# Constraint optimization problem solving

## Subclass of:

## 706 - Data processing: artificial intelligence

## 706015000 - NEURAL NETWORK

## 706016000 - Learning task

### Patent class list (only not empty are listed)

#### Deeper subclasses:

Class / Patent application number | Description | Number of patent applications / Date published |
---|---|---|

706019000 | Constraint optimization problem solving | 28 |

20080222066 | CALIBRATION AND PROFILE BASED SYNOPSES ERROR ESTIMIATION AND SYNOPSES RECONCILIATION - 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. | 09-11-2008 |

20120226644 | Accurate and Fast Neural network Training for Library-Based Critical Dimension (CD) Metrology - Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described. | 09-06-2012 |

20090030859 | Method and apparatus for real-time website optimization - 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. | 01-29-2009 |

20080301073 | METHOD FOR WAFER ANALYSIS WITH ARTIFICIAL NEURAL NETWORK AND SYSTEM THEREOF - 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. | 12-04-2008 |

20100268677 | APPROACH FOR SOLVING A CONSTRAINED OPTIMIZATION PROBLEM - Approaches for performing simulation optimization for solving a constrained optimization problem are generally disclosed. One embodiment according to the present disclosure is to formulate a Lagrange equation having incorporated a Lagrange parameter, a first long run average function for an objective associated with the constrained optimization problem, and a second long run average function for a constraint associated with the constrained optimization problem. Then, to identify a parameter value that may lead to an extreme value for the Lagrange equation, in an iterative manner, averages of the first long run average function and the second long run average function are calculated, a gradient of the Lagrange equation is estimated, and the Lagrange parameter is updated. | 10-21-2010 |

20090112780 | DISCOVERING OPTIMAL SYSTEM CONFIGURATIONS USING DECENTRALIZED PROBABILITY BASED ACTIVE SAMPLING - 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. | 04-30-2009 |

20090119237 | METHOD FOR SOLVING MINIMAX AND LINEAR PROGRAMMING PROBLEMS - 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(n | 05-07-2009 |

20090055335 | PROBLEM SOLVING SYSTEM AND METHOD - 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. The algorithm selector operable to; receive the input data and review the at least one user defined characteristic; select at least one algorithm from the algorithm pool based on the at least one user defined characteristic and selected algorithm's algorithm characteristic; select at least one data source from the data source pool based on the at least one user defined characteristic indicating a need for additional data and the selected data source's data characteristic; and bundle the input data, the selected algorithm and data source as a job operation for execution by a job operation subsystem to obtain a result, and return the result to a designated party. An associated method of solving a problem with such a system is also provided. | 02-26-2009 |

20110125685 | Method for identifying Hammerstein models - The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part. | 05-26-2011 |

20110125684 | Method for identifying multi-input multi-output hammerstein models - The method for the identifying of multiple input, multiple output (MIMO) Hammerstein models that includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). | 05-26-2011 |

20120330870 | METHOD AND APPARATUS FOR A LOCAL COMPETITIVE LEARNING RULE THAT LEADS TO SPARSE CONNECTIVITY - Certain aspects of the present disclosure support a local competitive learning rule applied in a computational network that leads to sparse connectivity among processing units of the network. The present disclosure provides a modification to the Oja learning rule, modifying the constraint on the sum of squared weights in the Oja rule. This constraining can be intrinsic and local as opposed to the commonly used multiplicative and subtractive normalizations, which are explicit and require the knowledge of all input weights of a processing unit to update each one of them individually. The presented rule provides convergence to a weight vector that is sparser (i.e., has more zero elements) than the weight vector learned by the original Oja rule. Such sparse connectivity can lead to a higher selectivity of processing units to specific features, and it may require less memory to store the network configuration and less energy to operate it. | 12-27-2012 |

20100223217 | DYNAMIC COMPUTATION OF OPTIMAL PLACEMENT FOR SERVICES IN A DISTRIBUTED COMPUTING SYSTEM - Components of a distributed computing system are monitored, the components including hardware components and software components that operate on the hardware components. At least one of the software components is a service that includes a service level agreement. Performance characteristics of the components are determined based on the monitoring. The performance characteristics of the service are compared to the service level agreement to determine whether the service level agreement has been violated. At least one of the service or an additional service collocated with the service is migrated based on the performance characteristics of the components if the service level agreement has been violated. | 09-02-2010 |

20100274747 | Constraint Processing with Zero Value Handling - Systems and methods for managing floating point variables are described in the present disclosure. According to one example, an embodiment of a method is described. The method comprises providing a floating point variable having a domain that includes a flag representing whether a specific value is included in or excluded from the domain of the floating point variable. The method also includes analyzing a constraint on the floating point variable to determine if the constraint excludes the specific value from the domain of the floating point variable. A value of the flag is manipulated to indicate that the specific value is excluded from the domain of the floating point variable if it is determined that the constraint excludes the specific value. In some cases, the specific value can be the value zero, for example. | 10-28-2010 |

20100057652 | APPROACH FOR SOLVING GLOBAL OPTIMIZATION PROBLEM - 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. | 03-04-2010 |

