211results about "Computing models" patented technology

A vehicle is controlled to travel on a road, the road having a geometric design defined by one or a combination of road alignment, profile, and cross section, and different traffic situations are defined by different values ​​of parameters of the road's geometric design, traffic on the road, and traffic rules for traffic flow on the road. The vehicle is controlled by transforming a mixed-integer non-convex constrained optimization problem for the current real-world traffic situation into a mixed-integer convex optimization problem for an approximate representation of the real-world traffic situation by relaxing configuration parameters of the real-world situation and restricting corresponding constraint parameters. Current control commands for controlling one or more actuators of the vehicle are generated by solving the transformed mixed-integer convex optimization problem for the approximate representation of the real-world traffic situation.

To provide a system and program capable of formulating mathematical optimization problems at a lower cost.SOLUTION: A system includes: an interface unit for acquiring a request relating to formulation of a mathematical optimization problem from a terminal device of a user; a first processing unit that inputs the request and first problem-related information, the problem-related information of a similar problem similar to the mathematical optimization problem represented by the request, into a large-scale language model as prompts to acquire second problem-related information, the problem-related information corresponding to the request; and a second processing unit that inputs the second problem-related information into a solver to acquire an optimal solution, where the interface unit outputs one or both of the second problem-related information and the optimal solution to the terminal device.SELECTED DRAWING: Figure 1

To efficiently solve a secondary programming problem having a k-hot constraint (k is a positive integer) to a binary variable.SOLUTION: According to one preferable aspect of the present invention, an optimization method uses an information processing device including a processor, a storage device, an input device, and an output device to solve a secondary programming problem with one or more independent k-hot constraints (k is a positive integer) imposed on a binary variable. In the optimization method, the information processing device adds a correction value to a nonlinear coefficient of the binary variable with the k-not constraints imposed thereon to perform continuous value relaxation of the binary variable, and the information processing device executes solution search while satisfying the k-hot constraints by performing state transition so as to make the sum of a pair of continuous value variables with the k-hot constraints imposed thereon constant.SELECTED DRAWING: Figure 9

The present disclosure proposes a kinetics model generation technique for shortening the total calculation time by increasing the probability that a target parameter value is subjected to parameter value processing while avoiding a situation in which a target parameter value to be collected falls into a local solution. The kinetics model generation technique is a technique for generating a set of target parameter values representing values of unknown parameters that contribute to a kinetics model having a desired property, such generation being on the basis of a non-target parameter value representing the value of an unknown parameter that does not contribute to the kinetics model having the desired property. More specifically, the technique includes: sampling processing for generating a candidate parameter value to be set to an unknown parameter, by generating a random number from a given proposal distribution; rejection determination processing for determining whether or not to accept the candidate parameter value on the basis of the non-target parameter value; and parameter value processing for determining whether or not the kinetics model has the desired property when the candidate parameter value accepted by the rejection determination processing is applied to the kinetics model (see Fig. 3).

To efficiently search a solution in a wide search space.SOLUTION: The processing unit 12 determines a plurality of upper solutions from a plurality of solutions of the combinatorial optimization problem stored in the storage unit 11 based on an evaluation function value of each of the plurality of solutions, determines, for each of the plurality of upper solutions, a selection probability such that an upper solution having an evaluation function value closer to a highest solution among the plurality of upper solutions has a higher probability of being selected, and selects a first solution from the plurality of upper solutions according to the selection probability. A second solution different from the first solution is selected from the plurality of solutions, a third solution is generated by a path recombination method using the selected first and second solutions, and the solution search of the combination optimization problem is executed with the third solution as an initial solution.SELECTED DRAWING: Figure 1

This optimal attribute search device is configured so as to comprise: an optimal value search unit (2) that, when each of a plurality of values for an attribute of a certain target is assigned as a variable, acquires a first capture function that outputs the difference between a treatment effect, which is the effect when a certain treatment is applied to the target, and a non-treatment effect, which is the effect when the treatment is not applied to the target, and retrieves, as a first optimal value, one value from among the plurality of values for the attribute, on the basis of the output of the first capture function; an observation value acquisition unit (3) that acquires, as a first observation value, an observation value of a treatment effect corresponding to the first optimal value retrieved by the optimal value search unit (2); and a function updating unit (4) that updates the first capture function using the first observation value acquired by the observation value acquisition unit (3). The optimal value search unit (2) searches for the first optimal value on the basis of the output of the first acquisition function that has been updated by the function updating unit (4), and outputs the retrieved first optimal value.

