Abstract: The first candidate determination means 15B is configured to determine, based on seller information 41B indicating a sale condition of a transaction target presented by each of sellers and buyer information 42B indicating a buy condition of the transaction target presented by each of buyers, first candidates C1x to be plural candidates for combinations of the sellers and the buyers establishing valid transactions of the transaction target. The second candidate selection means 16B is configured to select plural second candidates C2x to be displayed from the first candidates C1x based on similarity among the first candidates C1x.

Abstract: The seller information acquisition unit 51B is configured to acquire seller information indicative of a sell condition of a transaction target, the sell condition being presented by each of a plurality of sellers. The buyer information acquisition unit 52B is configured to acquire buyer information indicative of a buy condition of the transaction target, the buy condition being presented by each of a plurality of buyers. The determination unit 54B is configured to determine combinations of each of the sellers and each of the buyers establishing the transaction, on the basis of the seller information acquired by the seller information acquisition unit 51B and the buyer information acquired by the buyer information acquisition unit 52B and a profit for a mediator mediating the transaction of the transaction target.

Abstract: An information processing (1) includes: input means (2) for inputting a predicted value of an amount of demand at each of a plurality of times; and output means (3) for outputting a production plan that satisfies the predicted value based on an optimum solution of an optimization model in which at consecutive times, a constraint is given to an amount of production of each of at least one production facility and a data range indicating a range of uncertainty is set in the amount of demand, the optimization model determining the production plan including a planned value of the amount of production of each of the at least one production facility at each of the times up to a predetermined time for the amount of demand at each of the times up to the predetermined time.

Abstract: A computational resource management apparatus is for managing a cluster system that executes a plurality of tasks. The computational resource management apparatus includes a condition specification unit that specifies a relationship between computational resources of the cluster system and computation time, a dependency relationship between tasks, and an execution time limit of each task, and a scheduling unit that determines, for each task, an execution sequence and computational resources to be allocated from among the computational resources of the cluster system, based on the relationship between the computational resources and computation time and the dependency relationship that are specified, such that the execution time limit is met.

Abstract: A computational resource management apparatus is for managing a cluster system that executes a plurality of tasks. The computational resource management apparatus includes a condition specification unit that specifies a relationship between computational resources of the cluster system and computation time, a dependency relationship between tasks, and an execution time limit of each task, and a scheduling unit that determines, for each task, an execution sequence and computational resources to be allocated from among the computational resources of the cluster system, based on the relationship between the computational resources and computation time and the dependency relationship that are specified, such that the execution time limit is met.

Abstract: A computational resource management device uses a measured value of an execution time of data processing, a measured value of a resource amount, and a feature of input data as training data to learn a model indicating a relationship between the execution time and the resource. The device inputs, into the model, a feature of data scheduled to be input to calculate an estimated value of the execution time of the scheduled data processing, and uses the estimated value of the execution time, a variation index indicating variation in the estimated value of the execution time, and distribution of estimated residuals to calculate a resource amount required in the scheduled data processing. The device creates an execution plan of the scheduled data processing, based on the estimated value of the execution time, the variation index, the distribution of estimated residuals, and the calculated resource amount.

Abstract: A query specification unit 81 specifies a query as a combination of a variable, on which an intervention operation is performed for a causal relation, and a value of the variable. An intervention data generating unit 82 generates intervention data including a value of a target variable, acquired with an intervention operation based on the query, and the query. A causal relation updating unit 83 updates the causal relation using the generated intervention data. On this occasion, the query specification unit 81 specifies a query that minimizes an expected loss by updating from among queries specified based on the expected loss representing an estimation error of the target variable by the query.

Abstract: In an optimization device 80, an explanatory variable used for explanation of a prediction target becomes an instrumental variable for optimization, and the optimization is performed on the basis of a prediction of the prediction target. A candidate set determination unit 81 determines a set of candidates for a predicted instrumental variable. For instrumental variables included in the set, a margin determination unit 82 determines a margin including an estimation error, which is an error due to the prediction, with a designated probability. A robust optimization unit 83 performs robust optimization related to the instrumental variables by using the determined margin.

Abstract: A first reception unit 81 receives, as an input, a graph including: a plurality of nodes representing experimental operations; a plurality of nodes representing results of the operations; and edges representing cause-and-effect relationships between the experimental operations and the operation results. A second reception unit 82 receives, as an input, either information indicating the degree of cause-and-effect relationship between each experimental operation and each operation result or past experimental results from which the strength of each cause-and-effect relationship can be estimated. On the basis of the input received by the first reception unit 81 and the information received by the second reception unit 82, an output unit 83 outputs the order in which a plurality of experimental operations are to be performed.

Abstract: A feature selection unit 81 selects, from a set of features that can influence the sales volume of a product, a first feature set as a set of features that influence the sales volume and a second feature set as a set of features that influence a price of the product. A learning unit 82 learns a predictive model in which features included in the first feature set and the second feature set are set as explanatory variables, and the sales volume is set as a prediction target. An optimization unit 83 optimizes the price of the product under constraint conditions to increase a sales revenue defined by using the predictive model as an argument. Further, the learning unit 82 learns a predictive model in which at least one feature included in the second feature set but not included in the first feature set is set as an explanatory variable.

Abstract: An evaluation system 80 includes an evaluation unit 81 for evaluating, when there is a prediction model estimated using data generated from the true model, the optimal solution calculated from the prediction model in consideration of bias generated between evaluation based on the prediction model and evaluation based on the true model.

Abstract: Computational resource management device includes a model learning unit that uses a measured value of an execution time of data processing, a measured value of a deresource amount, and a feature of input data as training data to learn a model indicating relationship between the execution time and the resource, an execution time estimation unit that inputs, into the model, a feature of data scheduled to be input to calculate an estimated value of the execution time of the scheduled data processing, a resource amount calculation unit that uses the estimated value, a variation index indicating variation in the estimated value, and distribution of estimated residuals to calculate resource amount required in the scheduled data processing, and an execution plan creation unit that creates an execution plan of the scheduled data processing, based on the estimated value, the variation index, the distribution of estimated residuals, and the calculated resource amount.

Abstract: An optimization system according to the present invention includes: a memory; and one processor being coupled to the memory and accepting an indicator probabilistically indicating a range of a prediction error related to a predicted value of the sales quantity, the predicted value being calculated with the prediction formula when a prediction formula predicting a sales quantity of a commodity is expressed by a function of a price of the commodity; optimizing the price to maximize the sales amount acquired by the objective function under a constraint with an objective function acquiring a sales amount including and being determined by the sales quantity and the price; and taking the predicted value and optimizing the price to increase a minimum value of the sales amount within the range of the prediction error, the range being indicated by the indicator.

Abstract: An information processing device according to one aspect of the present invention includes: a memory; and at least one processor coupled to the memory wherein, the processor performing operation, the operation comprising: acquiring an optimization model for calculating an optimum solution considering variation in one or more parameters; calculating the optimum solution in the optimization model; transforming the optimization model based on the optimum solution; and outputting the optimum solution.