166 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.

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 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.

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.

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.

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.

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.

The present disclosure a method for data regulations-aware cloud storage and processing service allocation. Conventional approaches fail to address the technical problem of data placement for storage as well as processing, considering multiple criteria. Further, the conventional approaches fail to address compliance with data regulations, tier pricing policy for multiple Cloud Service Providers (CSPs) which impacts storage and processing center selection and constraint satisfaction. The present disclosure proposes a joint optimization model for the selection of storage and processing services from multiple cloud service providers, taking into practical consideration of data regulations and tiered pricing, which has not been addressed in the prior art. To solve this hard multi-objective combinatorial optimization problem, the present disclosure utilizes a cost-reduction-based algorithm for obtaining optimal solution.

The invention discloses an ultra-parallel microwave photon Isin machine system based on wavelength division multiplexing and an optimization method, and belongs to the technical field of photon calculation and combination optimization. The Isin machine system comprises a multi-wavelength light source module, a microwave modulation and wavelength division multiplexing coding module, a nonlinear calculation and evolution module and a demultiplexing and dynamic feedback module, all the modules are optically and electrically connected to form a closed-loop system, and different optimization problems can be coded to independent wavelength channels to achieve parallel solving. The optimization method is applied to the Isin machine system, and closed-loop optimization is completed through the steps of coupling matrix mapping, microwave modulation loading, nonlinear evolution, dynamic feedback control and the like. The method supports synchronous processing of 1024 optimization problems, and energy consumption of a single problem is lt; the method is mainly applied to the fields of logistics scheduling, financial modeling, communication network optimization and the like, and an efficient physical calculation solution is provided for a large-scale combination optimization problem.

An information processing device judges, with respect to at least any one of multiple quantum bits in a quantum circuit, whether a corresponding first-order term is present in a cost operator. When the first-order term corresponding to any one of the plurality of quantum bits is not present the information processing device updates at least any one of the cost unitary operators so that the first-order term corresponding to the any one of the quantum bits and to which a new first variational parameter is assigned is included in the exponent part. The information processing device solves a combinatorial optimization problem based on multiple mixer unitary operators and the multiple cost unitary operators after updating at least the any one of the cost unitary operators.

A combinatorial optimization problem processing apparatus includes an annealing unit that acquires, using an annealing scheme, a candidate for a first solution that is a variable q satisfying a first condition regarding a partition function whose Hamiltonian is a function representing a combination optimization problem that is an analysis target and the function includes third- or higher-order terms of a variable q, the Hamiltonian being represented by an integral representation using a variable X, the Hamiltonian being represented by a sum of a first equation that includes only second- or lower-order terms of the variable q and a second equation that does not include the variable q, in an alternate optimization algorithm in which first optimization processing for acquiring the first solution and second optimization processing for acquiring a second solution that is a variable X satisfying a second condition regarding the partition function are repeated until an end condition is satisfied, a calculation unit that acquires the second solution, and a solution candidate acquisition unit that acquires a candidate for the immediately preceding first solution when the end condition is satisfied, as a candidate for a solution of the combinatorial optimization problem.

A computer-readable recording medium stores therein an information processing program for causing a computer to execute a process, the process including: calculating a first solution of a combinatorial optimization problem based on an Ising model corresponding to the combinatorial optimization problem; determining a value of a parameter of a quantum approximation optimization algorithm corresponding to the combinatorial optimization problem so as to maximize a probability that a quantum state of a quantum circuit of the quantum approximation optimization algorithm becomes the calculated first solution; and calculating a second solution of the combinatorial optimization problem based on the quantum circuit of the quantum approximation optimization algorithm in which the determined value of the parameter is set.

To obtain a proper tabu period in a short period.SOLUTION: A processing unit 12 searches for a solution of a combinatorial optimization problem represented by a combination of values of a plurality of state variables by a first tabu search in which a tabu period for fixing a value of a state variable whose value has changed is changed for each predetermined period. The processing unit 12 stores the number of state variables whose values have changed in the predetermined period in a storage unit 11 for each tabu period. The processing unit 12 determines a first tabu period for fixing the value of the state variable based on a slope of change in the number with respect to change in the tabu period, and searches for the solution by a second tabu search using the determined first tabu period.SELECTED DRAWING: Figure 1

To improve the reliability of quantum operations performed by quantum computers. [Solution] The information processing device generates quantum circuit coupling information based on first physical circuit information, and generates a combinatorial optimization problem and constraints based on topology information, quantum circuit coupling information, and calibration data. Based on the optimal solution among one or more solutions of the combinatorial optimization problem that satisfies the constraints, the information processing device generates second physical circuit information representing a second physical quantum circuit obtained by updating the first physical quantum circuit.

The present application relates to an optimization device, an optimization program, and an optimization method. The optimization device includes a solving unit. The solving unit is configured to: among a plurality of bits for solving a plurality of combinatorial optimization problems, assign a plurality of first bits to a first combinatorial optimization problem included in the plurality of combinatorial optimization problems, and assign a plurality of second bits to a second combinatorial optimization problem included in the plurality of combinatorial optimization problems. The solving unit is further configured to set an interaction between each of the plurality of first bits and each of the plurality of second bits to zero, and solve the plurality of combinatorial optimization problems.

The invention provides a high-order coupling Isin machine and a solution method for a combinatorial optimization problem, and relates to the technical field of microwave photonics. The high-order coupling Isin machine comprises a beam splitting unit which is configured to split a first optical signal carrying spin state information into a second optical signal and a third optical signal; a first spin storage unit configured to store a second optical signal; a first photoelectric converter unit configured to perform photoelectric conversion processing on the second optical signal; the first signal processing unit is configured to amplify, attenuate and filter the second optical signal subjected to photoelectric conversion so as to output a first electric signal; and the programmable unit is configured to demodulate the first electric signal based on a pre-stored n-order coupling tensor so as to extract spin state information and perform n-order spin coupling calculation on the spin state information so as to output a spin coupling signal, and n is an integer greater than or equal to 3.

The application discloses an airport crowd route guidance scheme optimization method and device and a storage medium, and the method comprises the following steps: acquiring layout information of an airport, and abstracting an airport scene into a complete graph according to the layout information; screening edges in the complete graph according to a preset screening condition, so as to delete edges that do not meet the condition; abstracting an airport crowd route guidance scheme optimization problem into a combination optimization problem according to the screened complete graph; and using a discrete differential evolution algorithm to optimize the combination optimization problem, so as to obtain a final optimization result. The application performs mathematical modeling on an airport departure layer scene, and automatically generates and optimizes an airport crowd route guidance scheme by using a discrete differential evolution algorithm, so that a high-quality solution can be obtained, and the guidance of the crowd route in the airport can be effectively assisted. The application can be widely applied to the fields of intelligent transportation and evolutionary calculation.