222 results about "Combinatorial optimization problem" patented technology

The invention relates to the technical field of deep learning model training optimization, in particular to a fine-grained resource scheduling method and system in DNN model parallel training, and the method comprises the steps: converting a DNN model into calculation graph representation, and constructing a calculation graph in combination with the topological information of calculation equipment; modeling calculation stage division in a DNN model training process and resource mapping from each calculation stage to calculation equipment as a stage division and resource mapping combined optimization problem; and iteratively solving the combinatorial optimization problem by using a heuristic algorithm, and optimizing the computing equipment resource mapping of each computing stage by dynamically adjusting the weight of constraint conditions and minimizing the overhead and load difference of the computing equipment. According to the method, the calculation cost, the communication overhead, the overall load balancing and the solving time are comprehensively considered in the target function, efficient calculation resource allocation and task scheduling optimization are achieved, the training and reasoning efficiency of a large-scale deep learning model is improved, and the method has a good application prospect in the field of deep neural network distributed parallel training.

The embodiment of the invention discloses a quantum computer, a method and a related device for solving a combinatorial optimization problem, and the quantum computer comprises a measurement and control all-in-one machine which is configured to generate a control signal based on a constraint condition, the constraint condition is used for representing the constraint when a plurality of elements are combined; the quantum chip comprises at least one group of quantum bits, and the number of the quantum bits is the same as that of the elements; and the interface is connected with the measurement and control all-in-one machine and the quantum chip and is configured to control the quantum chip based on the control signal so as to generate an optimal combination of elements represented by the ground state of the group of quantum bits. By adopting the embodiment of the invention, the requirement of quantum bits can be effectively reduced, the method is suitable for a real quantum computer, and a practical and feasible quantum circuit can be operated to solve a combinatorial optimization problem.

The invention discloses a quantum Isin model construction method for a security constraint unit commitment optimization problem, and relates to the field of quantum computation.The quantum Isin model construction method comprises the steps that a security constraint unit commitment optimization model is constructed, and parameters of a mixed integer programming problem are obtained; using a Benders decomposition method to decompose a mixed integer programming problem into a main problem and a sub-problem; substituting the optimal binary solution to solve the sub-problem to obtain a new cut plane and expand a cut plane set; constructing a compact high-dimensional quadratic function fitting cutting plane set; solving a positive semidefinite programming problem to obtain a high-dimensional quadratic function parameter; converting a quadratic unconstrained binary optimization model constructed based on a high-dimensional quadratic function into an Isin model; solving the Isin model to obtain a quantum bit state, and solving an optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solution. According to the invention, the problem of huge consumption of quantum bit resources in the prior art is solved, especially the problem of difficulty in processing NP with complex constraints and more variables is solved.

A neuromorphic network may solve combinatorial optimization problems. The neuromorphic network may include variable neurons, a solution monitoring neuron, and one or more readout neurons. The variable neurons may each represent one binary variable in a combinatorial optimization problem. An internal state of a variable neuron may change as the variable flips. The internal state may be stored in a memory of the variable neuron. The variable neuron may spike when its internal state changes. One or more other variable neurons receiving the spike may determine whether to change their internal states based on the spike. The variable neurons may send their internal states to the solution monitoring neuron to compute a cost of the QUBO problem and determine whether a solution is found. A readout neuron may receive variable assignments resulting in the solution from at least some variable neurons and integrate the variable assignments into one message.

The invention belongs to the technical field of image classification, particularly relates to an automatic optimization method for image classification migration based on evolutionary computation, and is particularly suitable for automatically searching and optimizing a migration strategy through an evolutionary algorithm in a scene with large data distribution difference between an original domain and a target domain so as to improve the accuracy and generalization ability of target domain image classification. According to the method, fine tuning strategy search in an image classification task is modeled as a combinatorial optimization problem under structural constraints, and a customized genetic algorithm of structural perception and a lightweight performance prediction mechanism are creatively fused; the optimal fine tuning configuration adaptive to the target image classification task is efficiently and automatically searched at low cost, and the classification accuracy and generalization ability of the model in small sample, cross-domain and resource limited scenes are remarkably improved.

