284results 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

Provided is an information processing means that makes it possible to improve the performance of an annealing method and efficiently obtain the solution to a constrained quadratic programming problem.　The information processing device includes: a problem generation unit into which a conversion function and a constrained quadratic programming problem having an objective function and a linear constraint are input, and that uses the constrained quadratic programming problem as a basis to generate a relaxation problem and an unconstrained quadratic programming problem in which the linear constraint is replaced with a penalty term; a relaxation unit that calculates a dual variable by solving the relaxation problem; a penalty management unit that calculates a penalty coefficient corresponding to the linear constraint of the constrained quadratic programming problem on the basis of the conversion function and the dual variable; and an annealing calculation unit that determines an executable solution satisfying the linear constraint by using an annealing method to solve the unconstrained quadratic programming problem in which the penalty coefficient is applied to the penalty term.

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.

The present invention belongs to the technical field of automatic driving, and particularly relates to a method, device, recording medium and electronic device for collision control by man-machine collaborative driving. The collision control method by man-machine collaborative driving includes the steps of: establishing a man-machine path tracking control game model corresponding to man-machine interaction behavior based on a driver's deterministic steering torque and a driver's random steering torque (S101); solving the man-machine path tracking control game model to obtain man-machine torque collision information (S102); and determining a shared control strategy based on the man-machine torque collision information, thereby controlling the vehicle based on the shared control strategy (S103). The collision control method by man-machine collaborative driving provided in the present invention can improve the accuracy of vehicle control by describing the mapping relationship between the difference in man-machine decision-making and the man-machine steering torque interaction.

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.

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 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.

The present invention provides a method to quickly and accurately estimate an estimation target within an analysis condition. This method includes: measuring data at least one measurement point; performing a first analysis using an analysis condition with the estimation target to obtain a result; performing a second analysis by applying perturbation to the estimation target value used in the first analysis to obtain a result; and calculating a sensitivity matrix based on the first and second analysis results, calculating the error between the first analysis result and the measurement data or data derived from it, and determining the amount to adjust the estimation target value from the sensitivity matrix and error.

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.

Each constraint term in an expression representing energy in a combinatorial optimization problem is input the input means 71. The automatic establishment rate calculation means 73 calculates an automatic establishment rate for each constraint term, wherein the automatic establishment rate is a probability that a constraint represented by a constraint term is satisfied when all other constraints associated with individual spins associated with the constraint term are satisfied. The energy increase amount determination means 74 determines amount of energy increase at constraint breakdown for each constraint term, wherein the amount of energy increase at constraint breakdown is amount of energy increase when a constraint represented by a constraint term is no longer satisfied. The spin number derivation means 75 derives the number of spins associated with a constraint represented by a constraint term, for each constraint term.

An information processing device for assisting in the creation of an Ising model for causing an annealing-type optimization machine to solve the problem of searching for an optimal solution for a composite material having a compounding constraint condition, the information processing device characterized by comprising: an input reception unit that receives an input of the compounding constraint condition and an objective function in which the characteristics of the composite material have been formulated according to the compounding; and a data creation unit that creates an Ising model that causes the annealing-type optimization machine to solve the optimal solution for a compounding that satisfies a constraint conditional expression in which the compounding constraint condition is formulated and in which the characteristics are optimized, wherein with regard to substances to be mixed into the composite material, an amount ri of a substance i is represented by bits, and whether the substance i is included in the composite material is represented by a bit, the constraint conditional expression includes a condition satisfying expressions (1) and (2), and the constraint conditional expression is formulated so as not to be calculated as an optimal solution of the compounding if at least one of expressions (1) and (2) is not satisfied.

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.

A machine learning device according to one embodiment of the present invention is provided with a storage unit and a processor. The storage unit stores answer data, including parameters presented in relation to measurement of a psychometric function and users' evaluation values for the parameters. The processor includes a logistic regression unit, a parameter selection unit, and a parameter presentation unit. The logistic regression unit repeatedly inputs parameters and evaluation values of the answer data to a logistic regression model and thereby trains the logistic regression model through machine learning to generate a trained model corresponding to the psychometric function. The parameter selection unit selects parameters to be presented, through active learning based on evaluation values of the answer data and the trained model. The parameter presentation unit presents the selected parameters to a subject.

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 a combinatorial optimization problem solution device that can find a candidate of the solution to combinatorial optimization problems faster.SOLUTION: A combinatorial optimization problem solution device 20 comprises a solution unit 21. By solving SAT expressed in a form of CNF in which constraints other than a no-transformation constraint, which is a constraint that satisfies a prescribed condition among one or more constraints imposed on a combinatorial optimization problem, are transformed, so that the no-transformation constraint is satisfied, the solution unit obtains a combination of values of multiple variables of the combinatorial optimization problem, where the combination is a candidate of the solution of the combinatorial optimization problem and satisfies the one or more constraints.SELECTED DRAWING: Figure 14