93 results about "Coordinate descent" patented technology

A method recovers an uncorrupted low-rank matrix, noise in corrupted data and a subspace from the data in a form of a high-dimensional matrix. An objective function minimizes the noise to solve for the low-rank matrix and the subspace without estimating the rank of the low-rank matrix. The method uses group sparsity and the subspace is orthogonal. Random subsampling of the data can recover subspace bases and their coefficients from a much smaller matrix to improve performance. Convergence efficiency can also be improved by applying an augmented Lagrange multiplier, and an alternating stepwise coordinate descent. The Lagrange function is solved by an alternating direction method.

The invention provides a reconfigurable intelligent surface assisted multi-user MIMO uplink transmission method. According to the transmission method, a signal sent by a user side is reflected to a base station through an RIS, and the RIS can change the phase of the signal incident on the RIS. Aiming at a bottleneck problem of channel information acquisition of an RIS-assisted multi-user MIMO transmission system, only partial channel state information is utilized, including instantaneous channel state information from the RIS to a base station channel and statistical channel state informationfrom a user to the RIS channel; through an alternate optimization method, a deterministic equivalence principle, a block coordinate descent method, an MM method and the like, a sending covariance matrix and an RIS phase shift matrix of a user are jointly designed to maximize the global energy efficiency of the system or traverse the spectral efficiency; when the channel state information is changed, the user side dynamically implements transmission power distribution with maximum energy or spectral efficiency, and the RIS dynamically adjusts the phase shift matrix. According to the invention,the implementation complexity is low, and the performance of multi-user MIMO uplink transmission can be effectively improved.

Mobile edge computing (MEC) provides the advantages of low delay, low power consumption and high reliability based on a mobile network edge through provision of an IT service environment and cloud computing capacity, so the mobile edge computing becomes a hotspot of future 5G research. The invention discloses a task unloading and resource allocation scheme based on MEC. The scheme comprises the steps of setting an unloading decision and resource allocation joint optimization problem; optimizing an unloading decision through adoption of a coordinate descent method; carrying out subchannel allocation on users when user delay conditions are satisfied through adoption of an improved Hungarian algorithm and greedy algorithm; and converting energy consumption minimization problem into a power minimization problem, and converting the power minimization problem into a convex optimization problem, thereby obtaining the optimum sending powers of the users. According to the scheme, different delay requirements of different users can be satisfied, system total energy consumption can be minimized, and system performance can be effectively improved.

The invention discloses a UAV (Unmanned Aerial Vehicle)-assisted WSN (Wireless Sensor Network), as well as a method for designing node scheduling, route planning and power distribution of the same, and belongs to the technical field of wireless communication and IOT (Internet Of Things). The UAV-assisted WSN consists of a UAV aerial base station and K single-antenna ground terminal nodes. The method of the invention is characterized by aiming at minimizing system power consumption, considering constraint conditions such as a data transmission rate of the terminal node, a flight speed of the UAV and a transmitting power of the UAV, building a mathematical optimization model by taking a scheduling variable of the terminal node, a flying route of the UAV and a transmitting power variable of the UAV as parameters, respectively converting two sub-problems decomposed from a non-convex optimization problem of mixed integer variables into upper bound convex problems corresponding to the two sub-problems, alternately iterating sub-optimization problems through a block coordinate descent method and an interior point method, and obtaining a suboptimal parameter solution for scheduling of theterminal node, the flying route and the power distribution of the UAV.

A method and system for image reconstruction of data acquired by a device such as computed tomography is provided. The method and system use a multi-stage statistical iterative reconstruction techniques to provide a three dimensional representation of the scanned object. In one embodiment, the first stage uses a projection-based reconstruction technique, such as Ordered Subset (OS) to converge on a solution for low frequency portion of the image. A subsequent stage uses a voxel-based reconstruction technique, such as Iterative Coordinate Descent (ICD), to converge on a solution for high frequency portions of the image. Systems and methods for reconstructing images from incomplete or partial projection data is also provided.

