Abstract: The inconsistency determination device 1 includes inconsistency determination means 9. The inconsistency determination means 9 determines whether or not there is inconsistency in an expression, which is to be converted into an energy function of a model representing states of individual spins by a first value or a second value, representing energy in a combinatorial optimization problem.

Abstract: The feature information derivation means 72 derives feature information that represents a feature of a model used to solve a combinatorial optimization problem, when the model is given. The solution method selection means 73 selects a solution method for the combinatorial optimization problem from among predetermined multiple types of solution methods based on the feature information. The solution request means 74 send a solution request that includes information that can specify the model used to solve the combinatorial optimization problem to a solution device that solves the combinatorial optimization problem using the selected solution method.

Abstract: An information processing device that compares a data amount of a QUBO model to be processed by an annealing engine and a data amount of a program representing the QUBO model, and that transmits whichever has a lower data amount to the annealing engine.

Abstract: The conversion means 72 generates a program for deriving a QUBO matrix that is a matrix that defines an energy function of QUBO, by converting a description of a natural language representing a combinatorial optimization problem into a description of the program for deriving the QUBO matrix. It is possible to generate a program for deriving a QUBO matrix based on a description of a natural language representing a combinatorial optimization problem.

Abstract: The annealing execution means 73 executes annealing using an energy function, when a solution to a combinatorial optimization problem, and the energy function of a model representing states of individual spins by a first value or a second value which is used to obtain the solution are given, and derives multiple new solutions to the combinatorial optimization problem. The solution determination means 74 determines a solution from among the multiple solutions derived by the annealing execution means 73, based on a solution determination rule for determining a solution among the multiple new solutions and the given solution to the combinatorial optimization problem.

Abstract: An optimization device includes a determination unit which determines weights for a plurality of constraint conditions imposed on a combinatorial optimization problem, respectively, and a generation unit which generates an energy function for the combinatorial optimization problem using a constant that is a sum of a constant used in formulation of an energy to be optimized in the combinatorial optimization problem and a sum over the plurality of constraint conditions of a constant used in formulation of an energy of one constraint condition multiplied by a weight determined for the one constraint condition.

Abstract: The difference matrix calculation means 72 calculates a difference matrix which is a difference between a first matrix representing a combinatorial optimization problem for which a solution was found last time and a second matrix representing the latest combinatorial optimization problem. The difference matrix compression means 73 compresses the difference matrix. The transmission means 74 transmits the difference matrix after compression to a combinatorial optimization problem solution device that finds a solution to the combinatorial optimization problem.

Abstract: A state output system includes a solving unit which executes a solving process for solving a combinatorial optimization problem by a simulated annealing method, an acquiring unit which acquires data indicating states of the solving process in execution from the solving unit, and an outputting unit which outputs the acquired data during the execution of the solving process.

Abstract: A solution accuracy guaranteeing annealing calculation device includes a first solving unit which solves a combinatorial optimization problem by an annealing method, and a second solving unit which solves a relaxation problem, which is a problem generated by relaxing constraints imposed on the combinatorial optimization problem, wherein the second solving unit calculates, if the combinatorial optimization problem is a minimization problem, a lower bound of a minimization target in the minimization problem by solving the relaxation problem generated from the combinatorial optimization problem, and calculates, if the combinatorial optimization problem is a maximization problem, an upper bound of a maximization target in the maximization problem by solving the relaxation problem generated from the combinatorial optimization problem.

Abstract: The term generation means 73 generates a term, when a solution to a combinatorial optimization problem, an energy function, which is used to obtain the solution, of a model representing states of individual spins by a first value or a second value, and a pair of identification information of spin and degree of wish to change which indicates degree to which an user wishes to change state of spin corresponding to the identification information in the solution are given, wherein the term is generated for each pair of the identification information and the degree of wish to change, based on the state of spin corresponding to the identification information in the solution and the degree of wish to change. The energy function derivation means 74 deriving a new energy function by adding terms generated by the term generation means 73 for each pair to the energy function.

Abstract: Provided is a solution system capable of obtaining the optimal state of individual spins with a small amount of memory. The matrix simplification means 93 changes a matrix used in an energy function in a model representing states of individual spins by a first value or a second value into simplified form. In this case, the matrix simplification means 93 deletes elements that have a value of “0” from the matrix, and also deletes some elements that do not have the value of “0” from the matrix, thereby to change the matrix into the simplified form. The spin state derivation means 94 derives the optimal state of each spin based on the matrix changed into the simplified form.