To efficiently calculate high-quality solutions. [Solution] The information processing device outputs a solution to a target optimization problem that minimizes or maximizes a target objective function. The information processing device includes a processing unit. The processing unit provides a solver device with an approximation problem that minimizes or maximizes an approximation function based on the target objective function, and performs a solution-finding process while repeatedly updating the intermediate state of the solution. The processing unit obtains from the solver device multiple intermediate solutions based on the intermediate state of the solution calculated during the execution of the solution-finding process for the approximation problem, and a final solution representing the solution calculated after the multiple intermediate solutions in the solution-finding process for the approximation problem. For each of the multiple intermediate solutions and the final solution, the processing unit calculates an evaluation value that represents the evaluation as a solution to the target optimization problem. The processing unit outputs one of the selected solutions selected from the multiple intermediate solutions and the final solution based on the evaluation value as the solution to the target optimization problem.

A storage system for simulating a quantum circuit includes multiple storage devices, wherein the multiple storage devices may be connected to a host device through a redundant array of independent disks (RAID) expansion card and may read data consecutively stored in a first storage device among the multiple storage devices to a main memory with a single access to the first storage device in response to a data read request for an operation in the main memory of the host device.

To provide an information processing device, method and program that can improve calculation accuracy and / or reduce processing time for a combinational optimization problem.SOLUTION: An information processing device comprises a first search unit and a second search unit. The first search unit searches for a first provisional solution of a combinatorial optimization problem where an objective function is partially differentiable using a first searcher which solves the overall problem of a combinatorial optimization problem. The second search unit outputs the final solution of the combinatorial optimization problem on the basis of the comparison between the signs of the first provisional solution and the partial deferential values of the objective function.SELECTED DRAWING: Figure 3

To reduce arithmetic loads by using an Ising model.SOLUTION: An arithmetic method according to the present invention has: a step of acquiring job information indicating a plurality of jobs as an arithmetic target; a step of setting a plurality of divided time zones including a plurality of time slots for each time series; and a step of acquiring, for each of the divided time zones, an arithmetic result obtained by assigning, using an Ising machine, the job indicated by the job information to a time slot included in the divided time zone. In the step of acquiring an arithmetic result, there is acquired an arithmetic result obtained by assigning a job to a time slot included in a divided time zone and a succeeding time slot on condition that a job which is not assigned to a time slot included in the divided time zone is assigned to the succeeding time slot which is a time slot immediately after the divided time zone.SELECTED DRAWING: Figure 8

The present invention calculates an accurate solution in a short time by extracting a partial problem structure from a network optimization problem. A problem solving device according to one embodiment solves a network optimization problem. The problem solving device comprises a processing unit that repeatedly executes first processing, second processing, and third processing. In the first processing, the processing unit classifies each of a plurality of determination variables included in the network optimization problem as a high-impact determination variable or a low-impact determination variable. In the second processing, the processing unit replaces each low-impact determination variable with an estimated value and performs sampling and statistical processing on each high-impact determination variable. In the third processing, the processing unit replaces a determination variable having a strong statistical bias with a value based on the statistical bias on the basis of the results from the sampling and statistical processing.

The use of data held by each organization without disclosing the contents to others. [Solution] A method for use in a system including a plurality of user terminals each having a control unit and a memory unit for storing sensitive data including a plurality of records, a data processing device having a calculation unit in a secure area, a key management device for managing a system key, and a memory device for storing a machine learning model encrypted using the system key, the method comprising: aggregating data held by the plurality of user terminals in the secure area to create combined data; training a machine learning model using the combined data to create a model with higher predictive accuracy than when training is performed using only the user's own data; and, by using the machine learning model in the secure area to perform inference based on the combined data, predictions can be made with higher accuracy than when training is performed using only the user's own data.