The invention discloses a multi-view monitoring video behavior detection and recognition method based on rarefaction, structuralization and judgment under multiple constraints, and the method is used for realizing the intelligent analysis and management of a monitored video. The method comprises the following steps of (1) preprocessing the video; (2) detecting a target; (3) tracking the target; (4) extracting the time and space characteristics; (5) sliding a time window and normalizing the characteristics; (6) detecting and recognizing the behavior in the multi-view monitored video under multiple constraints; and (7) fusing results of two adjacent time windows. The method has the advantages that the intrinsic correlation characteristics of human body behavior characteristics in a multi-view scene can be adequately analyzed, and through the excavation of rarefraction, structuralization and judgment constraints and the compilation of corresponding regular terms, a multi-view human body behavior recognition target function can be established; and meanwhile, the human body target behavior can be recognized through a coordinate descent method.

The invention discloses an unmanned aerial vehicle base station bandwidth and track joint optimization method with limited pushing power. The method is suitable for an unmanned aerial vehicle emergency communication scene. According to the invention, a single unmanned aerial vehicle is used as an air base station; in a scene of providing downlink transmission services for a plurality of users in aprocess of flying from a starting point to a terminal point, under the condition that the pushing power and the flight time of the unmanned aerial vehicle are limited; the minimum average rate of each user in the system is maximized by adjusting the flight path and the user bandwidth distribution; a non-convex optimization problem is established according to the moving position of the unmanned aerial vehicle, the pushing power and the bandwidth constraint, and the non-convex optimization problem is converted and solved by using a block coordinate descent and continuous convex approximation method, so that the minimum average rate of each user in the system is maximized, and the energy and frequency band resource utilization rate is maximized. The method has important guiding significancefor the unmanned aerial vehicle communication system under the condition that the pushing power and the total bandwidth are limited.

Optimal enhancement of 3G cellular network capacity utilizes two components of learning and optimization. First, a pair of learning approaches are used to model cellular network capacity measured in terms of total number of users carried and predict breakpoints of cellular towers as a function of network traffic loading. Then, an optimization problem is formulated to maximize network capacity subject to constraints of user quality and predicted breakpoints. Among a number of alternatives, a variant of simulated annealing referred to as Block Coordinated Descent Simulated Annealing (BCDSA) is presented to solve the problem. Performance measurements show that BCDSA algorithm offers dramatically improved algorithmic success rate and the best characteristics in utility, runtime, and confidence range measures compared to other solution alternatives. Accordingly, integrated iterative method, program, and system are described aiming at maximizing the capacity of 3G cellular networks by redistributing traffic from congested cellular towers to non-congested cellular towers.

The invention discloses a maximal posteriori optimized image reconstruction method for leading in a general Gibbs experiment in the PET imaging. The method comprises the following procedures: (1) PET imaging equipment is utilized to collect detector data before imaging, and the corrective parameter value and the system matrix of various data in the imaging equipment are obtained simultaneously; (2) a mathematical statistic statistical model used for reconstructing an image is reconstructed according to a statistical feature that is met by the corrective data acquainted by the procedure (1) before imaging; (3) the general Gibbs experiment is led in aiming at the compute of a mathematical model in the procedure (2) , a maximal posteriori estimate method is adopted to perform the conversion of a reconstruction model, to obtain an optimized equation with a constrained objective function used for obtaining a PET reconstruction image; (4) a parabola is adopted to replace a coordinate descent algorithm, to perform the iterative computation treatment and to reconstruct the image based on the selection of a global parameter in the optimized equation through a result obtained by the procedure (3). The invention can greatly improve the quality of the PET reconstruction image.

