Abstract: A program for causing a computer to execute processing that searches for a combination of a plurality of state variable values with which a value of an evaluation function is minimized or maximized by a Markov chain Monte Carlo (MCMC) method, the processing including: reading time-series data from a storage device storing the time-series data indicating a time change of the value of the evaluation function at a time of a search by the MCMC method that uses a first temperature; generating a plurality of time-series data sets that includes the value of the evaluation function for each period on a basis of the time-series data; calculating an index value based on magnitude of correlation between each of the plurality of time-series data sets; and determining a second temperature to be used for the search based on the index value.

Abstract: An apparatus of searching for a combination of values of state variables with which a value of an evaluation function of an Ising-type becomes a local minimum or maximum, the data processing apparatus including: a memory configured to store first local fields representative of first change amounts of the value of the evaluation function in a case where a value of each of the state variables changes, first coefficients indicative of strength of influence of each of the state variables on each of constraint terms representative of a constraint condition, and second local fields represented by a sum of a total sum of products of each of the first coefficients and each of the state variables and a second coefficient related to the constraint condition; and a processor configured to perform: reading any of the first coefficients related to a first state variable being any of the state variables.

Abstract: A noise estimation method includes decomposing a first matrix in which values of elements are represented by binary values into a coefficient matrix and a basic matrix, and estimating an element including noise among elements of the first matrix based on a result of comparison between a second matrix obtained by combining the coefficient matrix with the basic matrix and the first matrix.

Abstract: An information processing device includes: a memory configured to hold values of state variables included in an evaluation function presenting energy and a weight value for each set of the state variables; and a processor coupled to the memory and configured to: calculate an energy change value when each of the values of the state variables is set as a next change candidate based on the values of the state variables and the weight value; calculate a total energy change value by adding a penalty value according to an excess amount violating an inequality constraint, to each of the energy change values calculated for the state variables, the excess amount being calculated based on a coupling coefficient and a threshold value; and change any value of the state variables in the memory based on a set temperature value, a random number value, and the total energy change values.

Abstract: An optimization apparatus includes: a memory; and a processor coupled to the memory, the processor being configured to (a): execute transformation processing that includes transforming an input first cost function to a second cost function by using at least any one of “Legendre transformation,” “Lagrange function,” and “Wolfe's duality theorem,” (b): execute binary expansion processing that includes acquiring values in an Ising format by carrying out binary expansion for values relating to coefficients of the second cost function, (c): execute Ising machine processing that includes seeking values that represent a ground state regarding the values in the Ising format relating to the coefficients of the second cost function, and (d): execute reverse binary expansion processing that includes acquiring values that minimize the first cost function by carrying out reverse binary expansion for the sought values that represent the ground state.

Abstract: An optimization apparatus includes a memory and a processor. The memory stores one or more coupling coefficients that represent interaction of a plurality of variables corresponding to a plurality of bits included in an energy function. The processor selects, based on a difference of a value of the energy function associated with inversion of a value of each of the plurality of bits, adoption or rejection of bit inversion to perform optimization. The processor specifies a coupling coefficient corresponding to an auxiliary variable from the one or more coupling coefficients, the auxiliary variable being a product of variables corresponding to respective bits from which a variable corresponding to a specific bit in the energy function is excluded, and executes calculation of a term of a third-order or higher of a difference associated with inversion of the specific bit using the auxiliary variable and the coupling coefficient.

Abstract: An information processing apparatus includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to decompose a first matrix of a coupling coefficient which represents interaction between a plurality of variables into a plurality of matrices by using a rank number, obtain, from the plurality of matrices, a second element that corresponds to a first element of the coupling coefficient, and restore the first element based on the second element.

Abstract: An optimization method implemented by a computer configured to search for a solution using a replica exchange method, the optimization method includes: generating a reference bit to be referred to by each of a plurality of replicas, based on first states of respective replicas of the plurality of replicas at a time that is predetermined; causing each of the plurality of replicas to refer to the generated reference bit; and specifying second states at a time later than the time.

Abstract: A noise estimation method includes decomposing a first matrix in which values of elements are represented by binary values into a coefficient matrix and a basic matrix, and estimating an element including noise among elements of the first matrix based on a result of comparison between a second matrix obtained by combining the coefficient matrix with the basic matrix and the first matrix.

Abstract: An information processing device includes: a memory configured to hold values of state variables included in an evaluation function presenting energy and a weight value for each set of the state variables; and a processor coupled to the memory and configured to: calculate an energy change value when each of the values of the state variables is set as a next change candidate based on the values of the state variables and the weight value; calculate a total energy change value by adding a penalty value according to an excess amount violating an inequality constraint, to each of the energy change values calculated for the state variables, the excess amount being calculated based on a coupling coefficient and a threshold value; and change any value of the state variables in the memory based on a set temperature value, a random number value, and the total energy change values.

