in any CSP solver (e.g., a Boolean SAT solver) having certain formal properties. A system is provided that facilitates solver processing, the system comprising a bookkeeping component for representing input solver state of a computational thread as a set of graphs, and a merge component for pairwise merging of at least two input graphs of the set of graphs into a merged graph that represents final state of the computational thread.

Abstract: A novel method is disclosed for efficiently solving minimax problems, and in particular, for efficiently solving minimax problems wherein the corresponding matrix is large. In particular, the novel method solves minimax problems in O(n2 T) operation count, where n denotes the problem size and T is reversely proportional to the required duality gap as one skilled in the art will understand. Further disclosed herein is a method for solving linear programming (LP) problems by converting such problems into minimax problems, and then using the novel minimax solution method disclosed herein.

Abstract: A system and method for optimizing system performance includes applying sampling based optimization to identify optimal configurations of a computing system by selecting a number of configuration samples and evaluating system performance based on the samples. Based on feedback of evaluated samples, a location of an optimal configuration is inferred. Additional samples are generated towards the location of the inferred optimal configuration to further optimize a system configuration.

Abstract: System and method for controlling a process with spatially dependent conditions for producing a product with spatially dependent properties, e.g., a web/sheet-based process for producing a web/sheet-based product. Input data comprising a plurality of input data sets are provided to a neural network (analog or computer-based), each data set comprising values for one or more input parameters, each comprising a respective process condition or product property. The input data preserve spatial relationships of the input data. The neural network generates output data in accordance with the input data, the output data comprising a plurality of output data sets, each comprising values for one or more output parameters, each comprising a predicted process condition or product property. The output data preserve spatial relationships of the output data, which correspond to the spatial relationships of the input data. The output data are useable by a controller or operator to control the process.

Abstract: The invention provides a computer-implemented method for generating a solution to a constraint satisfaction problem (CSP). The method operates to implement various steps that include defining the CSP problem by a set of variable having finite domains, and constraints defined over the variables, solving the CSP by assigning values to said variables that are consistent with the constraints and debugging the CSP solution. The debugging of the CSP solution is carried out by iteratively executing a propagator to reduce the variable domain. Augmenting the constraints is carried out to supply an explanation for particular values assigned to the variables, and constraints defined over the variable utilized in the solution.

Abstract: A mechanism for verifying system behavior includes: (1) A “constraint-based inference engine” and (2) a “constraint-based simulator.” The inference engine accepts logical/temporal/data dependencies describing a system implementation and automatically derives new logical/temporal/data dependencies describing the input/output (“black-box”) behavior of the system or other aspect of the system's behavior. This capability means that a “behavioral model” can be automatically extracted from a “structural model,” thereby supporting “encapsulation” and “information hiding.

Abstract: The present invention relates to optimization analysis achieving a target value and provides a device, method and program ensuring improved analysis efficiency and analysis accuracy. The device includes an analysis unit (CPU 14), an evaluation unit (CPU 14) and a condition modification unit (CPU 14). The analysis unit executes an analysis with analysis conditions imparted thereto. The evaluation unit evaluates the analysis results. The condition modification unit modifies the analysis conditions imparted to the analysis unit, based on the evaluation results. An optimum solution is thus derived by performing one or more analyses through the modification of the given analysis conditions.

Abstract: Provided is a problem solving system. More specifically, the problem solving system provides an interface operable to receive user supplied input data, the input data having at least one user defined characteristic. The system provides an algorithm pool having a plurality of pre-defined algorithms, each algorithm having at least one associated algorithm characteristic. The system also provides a data source pool having at least one data source, each data source having at least one data characteristic. An algorithm selector is in communication with the interface, algorithm pool and data source pool.

Abstract: In one embodiment, the present invention is a method and apparatus for automatic system parameter configuration for performance improvement. One embodiment of the inventive method involves formulating a black box optimization problem, and solving the optimization problem using an enhanced smart hill climbing method. The smart hill climbing method includes both a global and a more precise local search to identify an optimal solution. In one embodiment, one or both of the global and local searches employs a weighted Latin Hypercube Sampling method in combination with importance sampling techniques to yield improved search results.