The invention discloses a synthetic aperture radar self-focusing method. The synthetic aperture radar self-focusing method specifically comprises the following steps: range pulse compression, range migration correction, azimuth de-ramping, coordinate descent multidimensional phase error estimation, single azimuth unit phase error estimation and multidimensional phase error estimation value output. The synthetic aperture radar self-focusing method disclosed by the invention has the advantages that since a coordinate descent method is used for conducting multidimensional joint estimation of phase errors of each azimuth at each moment and a multidimensional projection method is used for transforming the problem of solving maximum contrast by one-dimension search into the problem of solving a quartic polynomial, the calculation amount is greatly reduced; the estimation of azimuth phase errors by adopting the method disclosed by the invention has the feature of no order limit.

The invention provides an unmanned aerial vehicle spectrum resource allocation method based on block coordinate descent. The method comprises the following steps of determining the number of unmannedaerial vehicles, setting a system licensed channel set and setting the system maximum power upper limit and other environment parameters; establishing a spectrum allocation matrix and an adjacent channel interference matrix, importing an original problem, and simplifying an original complex max-min mixed planning problem through utilization of problem structure features; providing a low complexityiterative algorithm based on the block coordinate descent, thereby solving the problem; and carrying out power and channel allocation on the unmanned aerial vehicles by a ground control station according to the maximum SINR value of each unmanned aerial vehicle through utilization of remote control channels, thereby finishing designing the unmanned aerial vehicle time-frequency resource allocation method. The method has the beneficial effects that according to the unmanned aerial vehicle spectrum resource allocation method based on the block coordinate descent, on the premise of improving thereliability of receiving control signals by each unmanned aerial vehicle and ensuring the system maximum consumption power constraint, limited spectrum resources are reasonably allocated.

The invention discloses a hot continuous rolling strip steel convexity prediction method based on a gradient boosting tree model. The method comprises the steps: selecting technological parameters, equipment parameters, strip steel parameters and the actual strip steel convexity of related hot rolling strip steel as input and output of a hot continuous rolling strip steel convexity prediction model; collecting related original modeling data at a hot-rolled strip steel production site, preprocessing the data, and obtaining final modeling data by removing missing values and abnormal values and balancing the data; dividing the final modeling data obtained through preprocessing into a training data set and a test data set according to a certain proportion; based on the training data set, establishing a hot continuous rolling strip steel convexity prediction model based on a gradient boosting tree algorithm through cross validation; determining optimal parameters of the hot continuous rolling strip steel convexity prediction model by adopting a coordinate descent method; and evaluating the performance of the established hot continuous rolling strip steel convexity prediction model basedon the test data set. According to the method, the convexity of the hot continuous rolling strip steel can be accurately predicted, and the problem of large convexity deviation of the hot continuousrolling strip steel can be solved.

The invention relates to a strategy method for minimizing energy consumption of a mobile edge computing network under a time-sensitive condition, which is characterized by comprising the following steps of: 1, an initialization stage: acquiring basic configuration information of the network by a node in the network at the stage, and initializing related parameters; 2, establishing an energy consumption minimum system optimization model; 3, solving an initial optimal unloading ratio according to constraint conditions; 4, when the constraint condition is met, returning to the step 2, and entering the next round of iterative loop calculation according to the block coordinate descent algorithm; and 5, obtaining the optimal unloading ratio, computing resources and subcarrier allocation expressions of different computing tasks of each user node after reaching the maximum limit frequency in a circulating manner. Under the condition that the QoE requirement of a user for a timeliness calculation task is met, the total energy consumption of the whole mobile edge calculation network is minimized through three strong coupling optimization variables including decoupling optimization, unloadingratio optimization, calculation resource allocation and subcarrier allocation.

Optimal reduction of 4G LTE cellular network congestion utilizes two components of learning and optimization. First, an MLPDL learning approach is used to model cellular network congestion measured in terms of PRB utilization and predict 80% utilization as breakpoint thresholds of cellular towers as a function of average connected user equipments. Then, an optimization problem is formulated to minimize LTE network congestion subject to constraints of user quality and load preservation. Two alternative solutions, namely Block Coordinated Descent Simulated Annealing (BCDSA) and Genetic Algorithms (GA) are presented to solve the problem. Performance measurements demonstrate that GA offers higher success rates in finding the optimal solution while BCDSA has much improved runtimes with reasonable success rates. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the congestion of 4G LTE cellular networks by redistributing traffic from congested cellular towers to non-congested cellular towers.