The invention discloses a short-wave time difference positioning method based on particle swarm optimization, which is realized by aiming at a short-wave signal source positioning problem under the condition that ionized layer information is unknown through the following steps of: dynamically screening a receiving station combination with optimal geometric distribution by adopting a particle swarm optimization algorithm based on longitude and latitude and time difference observation data of a receiving station group; taking the main receiving station as a coordinate origin, and converting the geographic coordinates of the selected station into rectangular plane coordinates; constructing a time difference positioning equation set by using a Chan algorithm, and calculating a target position through pseudo-inverse solution and an iteration strategy; and filtering the ill-conditioned solution based on a geometric accuracy factor threshold, analyzing a positioning point cluster through sliding window density, and outputting median coordinates as a final positioning result. According to the method, the receiving station is innovatively selected and modeled as a combinatorial optimization problem, the positioning precision in a complex electromagnetic environment is remarkably improved through a collaborative architecture of particle swarm optimization and a Chan algorithm, and the problem of positioning drift caused by poor geometric distribution of stations in a traditional method is effectively solved.

The invention discloses a carbon flow optimization method and device for edge calculation under vehicle-road cooperation and a medium, and relates to the technical field of traffic carbon emission. The method comprises the steps that a real-time state parameter sequence and environment information of a vehicle are acquired, and at least two candidate driving strategies are generated through a built-in carbon emission digital twinborn model; according to the candidate driving strategies, the corresponding expected carbon emission amount is calculated by inquiring the emission factor mapping table of the specific power of the vehicle and integrating; packaging the candidate driving strategy and the corresponding expected carbon emission into a bidding packet, and sending the bidding packet to an edge computing node on the roadside; based on the received bidding package, carbon right reverse auction is initiated, a combined optimization problem is solved by adopting a heuristic search algorithm, and a bid winning strategy set is determined; and generating a cooperative driving strategy according to the bid winning strategy set, and issuing the cooperative driving strategy to the corresponding vehicle end node. By means of the method, information distortion is restrained, and direct and real-time optimization control over regional total carbon emission is achieved.

The invention discloses an intelligent discharging method and device based on array combinatorial optimization and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: analyzing a target graphic file to extract cutting piece information in the target graphic file; classifying the cutting piece information so as to classify the outline of the flexible material and sub cutting pieces in the flexible material; constructing an initial typesetting scheme according to the flexible material contour and the sub-cutting pieces; selecting a reference typesetting scheme from the initial typesetting schemes according to a preset fitness function; performing cross processing on the reference typesetting scheme; performing variation processing on the part of the reference typesetting schemes after the cross processing; carrying out correction processing on the reference typesetting scheme which is subjected to cross processing and is not subjected to variation processing and the reference typesetting scheme which is subjected to variation processing; and taking the reference typesetting scheme with the highest fitness as a target typesetting scheme. Typesetting of the sub-cutting pieces is regarded as a combinatorial optimization problem, and the material utilization rate is improved.

There is provided a quantum computing method for obtaining an optimal solution to a problem having a plurality of discrete variables, where the problem is represented by a cost function, the method comprising:-generating a graph structure from the cost function,-dividing the graph structure into at least two separate sub-graph structures, where each sub-graph structure comprises a subset of the plurality of variables,-dividing the sub-graph structures into at least two separate sub-graph structures, mapping each subgraph structure to a local cost function represented as a local cost Hamiltonian, determining, for each local cost Hamiltonian, all eigenstates corresponding to an energy below a predetermined cutoff energy using a quantum processing device, wherein each variable in the subset of the plurality of variables is represented by a qubit of the quantum processing device,-recombining the determined eigenstate, and-approximating a ground state from the recombined eigenstate, where the ground state represents the optimal solution.

The invention discloses a parallel computing method for a unit commitment optimization problem, which relates to the technical field of power systems and comprises the following steps: acquiring day-ahead optimization scheduling input data of a power system; decomposing the scheduling time period set with the high time resolution into a reference scheduling time period subset and a coordinated scheduling time period subset; building mutually independent unit commitment sub-optimization models in parallel and solving the mutually independent unit commitment sub-optimization models; according to a preset data restoration rule and the first unit optimal start-stop plan matrix of each unit combination sub-optimization model, restoring to obtain a second unit optimal start-stop plan matrix; determining a global set of units needing to be started and stopped and a set of remaining units; determining the start-up time period of each unit in the remaining unit set in a rolling identification form; and a unit commitment optimization model is constructed and solved. The method is used for solving the problem of unit commitment optimization, especially the problem of unit commitment optimization of a power system with high time resolution and the day-ahead clearing calculation of a power spot market.