Abstract: A novel method of automated, real-time website optimization at least includes: a) receiving website optimization data including an optimization goal, and website source code; b) receiving website optimization criteria indicative of the completion of a website optimization experiment; c) executing an optimization algorithm used to select an optimized website version; d) comparing the output of the optimization algorithm with the website optimization goal to determine whether the website version under consideration is optimized; e) providing feedback from the output of the executed optimization algorithm to an input of the optimization algorithm; f) based upon the feedback, determining the next iterative step of the optimization algorithm; g) performing new iterative steps of the optimization algorithm; h) converging to an optimized website state; and i) modifying the website source code to implement the optimum version of the website.

Abstract: A computer-implemented method for determining a boundary for binary classification includes providing a data set, initializing a value for noise in the data set, and determining a hyperplane dividing the data set and a slack variable given a current value for noise. The method further includes updating the value for noise and the slack variable given the hyperplane, and determining the hyperplane to be the boundary for binary classification of the data set upon determining a termination criterion to be met, wherein elements of the data set are classified according to the boundary.

Abstract: A computer-implemented land planning system is designed to generate at least one conceptual fit solution to a user-defined land development problem. The system electronically creates at least one candidate solution to the land development problem. The candidate solution incorporates a number of engineering measurements applicable in development of an undeveloped land site. A fitness function quantitatively evaluates the candidate solution based on its fitness. A heuristic problem-solving strategy manipulates the engineering measurements of the candidate solution to achieve a more quantitatively fit solution to the land development problem. A computer output device outputs to a user documentation illustrating the fit solution to the land development problem.

Abstract: A node participating in a network of nodes that solve a distributed constraint problem. The nodes have mutually constrained sub-problems of the constraint problem. The node receives values or decisions from neighbor nodes. Those values or decisions allow the sub-problems of the neighbor nodes to be solved. Some received values or decisions correspond to a first ordering of the nodes, and some received values or decisions correspond to a second ordering of the nodes. Attempts are made to solve the sub-problem of the node as constrained by the values or decisions of the neighbor nodes. Results of the attempts are communicated to neighbor nodes, where for a given value or decision the node to which the corresponding attempt result is communicated depends on which of the orderings the given value or decision corresponds to. An attempt for one context can be affected by attempts in another context.

Abstract: An information processor, a state judging unit, a diagnostic unit, an information processing method, a state judging method and a diagnosing method aim at precisely recognizing each operation carried out by an object that functions in a number of operation modes. For this purpose, a number of combinations of n parameter values, concerning each of the operation modes, that vary with operation are detected by sensors (1a-1d) during the operation of the object, and a Self-Organizing Map creating means (2) creates Self-Organizing Maps, which corresponds one to each of the operation modes and which serve as individual separation models, regarding detection data based on the detected combination of parameter values as learning data.

Abstract: A method for wafer analysis with artificial neural network and the system thereof are disclosed. The method of the system of the present invention has several steps, including: first of all, providing a test unit for wafer test and generating a plurality of test data; next, transmitting the test data to a processing unit for transferring to output data; then, comparing the output data with predictive value and modifying bias and making the output data close to the predictive value, and repeating the steps mentioned above to train this system; finally, analyzing wafers by the trained system. Using this system to analyze wafers not only saves time, but also reduces manpower and the risk resulting from artificial analysis.

Abstract: In an embodiment of a method of evaluating robustness of a proposed solution to a constraint problem, operational output data for at least first and second modified versions of the proposed solution is generated. The first modified version has at least one control variable of the proposed solution perturbed in a first direction and the second modified version has the at least one control variable of the proposed solution perturbed in a second direction. At least a portion of the generated operation output data is then presented such as on a graphical user interface.

Abstract: An approach that enables reducing the number of computations while modeling data sets using a neural network. To model a first system characterized by a data set, a determination is made as to whether the data elements of the data set follow a similar pattern as data elements of another data set. If such an another data set exists, the weights determined with a system associated with the another data set, are used as initial weights while modeling the first system. Due to such a feature, number of computations in a neural network can be reduced while modeling several data sets.