The invention provides a method for distribution of time-frequency resources of unmanned aerial vehicles based on block coordinate descent. The method comprises the following steps of determining thenumber of the unmanned aerial vehicles, setting a system authorization channel set, and setting a maximum power upper limit of the system and other environmental parameters; constructing a spectrum allocation matrix and an adjacent channel interference matrix to be introduced into an original problem, and utilizing the structural characteristics of the problem to simplify an original complex max-min hybrid planning problem; providing a low-complexity iterative algorithm based on the block coordinate descent to solve the problem; and making a ground control station carry out power and channel distribution on the unmanned aerial vehicles through a remote control channel according to a maximum SINR value of each unmanned aerial vehicle, and completing the design of the method for distributionof the time-frequency resources of the unmanned aerial vehicles. The method for distribution of the time-frequency resources of the unmanned aerial vehicles based on the block coordinate descent hasthe beneficial effects that the reasonable distribution of the limited frequency spectrum resources is realized on the premise of improving the reliability of a control signal received by each unmanned aerial vehicle and ensuring the maximum consumption power constraint of the system.

Time-of-flight (TOF) clinical data collected during a PET scan are very sparse and have significant size. These data undergo TOF axial rebinning and azimuthal mashing if histogrammed data-based reconstruction algorithms are used. In a clinical environment, TOF compression is typically performed by the hardware rebinner. Normalization data, acquired on a regular basis and used for estimation of some norm components, are compressed by the hardware rebinner in a similar manner. This disclosure presents simple update iterative algorithms for crystal efficiencies norm component estimation from TOF compressed normalization data. Previously known methods are not directly applicable since the compression procedure significantly complicates normalization data model equations. The iterative algorithms presented herein have advantages of being easily adapted to any acquisition geometry, and of allowing estimation of parameters at crystal level when a number of crystals is relatively small. A monotonic sequential coordinate descent algorithm, which optimizes the Least Squares objective function, is presented. A simultaneous update algorithm, which possesses the advantage of easy parallelization, is also presented.

The invention discloses an SINR maximization method for an IRS-assisted dual-function radar communication system, and the method achieves the maximization of the SINR of an MIMO radar through the joint transmission beam forming and phase shift optimization technology under the condition of considering the existence of an eavesdropping target in an intelligent reflection surface (IRS)-assisted multiple-input-multiple-output (MIMO) radar. According to the method, an IRS-assisted dual-function radar communication (IDFRC) system considering a physical layer security (PLS) technology is established, a transmitting beam former, an artificial noise vector and the phase shift of an IRS are jointly optimized to maximize the SINR of an MIMO radar, and in order to solve the optimization problem non-convexity caused by the non-concavity of a security rate function, a block coordinate descent (BCD) algorithm is used to alternately update the transmit power and the phase shift of the IRS. Specifically, a controlled minimization (MM) algorithm is utilized to optimize a phase shift at a given transmit power, a first-order Taylor expansion is utilized to convert a problem into a convex optimization problem, and two transmit beamforming vectors are designed to detect a target and securely transmit information to a legal receiver. The method has the advantages that the complexity is low, the signal to interference plus noise ratio of the radar is remarkably improved, and the feasibility and the practicability are high.

The invention discloses an unmanned aerial vehicle auxiliary edge calculation unloading method based on terminal energy efficiency optimization, and the method comprises the steps: jointly optimizingthe task load of local calculation of a user terminal, the task load of calculation unloading and the track of an unmanned aerial vehicle through a global optimization algorithm based on block coordinate descent; the optimization effect on the energy consumption of the user terminal under different channel conditions is discussed, and finally, the unmanned aerial vehicle auxiliary edge calculationmodel taking the minimization of the energy consumption of the user terminal as a target and taking the task load and the time delay demand of the user and the energy of the unmanned aerial vehicle as constraints is constructed. According to the method, computing resources of a local side and an edge side are fully utilized, the energy efficiency of the user terminal is improved, the time delay of local calculation of the user terminal is considered, synchronous completion of local and edge side calculation in task time is ensured, and the effectiveness of the method is improved. In addition,effectiveness and reliability of the method under different channel conditions are verified, and the method has a wider practical application scene.