This information processing device receives an input of a gain table indicating the extent to which a cooperation task consisting of a combination of actions is performed, such input received for each of the combination patterns of the actions that can be taken by each performer in the cooperation task. Thereafter, the information processing device excludes, from the input gain table, an action that has no possibility of improving the performance of the cooperation task (first exclusion step). Next, the information processing device excludes, from the gain table resulting from the execution of the first exclusion step, patterns in which there is only one combination of the actions that improves the performance of the cooperation task (second exclusion step). Subsequently, if there are patterns with similar combinations of the actions in the gain table resulting from the execution of the second exclusion step, the information processing device excludes one of the patterns among the patterns with similar combinations of the actions (third exclusion step). Consequently, the information processing device outputs the combinations of actions remaining in the gain table after the execution of the third exclusion step.

To calculate, with satisfactory accuracy and with less computing amount, an approximate Pareto solution group in a multi-purpose minimization problem.SOLUTION: A computing apparatus according to the present invention has a prediction unit, a selection unit, an update unit, a repeat control unit, and an output unit. The prediction unit executes prediction processing for predicting an increased amount of a Pareto hyper volume by using a wight pattern corresponding to each of a plurality of weight patterns. The selection unit executes selection processing for selecting a maximum weight pattern with a maximum increased amount. The update unit acquires, from a solver device, a plurality of solutions in a problem of minimizing a synthesized object function as a linear weighted sum of a plurality of object functions and a plurality of weight coefficient represented by the maximum weight patterns to add them to a candidate solution group, thereby carrying out update processing for updating the candidate solution group. The repeat control unit repeatedly controls a series of the prediction processing, the selection processing, and the update processing. The output unit outputs, as an approximate Pareto solution group, a group including a non-dominated solution of the candidate solution group.SELECTED DRAWING: Figure 6

An information processing device 1X mainly comprises an acquisition means 16X and a history display means 161X. The acquisition means 16X acquires constraint relaxation history information indicating the history relating to settings for the relaxation of constraint conditions in an optimization problem. On the basis of the constraint relaxation history information, the history display means 161X displays, on a display device, a graph structure in which each of the settings included in the history serve as the nodes.

A combinatorial optimization problem processing device 100 comprises: a 1 × 2 Mach-Zehnder optical modulator 10 for receiving and modulating a polarization clock pulse train; an optical interference circuit 20 for receiving the polarization clock pulse train modulated by the 1 × 2 Mach-Zehnder optical modulator 10 and generating a predetermined interaction in an Ising model at a period of N pulses of the polarization clock pulse train; a modulating signal generation unit 40 for generating the modulating signal of the 1 × 2 Mach-Zehnder optical modulator 10 by waveform-shaping an electric signal obtained by photoelectrically converting an optical signal output from the optical interference circuit 20 and outputting a monitor signal representing the solution of an optimization problem to the outside; and an initialization control unit 30 for creating a neutral state for interaction between elements to initialize the optical interference circuit 20, by controlling the optical interference circuit 20 to reduce attenuation of the polarization clock pulse train input to the optical interference circuit 20. The combination optimization problem of the N elements is processed in association with the Ising model.

This simulated Ising Hamiltonian generation device comprises: a simulated variable replacement unit which generates a simulated Ising Hamiltonian by replacing each variable of an Ising Hamiltonian with a simulated variable defined by a constant multiple of the sum of two or more distributed variables; and a simulated diagonal entry generation unit which generates simulated diagonal entries that are positive / negative reversal values of the total sum of second order products of the distributed variables included in each of the simulated variables.

