433results about "Computing models" patented technology

The optimization device 90 includes a determining means 91 and an optimizing means 92. The determining means 91 determines a type of combinatorial optimization problem from a QUBO matrix obtained by QUBO modeling of a combinatorial optimization problem that includes a two-way one-hot condition as a constraint condition. The optimizing means 92 performs optimization process according to the determined type of combinatorial optimization problem.

To provide a simulation device that can efficiently arrive at a good solution by appropriately determining a range of a temperature parameter for searching a ground state of an interaction model using simulated annealing (SA).SOLUTION: A simulation device, which uses SA (simulated annealing) to find a solution to a combinatorial optimization problem, acquires a local optimal solution to an object function thereof by a local search method that changes a temperature parameter, calculates a statistic from the local optimum solution and a probability distribution function that a variable included in the object function follows, calculates, on the basis of the calculated statistic (standard deviation of the local optimum state), a maximum value (temperature parameter with a large standard deviation) and a minimum value (temperature parameter with a small standard deviation) that are a search range of the temperature parameter, and performs, in the search range of maximum and minimum values of the temperature parameter, a plurality of times of simulated annealing to find a good solution.SELECTED DRAWING: Figure 10

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 a program capable of formulating a mathematical optimization problem at a lower cost. [Solution] A system comprising an interface unit that receives a request regarding the formulation of a mathematical optimization problem from a user's terminal device, a first processing unit that receives the request and first problem-related information, which is problem-related information of a similar problem to the mathematical optimization problem represented by the request, as prompts into a large-scale language model to receive second problem-related information, which is problem-related information corresponding to the request, and a second processing unit that receives the second problem-related information by inputting it into a solver, wherein the interface unit outputs one or both of the second problem-related information and the optimal solution to the terminal device.

The solution search device (1) includes a setting unit (12) that sets a first aspiration level, which is a target value for multiple objective functions in a multi-objective optimization problem, a first optimal solution calculation unit (13) that calculates a first optimal solution using the first aspiration level set by the setting unit and a frontier, which is a solution set for optimizing the multiple objective functions, and a first optimal solution adjustment unit (15) that, when an instruction to not adopt the first optimal solution calculated by the first optimal solution calculation unit is received from a user, receives an input of an adjustment amount for adjusting the first optimal solution from the user, and adjusts the first optimal solution within the range of the frontier based on the received adjustment amount.

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.

This route planning device is for planning routes of a plurality of agents, and is characterized by comprising: an information acquisition unit for acquiring a start state and a goal state of each of the plurality of agents; and a homotopy class calculation unit for calculating a homotopy class of a route from the start state of the plurality of agents to the state after movement.

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 reduce a memory use amount required upon calculation of an assignment problem.SOLUTION: A data processing apparatus according to the present invention has: a storage unit 11 for storing either of a flow matrix and a distance matrix of an assignment problem having an evaluation function represented by using matrix operation of the flow matrix and the distance matrix; and a processing unit 12 for selecting a first element and a second element among a plurality of elements assigned to a plurality of allocation destinations, reading out either of first matrix elements as the flow matrix and the distance matrix which respectively correspond to the first element and the second element from the storage unit 11, generating, based on the first element and the second element, a second matrix element as the other pattern matrix of the flow matrix and the distance matrix, calculating, by using the first matrix element and the second matrix element, a change amount of a value of the evaluation function obtained when the allocation change of changing the allocation destination of the first element or the second element is caused, determining, based on the change amount, whether or not the allocation change is allowed, and updating allocation states if it is determined that the allocation change is allowed.SELECTED DRAWING: Figure 4

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.

In order to prevent a reward that is obtained from becoming excessively low in a state in which selectable options change every round, an information processing device (1) includes an acquisition unit (11) that acquires information indicating a set of selectable options in a certain round, a selection unit (12) that selects a plurality of options from the set, and an accumulation unit (13) that accumulates data including the plurality of options selected by the selection unit (12) and an observation value of a reward obtained from the plurality of options selected by the selection unit (12). The selection unit (12) selects the plurality of options such that a predicted value of the reward obtained by referencing data that the accumulation unit (13) has accumulated satisfies a constraint that is determined in accordance with baseline options selected from the set.

This information processing device 1X implements an artificial intelligence (AI) for solving a combinatorial optimization problem and comprises primarily an executable solution determination means 53X and a correction means 55X. The executable solution determination means 53X determines an executable solution to the optimization problem. The correction means 55X corrects the optimization problem on the basis of: a first condition which is based on an objective function value corresponding to the executable solution; and a second condition which is made redundant in the optimization problem by the correction of the optimization problem by the first condition.

To provide a technology for an optimization device that can find optimized combinations even for combinatorial optimization problems imposed with constraints that are difficult to formulate.SOLUTION: An optimization device comprises: a data acquisition unit for acquiring data on an optimized combination using basic information related to basic component elements of the combination; a correction unit for correcting the basic information using the data on the optimized combination and constraint information related to constraint conditions; and a learning unit for acquiring new basic information that has learned the constraint conditions using the corrected basic information. The data acquisition unit acquires new data on the optimized combination using the new basic information acquired by the learning unit, the correction unit corrects the new basic information using the new data on the optimized combination acquired by the data acquisition unit and the constraint information, and the learning unit acquires further new basic information that has further learned the constraint conditions using the new basic information corrected by the correction unit.SELECTED DRAWING: Figure 1

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.

To detect a specific type of simple cycles included in a digraph.SOLUTION: A path detection device comprises a strongly connected component detection unit, a problem generation unit, a solution acquisition unit, and a simple cycle detection unit. The strongly connected component detection unit detects strongly connected components included in a digraph. The problem generation unit generates a QUBO problem with respect to the strongly connected components. The solution acquisition unit acquires a solution of the QUBO problem with respect to the strongly connected components. The simple cycle detection unit detects a simple cycle included in a subgraph with respect to the strongly connected components. A plurality of decision variables included in an objective function in the QUBO problem correspond to a plurality of nodes or a plurality of edges included in the strongly connected components. The subgraph is a part including two or more nodes or two or more edges selected on the basis of the solution, in the strongly connected components. A QUBO matrix included in the objective function is set so as to minimize the objective function if the subgraph satisfies a preset condition.SELECTED DRAWING: Figure 6

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.