The invention discloses a distributed optimal power flow method based on continuous punishment dual decomposition for a power system. The distributed optimal power flow method comprises the following steps: firstly acquiring network parameters of the power system, and determining a mathematical model of the optimal power flow problem; secondly introducing an auxiliary variable to obtain an equivalent problem of the original optimal power flow control problem; then solving the equivalent problem through inner and outer iterative algorithms, wherein the inner iteration is used for carrying out distributed solution on a corresponding inner augmented lagrangian problem by using a block coordinate descent algorithm under the condition of a fixed dual variable, and each bus needs to conduct data interaction with a neighboring bus before the optimization of each group of variables to realize local optimization of a local variable, and the outer iteration is used for updating a dual variable and a punishment factor according to the current constraint feasibility index; finally, completing the optimal power flow control over the power system according to an obtained bus input power value. According to the distributed optimal power flow method based on the continuous punishment dual decomposition for the power system, the optimal power flow of the power system is subjected to distributed design by using a continuous punishment dual decomposition technology, so that the performance loss of the system is the least on the premise of guaranteeing the feasibility of a power flow equation.

A system includes an integrated circuit configured to communicating data in a channel. A channel matrix for the channel including a plurality of columns is received. A preprocessing step is performed, using a preprocessing unit, to compute a plurality of preprocessed column values corresponding to respective columns. An update step is performed, using an update unit, to update an estimation vector using a plurality of outer-loop iterations of an outer loop. Each outer-loop iteration updates the estimation vector using the plurality of preprocessed column values. An access link process is performed using the estimation vector.

The invention discloses a computing efficiency optimization method in a millimeter wave mobile edge computing system based on a reconfigurable intelligent surface, and the method comprises the steps: building a computing efficiency optimization model based on a maximum-minimum fairness criterion in a partial unloading mode; performing combined optimization on hybrid beam forming of a base station, passive beam forming of a reconfigurable intelligent surface, transmitting power of each user and frequency of a local central processing unit, and giving a calculation efficiency optimization algorithm based on penalty non-precision block coordinate descent to obtain the combined optimization method. The joint optimization method provided by the invention is effective.

The invention discloses a shilling attack resistance recommendation algorithm based on matrix completion. The algorithm comprises the following steps that: firstly, carrying out statistics on the scoring of a user for an item, extracting the attribute characteristics of the user and the attribute characteristics of the item, and independently constructing a user-item scoring matrix, a user attribute characteristic matrix and an item attribute characteristic matrix; then, modeling shilling attack scoring in the user-item scoring matrix into structured noise in a matrix completion model; then, adopting a blocking coordinate descent algorithm to carry out iterative update on each variable, and solving a structured line noise matrix; subsequently, according to the structured line noise matrix,rejecting the shilling attack scoring in the user-item scoring matrix; and finally, using a traditional recommendation algorithm to carry out scoring prediction, and solving a prediction scoring matrix. By use of the recommendation algorithm provided by the invention, the shilling attack user in a recommendation system can be effectively detected, a personalized scoring prediction effect which ismore accurate than a traditional recommendation algorithm can be obtained under shilling attack interference, and the robustness of the recommendation algorithm is effectively improved.

The present disclosure provides a new scalable coordinate descent (SCD) algorithm and associated system for generalized linear models whose convergence behavior is always the same, regardless of how much SCD is scaled out and regardless of the computing environment. This makes SCD highly robust and enables it to scale to massive datasets on low-cost commodity servers. According to one aspect, by using a natural partitioning of parameters into blocks, updates can be performed in parallel a block at a time without compromising convergence. Experimental results on a real advertising dataset are used to demonstrate SCD's cost effectiveness and scalability.