The invention belongs to the technical field of energy consumption optimization of reconfigurable robots, and particularly relates to a deterministic optimization method and system for variable-structure energy consumption of a modular robot. The deterministic optimization method for the variable structure energy consumption of the modular robot comprises the following steps: determining an initial structure before the variable structure of the modular robot starts and a target structure after the variable structure of the modular robot ends; in the structure changing process of the modular robot, a motion replacement mechanism is used, so that the module can penetrate through the interior of the current structure to move; according to the method, the problem of minimum moving steps of all modules in the modular robot allosteric process is converted into the problem of combinatorial optimization of task allocation, a global optimal allosteric solution is obtained through configuration sliding and Hungary algorithm solving, and allosteric energy consumption optimization is achieved. According to the method, a motion substitution mechanism is provided, the globally optimal solution is rapidly and efficiently calculated through configuration sliding and the Hungary algorithm, the limitation that solving of a probability search method has randomness is overcome, and the certainty of the solution can be guaranteed.

The invention discloses an automatic guided vehicle path planning method based on a selection probability correction ant colony algorithm, relates to the field of intelligent optimization algorithms, and is suitable for logistics path optimization, robot navigation, intelligent traffic scheduling, network routing optimization and other application scenes needing to solve a combinatorial optimization problem. According to the method, an ant colony algorithm automatic guided vehicle path planning model is constructed, a straight movement preference probability correction mechanism is established, a sudden change point probability correction strategy is formulated, a pheromone redistribution rule is designed, the short-distance direction change frequency is reduced, the occurrence frequency of sudden change points is reduced, the attraction of a later-stage high-quality path is enhanced, and the path smoothness is improved.

Provided is a quantum computing method for obtaining an optimal solution of a problem with multiple discrete variables, wherein the problem is represented by a cost function, the method comprising:—generating a graph structure from the cost function,—dividing the graph structure into at least two disjunct subgraph structures, wherein each subgraph structure comprises a subset of the multiple variables,—mapping each subgraph structure to a local cost function represented as local cost Hamiltonian,—determining, for each local cost Hamiltonian, all eigenstates corresponding to an energy below a predetermined cut off energy using a quantum processing device, wherein each variable of the subset of multiple variables is represented by a qubit of the quantum processing device,—recombining the determined eigenstates, and-approximating a ground state from the recombined eigenstates, wherein the ground state represents the optimal solution.

According to a preferred aspect of the invention, provided is an information processing apparatus including a processor and a storage device. A penalty coefficient setting unit is implemented by the processor and the storage device using a solution finding function for obtaining a solution of a combinatorial optimization problem using a cost function and a constraint condition. The penalty coefficient setting unit sets a penalty function and a penalty coefficient based on the constraint condition related to a logical operation imposed between two variables of the cost function and a value of a model coefficient of the cost function such that the solution of the combinatorial optimization problem satisfies the constraint condition, and searches for the solution of the combinatorial optimization problem based on the penalty function and the penalty coefficient.

The invention relates to a method and device for determining a machining execution scheme of a multifunctional machine tool, and the method comprises the steps: determining the type of a tool required by each working procedure and a task relation table between each working procedure and the tool, and generating a plurality of groups of machining execution schemes based on a genetic algorithm; calculating the total switching time of the scheme through a total switching time calculation model and a task relation table, converting the total switching time into fitness, and carrying out collaborative optimization on a process machining sequence and a tool installation sequence of each machining execution scheme based on the fitness to obtain a target machining execution scheme corresponding to a global optimal individual of the genetic algorithm; by constructing a total switching time calculation model, uniformly considering two mutually coupled decision variables and applying a genetic algorithm to provide a thought and a tool for solving a combinatorial optimization problem, collaborative optimization is performed on the model, the tool replacement frequency caused by process switching and unnecessary movement of a tool replacement path in a tool magazine can be systematically reduced, and the tool replacement efficiency is improved. And the total cutter switching time in the machining process is reduced to the minimum.