Abstract: Provided is a solution system capable of reducing the amount of computation when solving a combinatorial optimization problem by applying an energy function in a model representing a state of individual spins by a first value or a second value to simulated annealing. The input unit 2 receives input of an energy function in a model representing states of individual spins by a first value or a second value, wherein the energy function is corresponding to a combinatorial optimization problem to be solved. In a process of the simulated annealing, the simulated annealing unit 3 selects a spin, selects a set to which the spin belongs, and changes the states of one or more spins including the spin while a condition where the set satisfies a constraint is maintained, when the set satisfies the predetermined constraint and the state of the spin is determined to be changed.

Abstract: Each chip 70 includes weight storage unit for storing weights for each edge determined by learning under the condition that channels in a first layer that is a layer in a neural network and channels in a 0th layer that is a previous layer to the first layer are divided into groups whose number is equal to the number of the chips, respectively, the groups of the channels in the first layer and the groups of the channels in the 0th layer and the chips are associated, an edge is set between the channels belonging to corresponding groups, an edge is set between the channels belonging to non-corresponding groups under a restriction. The weight storage unit stores the weights determined for the edge between the channels, each of which corresponds to each chip including the weight storage unit, belonging to corresponding groups.

Abstract: The determining unit 72 divides channels in the 0th layer and channels in the first layer into groups whose number is equal to the number of chips that are included in an operation device executing an operation of the neural network using a learning result of the weight for each edge, respectively. The determining unit 72 determines association of the groups of the channels in the 0th layer and the groups of the channels in the first layer and the chips included in the operation device, and edges to be removed, and removes the edges to be removed. The weight assignment unit 73 stores the weights of the edges in the weight storage unit in the chip corresponding to the edge.

Abstract: A moving image coding device 10 includes: a cost determination unit 12 that determines, as a cost relating to a block to be coded, a minimum cost among appraisal costs relating to determined candidates for a motion vector; a storage unit 13 that stores, as coding information relating to the block to be coded, a candidate for the motion vector corresponding to the determined cost; and an update unit 14 that updates, to coding information relating to the block to be coded, the cost of which is smaller than the sum total of costs relating to blocks included in the block to be coded, the coding information relating to the included blocks and stored in the storage unit 13.

Abstract: A moving-image encoding device calculates a code amount generated by encoding a moving image including an I picture, a P picture, and a B picture as picture types, and calculates a complexity history of the picture which is encoded on the basis of the code amount and a quantization width used in the encoding. This moving-image encoding device estimates an estimated complexity of an encoding object picture to be encoded on the basis of the complexity history of an encoded picture of the same picture type as a picture type of the encoding object picture and the complexity history of the encoded picture immediately preceding the encoding object picture.

Abstract: A moving-image encoding device calculates a code amount generated by encoding a moving image including an I picture, a P picture, and a B picture as picture types, and calculates a complexity history of the picture which is encoded on the basis of the code amount and a quantization width used in the encoding. This moving-image encoding device estimates an estimated complexity of an encoding object picture to be encoded on the basis of the complexity history of an encoded picture of the same picture type as a picture type of the encoding object picture and the complexity history of the encoded picture immediately preceding the encoding object picture.

Abstract: A moving image processing device includes: a storage unit storing, by dividing an image into pixel blocks and performing image processing for the individual pixel blocks, an execution state of image processing for each pixel block in encoding or decoding processing for the image; a determination unit determining whether image processing for a first pixel block is executable, based on an execution state of image processing for a second pixel block for which image processing for the first pixel block has a dependence regarding a processing order; and an execution unit performing, in parallel or pseudo-parallel, image processing for the first pixel blocks for which image processing is determined to be executable by the determination unit, and updating an execution state of image processing for the first pixel block, whereby encoding or decoding processing for a moving image is accelerated.

Abstract: A moving image coding device 10 includes: a cost determination unit 12 that determines, as a cost relating to a block to be coded, a minimum cost among appraisal costs relating to determined candidates for a motion vector; a storage unit 13 that stores, as coding information relating to the block to be coded, a candidate for the motion vector corresponding to the determined cost; and an update unit 14 that updates, to coding information relating to the block to be coded, the cost of which is smaller than the sum total of costs relating to blocks included in the block to be coded, the coding information relating to the included blocks and stored in the storage unit 13.

Abstract: Provided is a moving image encoding device which is capable of determining an encoding mode through a small amount of calculation while suppressing a deterioration in encoding efficiency. In the present invention, a moving image encoding device includes an encoding mode determining unit for determining a combination of a plurality of encoding modes for each block.