To efficiently determine a solution of a combination transition problem.SOLUTION: A method creates a set Dt1 of two combinations transitionable at a time from a combination Di satisfying a constraint of a combination transition problem and a transitionable condition representing the number of changes of combinations allowable in transition in one step, creates a set Dsrc1 of the combination representing an initial state of the combination transition problem from a combination Dsrc representing the initial state and the Dt1 and a combination representing a state transitionable at a time from the initial state, creates a set Ddst1 of a combination satisfying the constraint from a combination Ddst representing a final state of the combination transition problem, the target number of steps Dt1 representing the number of times of transition from the initial state to the final state, and the Dt1, and a combination representing a state transitionable at a time from a state represented by the combination to the final state, creates a conjunctive normal form CNFT obtained by connecting a conjunctive normal form CNFs, a conjunctive normal form CNF0, and a conjunctive normal form CNFS_t with conjunctions from the Dt1, the Dsrc1, the Ddst1, and the target number of steps, and determines the solution by using a SAT solver.SELECTED DRAWING: Figure 3

This information processing device comprises: an acquisition unit that acquires one or a plurality of data points; a generation unit that references a plurality of data points including the data points acquired by the acquisition unit and generates, for each step, an internal polyhedron defined by the plurality of data points and an external polyhedron defined by the plurality of data points; and an identification unit that identifies, from a difference set between a set of weight vectors obtained by referencing the internal polyhedron in the current step and a set of weight vectors obtained by referencing the internal polyhedron in the previous step, a target weight vector by referencing a difference between an optimization result obtained by referencing a data point included in the internal polyhedron in the current step and an optimization result obtained by referencing a data point included in the external polyhedron in the current step.

To provide a method, a device and a program that are capable of evaluating the distribution of basic data points when assessing the reliability of a prediction model which is created from basic data in which alloy properties are associated with explanatory variables related to alloy composition and manufacturing conditions and predicts properties from values of the explanatory variables, while taking into account the scale and importance of each explanatory variable.SOLUTION: The method corrects the distribution of basic data points in a vector space by correction processing. The correction processing sequentially performs, using each explanatory variable as a focus variable: a step of preparing a group of computational data points associating values of explanatory variables with values of a target variable Y predicted by a prediction model; a step of calculating an absolute value of inclination between computational data points adjacent along an a-axis using one of the explanatory variables as a focus variable (a), and then acquiring an average value μ(|ΔY / Δa|) of section inclination amounts; and a step of calculating xa by multiplying the value of the focus variable (a) by the average value for each basic data point, and replacing the value of the focus variable (a) with xa.SELECTED DRAWING: Figure 5

To efficiently obtain a solution for a large-scale problem under complicated constraint conditions.SOLUTION: Generating solution candidates of an optimization problem under a condition excluding a part of constraints to be satisfied, selecting a solution candidate by solving a cost minimization problem under a constraint excluding a constraint between the solution candidates, generating a first tentative solution based on the solution candidate (preliminary optimization processing), and detecting a constraint violation for the first tentative solution; Generating, as a second tentative solution, a solution obtained by correcting the constraint violation by performing optimization calculation for minimizing the number of violation constraints under the constraint determined to be satisfied by the detection using a variable of a unit smaller than the variable used in the preliminary optimization processing (constraint violation correction processing), and using the second tentative solution as an input; A third tentative solution is generated by performing the cost minimization using only the constraints satisfied when the first tentative solution and the second tentative solution are generated as the constraints of the problem (main optimization processing), and information based on the third tentative solution is output.SELECTED DRAWING: Figure 1

To provide an information processor, an information processing method, and a program that can reduce a time required to solve a combinatorial optimization problem with a constraint condition.SOLUTION: An information processor of the present disclosure includes: a first calculation unit that calculates a flip energy change, which is an energy change when each spin flips while satisfying a constraint condition, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with the constraint condition; a second calculation unit that calculates, on the basis of the flip energy change, a transition energy change, which is an energy change when transitioning to a next solution in a combinatorial optimization problem; and a third calculation unit that calculates, on the basis of the transition energy change, inverse temperature used when solving the optimization problem by pseudo-quantum annealing.SELECTED DRAWING: Figure 8

To improve a near optimum solution which can be searched for by setting a pseudo-initial solution in an Ising machine which cannot set any initial solution.SOLUTION: In order to solve the above problem, an energy function minimum value search apparatus according to the present invention has an initial solution acquiring unit, an interaction coefficient correcting unit, and a temporary solution management unit. The initial solution acquiring unit acquires a first temporary solution spin / binary variables necessary for calculation of a first energy function. The interaction coefficient correcting unit corrects an interaction coefficient of the first energy function to change it to a value which contributes to reproduction of the first temporary solution spin / binary variable, and thus generates a second energy function. The temporary solution managing unit searches for a minimum value of the second energy solution by Ising machine, and sets the spin / binary variables giving the minimum value as a second temporary solution spin / binary variable to evaluate a value of the first energy function by using the first temporary solution spin / binary variable and the second temporary solution spin / binary variable.SELECTED DRAWING: Figure 4