The invention belongs to the technical field of power system coordinated optimization, and relates to a light quantum acceleration-based multi-microgrid active power distribution network coordinated optimization method, which comprises the following steps of: 1, constructing a light quantum acceleration-based multi-microgrid active power distribution network coordinated optimization framework according to an optimization target and an actual condition; 2, constructing a light quantum acceleration collaborative optimization model for the multi-microgrid active power distribution network; 3, discretizing the multi-microgrid active power distribution network collaborative operation model, and then establishing a quantum interpretable model on the basis; 4, performing problem decoupling aiming at the discretization model, using transmission line power deviation as a coupling relation of an upper main body and a lower main body, and then providing a precision adjustment method for a precision problem of a light quantum computer so as to identify an adaptive solver of the problem; according to the method, the modeling flexibility of mixed integer optimization is reserved, and the potential parallelism and acceleration capability of quantum calculation in processing large-scale combination optimization problems are fully exerted.

The invention provides a quantum line optimization method and system based on a quantum approximate optimization algorithm. The quantum line optimization method comprises the steps of obtaining a graph structure; based on a preset disassembly strategy, decomposing the obtained graph structure to obtain one or more sub-graphs; based on a preset line routing rule, determining optimizable edges in each sub-graph; determining a quantum circuit according to the sub-graph and optimizable edges; and based on the optimizer, the plurality of processors execute in parallel, the optimal parameters of the quantum circuit are determined, and an optimized quantum circuit is obtained. The method is used for efficient optimization of a complex graph structure, and the efficiency and applicability of a quantum approximate optimization algorithm in solving the combinatorial optimization problem are improved.

This invention proposes a demand response resource combination optimization method that considers response reliability and main transformer load balancing. By constructing a demand response resource combination optimization model, the reliability of resource response and the load balancing of multiple main transformers are incorporated into the evaluation. A heuristic optimization algorithm is used to solve the problem, resulting in a demand response resource combination. The demand response resource combination optimization model includes: an objective function that minimizes the overall network demand response resource response cost; response reliability constraints of participating resources; load balancing constraints after the demand response resource is connected to the main transformer; and logical constraints representing whether the demand response resource is included. The heuristic optimization algorithm, combined with the objective function that minimizes the demand response cost, sequentially judges the satisfaction of response reliability constraints and main transformer load balancing constraints after a certain demand response resource is included in the response, and adjusts the combination of demand response resources to provide a feasible solution to the problem.

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 invention relates to an optical Ising calculation method and system based on dual digital micromirror devices (DMDs). First, the combinatorial optimization problem is mapped to an Ising model. Multiple mask vectors are constructed, and each mask vector is decomposed into multiple binary bit planes. A spin configuration matrix is ​​constructed and loaded into DMD-A. For each mask vector, its binary bit planes are sequentially loaded into DMD-B. The light intensity modulated by the two DMDs is collected and processed. The optical inner product is obtained by combining the bit-weighted components, and then the energy contribution value of the mask vector is calculated. The total Hamiltonian is obtained by summing all contributions. The energy difference is calculated by randomly flipping the spins and repeating the above steps. The probability of accepting the current flip is calculated based on the energy difference. This process is iterated until the maximum number of iterations is reached to obtain the final solution. The system is used to implement the above method. Compared with the prior art, this invention can achieve a high-speed, stable, and coherently independent optical Ising solution method.

According to one embodiment, an information processing apparatus comprising a processor. The processor is configured to execute: first search processing of searching for a first provisional solution of a combinatorial optimization problem in which an objective function is partially differentiable, using a first search machine that solves an overall problem of the combinatorial optimization problem; and a second search processing of outputting a final solution of the combinatorial optimization problem based on a comparison between signs of the first provisional solution and a partial differential value of the objective function.