20110082823 | Thought Dynamo Decision Mapping Model - The Thought Dynamo Decision (TDD) Mapping Model includes three overlapping 3-D grids that represent decisions from the perspectives of intellect, emotions and imagined outcomes. The continuums of the logic of intellect are power-powerless, good-evil and accuracy-intuitive. The logic of emotion has three continuums: trust-fear, honor-shame, freedom-bonding. And the imagined outcomes continuums are thriving-surviving, desired identity-undesired identity, and meaningful-meaningless. The intersection of each grid forms a central tendency: creative harmony of jealous space. A five step process is used for mapping thoughts and decisions: plot, associate, adjust, solidify and employ. The dynamics of thought and decision are mapped by assigning weights to inputs, strength to associations, dynamically accounting for adjustments and solidifications over time and projecting rules of thumb in decision making onto (3) 3-D axes. This model accounts for decision differences across cultures as illustrated by an examination of the Japanese construct of “amae”. | 04-07-2011 |

20130054500 | ROBUST CONTROLLER FOR NONLINEAR MIMO SYSTEMS - The robust controller for nonlinear MIMO systems uses a radial basis function (RBF) neural network to generate optimal control signals abiding by constraints, if any, on the control signal or on the system output. The weights of the neural network are trained in the negative direction of the gradient of output squared error. Nonlinearities in the system, as well as variations in system parameters, are handled by the robust controller. Simulation results are included in the end to assess the performance of the proposed controller. | 02-28-2013 |

20100094787 | CLUSTERING AND CLASSIFICATION EMPLOYING SOFTMAX FUNCTION INCLUDING EFFICIENT BOUNDS - A function optimization method includes the operations of: constructing an upper bound using a double majorization bounding process to a sum-of-exponentials function including a summation of exponentials of the form | 04-15-2010 |

20090006291 | Computer-implemented land planning system and method designed to generate at least one conceptual fit solution to a user-defined land development problem - 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. | 01-01-2009 |

20080222065 | LEARNING AND ANALYSIS SYSTEMS AND METHODS - 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. | 09-11-2008 |

20120303562 | ARTIFICIAL NEURAL NETWORK APPLICATION FOR MAGNETIC CORE WIDTH PREDICTION AND MODELING FOR MAGNETIC DISK DRIVE MANUFACTURE - A method for predicting and optimizing magnetic core width of a write head using neural networks to analyze manufacturing parameters, and determining new manufacturing parameters that will provide more optimal magnetic core width results. The manufacturing parameters can include: write pole flare point; wrap around shield dimension; and side gap dimension. | 11-29-2012 |

20140258195 | Neuromorphic Spatiotemporal Where-What Machines - In various embodiments, electronic apparatus, systems, and methods include a unified compact spatiotemporal method that provides a process for machines to deal with space and time and to deal with sensors and effectors. Additional apparatus, systems, and methods are disclosed. | 09-11-2014 |

20140289178 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD AND PROGRAM - A method for acquiring an input value such that an output value differs before and after refactoring is not known and an information processing apparatus searching for such an input value that inequivalent output values can be brought about for the equivalent input values among multiple target programs. The apparatus including: an acquisition section acquiring, for each of the multiple target programs, an input/output constraint showing a condition to be satisfied by an input value and an output value; a constraint condition generating section generating a constraint condition which becomes true when the multiple input/output constraints for the multiple target programs are satisfied, input values for the multiple target programs are equivalent, and output values for the multiple target programs are not equivalent; and a constraint releasing section giving the constraint condition to a constraint solver to obtain an input value satisfying the constraint condition. | 09-25-2014 |

20100121795 | DYNAMIC CONSTRAINT SATISFACTION PROBLEM SOLVER - A system for solving a dynamic constraint satisfaction problem comprises a constraint network of variables and constraints. The system creates a first sub-problem model that includes a first model type, one or more first variables and zero or more first constraints. The system propagates the first constraints through the constraint network and determines if a first conflict is detected from propagating the first constraints. If the first conflict is detected, the system restores the constraint network variables to a first previous state before the first constraints were propagated. The system creates a first sub-problem set that includes a second model type and one or more sub-problem models. The system connects the first sub-problem model to the first sub-problem set via a second constraint and propagates the second constraint through the constraint network. | 05-13-2010 |

20100010947 | INFORMATION PROCESSSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - 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. | 01-14-2010 |

20090299928 | Method for optimizing inequality and equality constrained resources allocation problems in industrial applications - 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. | 12-03-2009 |

20100299293 | Computer-implemented land planning system and method designed to generate at least one conceptual fit solution to a user-defined land development problem - 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. | 11-25-2010 |

20140201116 | OPTIMALLY CONFIGURING AN INFORMATION LANDSCAPE - According to an embodiment of the present invention, a system optimizes an information processing environment, and comprises at least one processor. The system collects information pertaining to operational behavior of the information processing environment and including a plurality of parameters. A neural network structure is established to associate the parameters to a desired operational performance characteristic for the information processing environment. The neural network structure is trained with the collected information from the information processing environment to produce a model for the information processing environment. The model is optimized to determine values for the parameters and the information processing environment is adjusted based on the determined parameter values to attain the desired operational performance of the information processing environment. Embodiments of the present invention further include a method and computer program product for optimizing an information processing environment in substantially the same manner described above. | 07-17-2014 |

20130304682 | Optimally Configuring an Information Landscape - According to an embodiment of the present invention, a system optimizes an information processing environment, and comprises at least one processor. The system collects information pertaining to operational behavior of the information processing environment and including a plurality of parameters. A neural network structure is established to associate the parameters to a desired operational performance characteristic for the information processing environment. The neural network structure is trained with the collected information from the information processing environment to produce a model for the information processing environment. The model is optimized to determine values for the parameters and the information processing environment is adjusted based on the determined parameter values to attain the desired operational performance of the information processing environment. Embodiments of the present invention further include a method and computer program product for optimizing an information processing environment in substantially the same manner described above. | 11-14-2013 |