To solve the problem in which conventional combinatorial optimization devices are unable to obtain high-quality feasible solutions.SOLUTION: A combinatorial optimization device for solving the above problem comprises a conversion unit, an Ising machine, a repair processing unit, and an improvement processing unit. The conversion unit converts a constrained combinatorial problem into an Ising model consisting of an objective function, a penalty function, and a penalty coefficient. The Ising machine solves the Ising model to generate a tentative solution. If the tentative solution does not satisfy the constraints, the repair processing unit corrects the tentative solution so that the constraints are satisfied, thereby generating a feasible solution. The improvement processing unit corrects the feasible solution so that the constraints are satisfied, or so that the constraints are satisfied and a value of the objective function is improved, thereby generating an improved solution.SELECTED DRAWING: Figure 3

A fluid simulation method is provided that can realize analysis in accordance with the property mechanisms of fluids such as blood. [Solution] A fluid simulation system that executes a fluid simulation method includes a shape evaluation unit (10), a velocity evaluation unit (20), a shear rate evaluation unit (30), a volume calculation unit (40), an optimization unit (50), and a flow path / fluid database (DB).

To realize efficient optimum solution search processing for optimization problem such as combinational optimization problem etc., by reducing a calculation amount.SOLUTION: Solutions generated in a previous stage of an optimum solution search are narrowed down. More specifically, an information processor 10 as an optimum solution search processor that executes an optimum solution search for solving an optimization problem has: an optimum solution search part 415a which generates solution candidates for the optimization problem; and an executable solution candidate search part 415b which executes the narrowing-down processing for the solution candidates corresponding to the solution candidates for the optimization problem and then specifies a solution for the optimization problem from among the narrowed-down solution candidates.SELECTED DRAWING: Figure 4

Compared to cases where weights are not included in the inverse analysis, we obtain an inverse analysis method that can derive parameters with higher accuracy. [Solution] The inverse analysis method by the inverse analysis device 10 is an inverse analysis method that performs inverse analysis using an objective function that obtains the sum of the differences between observed values ​​of predetermined types of physical quantities at multiple locations and calculated values ​​obtained by forward calculation using a mathematical model that approximates the actual field using predetermined parameters corresponding to the observed values, with weights applied to each of the multiple locations, and the parameters and weights are the targets of the inverse analysis.

This disclosure includes an information processing apparatus, an analysis method, and a computer-readable recording medium. It efficiently identifies spaces where mobile entities exhibit characteristic behaviors. The information processing apparatus analyzes the world in which the mobile entity moves. Semantic model information represents attribute information for each constituent element constituting the world. Spatial feature quantities representing spaces in the world are defined by item groups that include attribute information of at least multiple constituent elements existing in and around that space. A specific space is the space in the world where the mobile entity exhibits characteristic behaviors. Based on semantic model information related to a first world, the information processing apparatus extracts multiple constituent elements existing in and around a specific space in the first world and defines specific spatial feature quantities representing that specific space. Based on semantic model information related to a second world, the information processing apparatus extracts similar spaces from the second world that have spatial feature quantities similar to those of the specific spatial feature quantities related to the specific space in the first world.

Conventional state estimation methods based on constrained optimization problems with equality and / or inequality constraints rely on penalty-based heuristics that can generate very large weight values, resulting in mal-tuned gain matrices. Disclosed state estimation embodiments transform the constrained optimization problem into an unconstrained convex optimization problem, where violated equality and / or inequality constraints are represented as parameterized potential functions, each including a center of attraction parameter. This unconstrained convex optimization problem can be iteratively formulated using successively updated values ​​of the center of attraction parameter and solved until no equality and / or inequality constraints are violated, generating a final estimated state. This final estimated state may then be used to control a monitored system, such as a power system.