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

The application discloses an anti-characteristic-unstable Tor App traffic analysis method and system, first extracts basic characteristics and bidirectional statistics of traffic generated by a mobile phone App running on Tor as a candidate characteristic set, then selects a subset from the candidate characteristic set as the representation of the traffic, filters out the traffic of N specific application types by solving an application type combination optimization problem, selects N characteristic subsets from the candidate characteristic set, and iteratively selects a machine learning model and a characteristic set suitable for the specific application type by solving an optimal combination problem, so that the Tor App can be accurately identified. The application solves the problem of characteristic instability in Tor App traffic analysis caused by the instability of the mobile environment, the diversity of app work content and the increasing number of new apps running on Tor.

The application discloses a processing method and device for a combination optimization problem. The method comprises the following steps: running a segmented quantum circuit obtained by splitting an original quantum circuit, and performing error suppression on the measurement result of each segmented quantum circuit to obtain a first measurement result corresponding to each segmented quantum circuit after error suppression; recombining the obtained first measurement result to generate a second measurement result; obtaining an average energy expectation of the original quantum circuit based on the generated second measurement result; and when the execution of the segmented quantum circuit satisfies a specified condition, generating a target combination corresponding to the target combination optimization problem based on the average energy expectation. By using the embodiment of the application, the operation of a combination optimization algorithm with a large number of bits on quantum hardware is realized through the operation and recombination of the split quantum circuit.

The application claims a service-aware resource collaborative allocation method, belonging to the field of mobile edge computing resource allocation, which comprises: analyzing task demand characteristics, obtaining task abstract categories combined with task demand characteristics, and mapping them into corresponding demand types. Based on the task demand type, candidate collaborative node clusters are selected through destruction operators and repair operators, then the task and resource matching problem is modeled as a combinatorial optimization problem, the collaborative benefits of candidate node combinations are calculated, and the edge node combination with the highest collaborative benefit is selected. The application mainly considers the problems of task demand fuzziness and task and resource matching imbalance in the computing power network, designs a task demand type identification algorithm and a resource collaborative allocation strategy, selects a reasonable collaborative node cluster, and reduces the time delay while improving the resource utilization.

Systems and methods for solving problems including combinatorial optimization problems are disclosed. A set of solutions to a combinatorial optimization problem are obtained from a quantum computing system such as a quantum annealer. A pattern mining operation is performed on a set of solutions output by a quantum annealing. The patterns are input to a solver to generate a solution to the initial problem.

The present invention relates to a distributed quantum computing method, device, equipment and medium for combinatorial optimization. The method includes: modeling a combinatorial optimization problem to be solved to obtain a problem function and a global solution space, and constructing a corresponding Hamiltonian based on the problem function of the combinatorial optimization problem to be solved; randomly taking values from the value range of each bit in the global solution space to generate an initial global solution; extracting a first preset number of bit values from the initial global solution to construct a first subset; constructing a quantum circuit corresponding to the first subset; optimizing the first subset through the quantum circuit to obtain an updated global solution and calculating the expectation value of the Hamiltonian corresponding to the updated global solution, and taking the global solution corresponding to the minimum Hamiltonian expectation value as the optimal solution of the combinatorial optimization problem to be solved. The present invention effectively solves the computing power requirement problem of large-scale combinatorial optimization problems, improves the performance and accuracy of the algorithm, and has wide applicability and flexibility.

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.

A method for enhancing a hybrid classical algorithm for combinatorial optimization includes executing a variational algorithm on a quantum processor, the execution producing a subset (solution space) of a set of solutions of the variational algorithm within a predefined time period, the variational algorithm computing a quantum state of a quantum system corresponding to a combinatorial optimization problem, each solution in the subset having a corresponding value; sorting the subset of solutions according to a sorting criterion; isolating a portion of the subset of solutions based on the sorting, wherein the value corresponding to each solution in the portion is within a boundary bounded by a threshold; computing an average value of the solutions from the portion of the subset of solutions; and changing the variational algorithm to produce a second subset of solutions such that the second subset of solutions includes solutions having values within the boundary.

Systems and methods relate to selectively arranging a plurality of qubits into a spatial structure to encode a quantum computing problem. Exemplary arrangement techniques can be applied to encode various quantum computing problems. The plurality of qubits can be driven according to various driving techniques into a final state. The final state can be measured to identify an exact or approximate solution to the quantum computing problem.