Abstract: An optimization apparatus includes: a memory; and a processor coupled to the memory, the processor being configured to (a): execute transformation processing that includes transforming an input first cost function to a second cost function by using at least any one of “Legendre transformation,” “Lagrange function,” and “Wolfe's duality theorem,” (b): execute binary expansion processing that includes acquiring values in an Ising format by carrying out binary expansion for values relating to coefficients of the second cost function, (c): execute Ising machine processing that includes seeking values that represent a ground state regarding the values in the Ising format relating to the coefficients of the second cost function, and (d): execute reverse binary expansion processing that includes acquiring values that minimize the first cost function by carrying out reverse binary expansion for the sought values that represent the ground state.

Abstract: A learning method includes: randomly selecting one or more feature vectors from feature vectors for learning to form a sample set, by a processor; selecting, from the feature vectors for learning, one of feature vectors appended with a label different from a label appended to a feature vector included in the sample set as a reference vector, the selecting being carried out based on a generalized average of distance from a feature vector included in the sample set, by the processor; and learning a hyperplane that divides a feature vector space, the learning being carried out using a pair of one of feature vectors appended with a label different from a label appended to the reference vector, among the feature vectors for learning, and the selected reference vector, by the processor.

Abstract: A data placement device creates a similarity index for each of computational resources based on a similarity between each of the pieces of acquired data and each of the pieces of data stored in the computational resources. The data placement device allocates on the basis of the similarity index of each of the computational resources with respect to the pieces of the data, the pieces of the data to each of the computational resources by using a matching system in which the similarity index associated with each allocation becomes stable in a direction in which the similarity index is small. The placement device places the pieces of the acquired data into the computational resources on the basis of the allocation result.

Abstract: A data compression apparatus includes a memory; and a processor configured to generate compressed matrix data, compare a threshold and an index value calculated about a specific value data string that is a data string obtained by coupling specific values specified from element values that are not zero values in each row of the compressed matrix data, specify a given constant as respective coefficients when the index value is larger than the threshold, calculate reciprocals of respective specific values as the respective coefficients when the index value is equal to or smaller than the threshold, and output matrix data after operation that is a result of rounding based on the number of places of significant figures of a decimal part in each element that corresponds about products of respective elements of the compressed matrix data and the respective coefficients calculated, regarding the respective elements of the compressed matrix data.

Abstract: A data compression apparatus includes a memory; and a processor configured to generate compressed matrix data, compare a threshold and an index value calculated about a specific value data string that is a data string obtained by coupling specific values specified from element values that are not zero values in each row of the compressed matrix data, specify a given constant as respective coefficients when the index value is larger than the threshold, calculate reciprocals of respective specific values as the respective coefficients when the index value is equal to or smaller than the threshold, and output matrix data after operation that is a result of rounding based on the number of places of significant figures of a decimal part in each element that corresponds about products of respective elements of the compressed matrix data and the respective coefficients calculated, regarding the respective elements of the compressed matrix data.

Abstract: A matrix decomposition device extracts information on positions where elements become 1 in a matrix having, as an element, an absolute value of a difference obtained by subtracting a product of an updated first matrix and an updated second matrix from the matrix as the decomposition target, and determines a number of trials to change spins based on a ranking number and the information on the positions, calculates an updated energy function based on the updated first matrix and the updated second matrix according to the generated energy function, as many times as a predetermined iteration count, changes a spin of a first matrix corresponding to selected column number information, and a spin of a second matrix corresponding to selected row number information, respectively, calculates a changed energy value by using the changed spin of the first matrix and the changed spin of the second matrix.

Abstract: A classification method includes: setting a position of a projective point based on information on a domain of definition of a feature value space, using a processor; and projecting a vector present in the feature value space to a sphere present in a space of dimensions higher by at least one dimension than a dimension of the feature value space by using the set position of the projective point, using the processor.

Abstract: A data arrangement apparatus executes a process request on data stored in a database, calculates similarity of a process in a combination of the process request and the data, divides the data in such a manner as to distribute the similarity and processes a process request accepted for the divided data.

Abstract: A storage unit stores structure data that includes coordinates of vertices of a plurality of polygons representing a three-dimensional structure. A computation unit calculates coordinates of a certain point with reference to the structure data. This point is used, together with the vertices of the polygons, to produce a graph from the structure data according to spatial arrangement of the polygons and further to calculate characteristic quantities based on the produced graph. The computation unit stores the calculated coordinates of the point in a memory device, as a piece of information relating to the structure data.

Abstract: A search device according to an embodiment maps a feature vector onto a hyper-sphere on the basis of parameters which include an intersection and a distance, with the intersection at which an m-dimensional feature space and a straight line passing through the hyper-sphere present in a space greater in dimension than m intersect and the distance being from the north pole of the hyper-sphere to the feature space. In this case, the search device searches for the parameters which allow the positions of feature vectors mapped onto the hyper-sphere to be concentrated on a predetermined hemisphere of the hyper-sphere.