Abstract: Disclosed are interactive computer-implemented support solutions for facilitating the configuration of complex artifacts having characteristics that vary over a relatively wide range of possibilities. The artifact is defined by a configuration model and its features are described by a set of interrelated parameters. A model translation module converts the configuration model into a constraint satisfaction problem. The constraint satisfaction problem, in turn, is defined by a set of variables, wherein each variable may attain a finite number of different values, and a set of constraints restricting which variable values that are simultaneously possible for the variables in the set of variables. A configuration engine calculates a solution to the constraint satisfaction problem, which is optimal with respect to a value assigned to each variable in the constraint satisfaction problem relative to a predefined optimizing criterion.

Abstract: Technologies are described herein for laying out content. Constraints and rules are defined for one or more content objects. The constraints are utilized to lay out content objects on a canvas. Content is received for one of the content objects. The constraints and rules for the content object are utilized in an attempt to fit the content to the selected content object. If the content cannot be fit to the selected content object, the rules are utilized to modify the constraints for the other content objects and the layout for the canvas is regenerated. If the available content cannot be fit to the content objects on a page without overflowing, one or more additional pages are added to the canvas. Once the additional pages have been added, the content objects are laid out and content is fitted to the content objects across all of the pages.

Abstract: A method, a computer-readable medium, and a system for tuning a cost function to control an operational plant are provided. A plurality of cost function parameters is selected. Predicted future states generated by the neural network model are selectively incorporated into the cost function, and an input weight is applied to a control input signal. A series of known signals are iteratively applied as control input inputs, and the cost output is calculated. A phase is taken of the control and plant outputs in response to each of the known signals and combined, thereby allowing effective combinations of the cost function parameters, the input weight, and the predicted future states to be identified.

Abstract: A method, computer program storage medium and system that implement evolutionary algorithms on heterogeneous computers; in which a central process resident in a central computer delegates subpopulations of individuals of similar fitness from a central pool to separate processes resident on peripheral computers where they evolve for a certain number of generations after which they return to the central pool before the delegation is repeated.

Abstract: A system for inferring geological classes from oilfield well input data is described using a neural network for inferring class probabilities and class sequencing knowledge and optimising the class probabilities according to the sequencing knowledge.

Abstract: A system that automatically generates problem-specific neural networks for each of a plurality problems associated with an analyzed environment when there is sufficient data to do so for each such problem and that, in connection with generating a solution for each of the plurality of problems, selects which of the problem-specific neural networks associated with that problem to use in order to produce the solution. The system also retrains each neural network when appropriate. The system also generates new neural networks for each problem when it is appropriate to do so. In one implementation, a genetic algorithm is used to generate the new neural networks.

Abstract: Apparatus, methods and computer code for estimating a synopsis error are disclosed herein. In some embodiments, one or more queries of a query workload are analyzed without running the analyzed queries. In some embodiments, the synopsis error is first estimated for a first memory allocation vector, and then, using intermediate or final results of the first estimated, estimated for a second memory allocation vector. In some embodiments, teachings related to estimating synopsis error are applied to the memory reconciliation problem.

Abstract: Method and system for designing experiments by selecting at least a first experiment from an experimental space using a data-driven optimizer, receiving experimentally determined data, evaluating the experimentally determined data at a mata layer module, processing the experimentally determined data at the optimizer; the processing at the optimizer being influenced by the evaluation data.

Abstract: Systems and methods for solving a constraint satisfaction problem (CSP) defined by one or more variables, constraints and probabilistic requests (PRs) are provided. In one embodiment, each PR is mapped onto a set of constraints that implement the PR. The CSP solver then finds a series of solutions to the CSP. For each solution, if a constraint associated with a PR is satisfied, then a success counter for that PR is incremented; else a failure counter for that PR is incremented. If the constraint was not considered for the purpose of solving the CSP, then the failure counter for the PR associated with the constraint is incremented.