The invention provides an unmanned aerial vehicle communication anti-interference method based on three-dimensional trajectory power optimization. The method comprises the steps: constructing a channel model according to communication characteristics of the unmanned aerial vehicle, constructing constraint conditions of the unmanned aerial vehicle according to an actual application environment, and taking a weighted difference between an average throughput which can be realized by the unmanned aerial vehicle and an average interference area brought by the unmanned aerial vehicle as an original objective function, so that the effective anti-interference communication capability of the unmanned aerial vehicle is represented; and obtaining a good suboptimal solution of the flight path and the transmitting power of the unmanned aerial vehicle by maximizing the lower bound of an original objective function by using a block coordinate descent algorithm and a continuous convex approximation algorithm. According to the method, the balance between the anti-interference communication capability of the unmanned aerial vehicle and the interference generated by the unmanned aerial vehicle, which is not considered in the existing research, is considered, a reasonable mathematical model is designed to represent the balance, and finally, compared with independent track control or power control, the method provided by the invention has higher performance.

The invention discloses a digital media object classification method based on large margin distributed learning and aims to solve the problem that marking of classes of digital media causes noise. The form of the classification problem of the digital media objects is finally reduced into a convex secondary optimization problem by maximizing a margin average and minimizing a margin variance; two optimization algorithms which are based on dual coordinate descent and average stochastic gradient descent respectively are achieved according to whether or not to use a nonlinear kernel function and according to features of a training digital media object base; users can make selections on their own according to actual conditions; if the users select the nonlinear kernel function, the DCD (dual coordinate descent) is selected as the optimization algorithm for training; if the users select a linear kernel function, the training digital media object base provide many samples or has few features, the ASGD (average stochastic gradient descent) is selected as the optimization algorithm for training, and if not, the DCD is still selected as the optimization algorithm.

The invention discloses an automatic focusing method for decomposition projection. The method comprises the steps: obtaining original data in the whole synthetic aperture time, and dividing the original data into N sub-aperture data; performing focusing imaging processing on each sub-aperture data to obtain a first sub-image; registering the first sub-image in a ground virtual polar coordinate system to obtain registered image data; and performing error correction and sub-image fusion on the registered image data by adopting a fast coordinate descent method to obtain second sub-image data, andoutputting a focusing imaging result if the second sub-image data is a sub-image. According to the invention, the PGA algorithm is applied to the ground virtual polar coordinate system, so that the problem that a traditional frequency domain method is incompatible with a time domain method is solved, automatic focusing in the FFBP algorithm is achieved, and the focusing precision of the sub-aperture images is effectively improved; and the residual phase error model of the sub-images is established, the method is suitable for an unstable strip mode of a radar platform, the error estimation precision is improved, and the calculation complexity is reduced.

Optimal enhancement of 3G cellular network capacity utilizes two components of learning and optimization. First, a pair of learning approaches are used to model cellular network capacity measured in terms of total number of users carried and predict breakpoints of cellular towers as a function of network traffic loading. Then, an optimization problem is formulated to maximize network capacity subject to constraints of user quality and predicted breakpoints. Among a number of alternatives, a variant of simulated annealing referred to as Block Coordinated Descent Simulated Annealing (BCDSA) is presented to solve the problem. Performance measurements show that BCDSA algorithm offers dramatically improved algorithmic success rate and the best characteristics in utility, runtime, and confidence range measures compared to other solution alternatives. Accordingly, integrated iterative method, program, and system are described aiming at maximizing the capacity of 3G cellular networks by redistributing traffic from congested cellular towers to non-congested cellular towers.

The invention discloses a Rayleigh wave inversion method of employing simulated annealing improved on the basis of differential evolution and block coordinate descent. The method comprises the steps of firstly building an inversion objective function; optimizing a parameter model of the inversion objective function by using an differential evolution algorithm and obtaining best individuals; carrying out simulated annealing treatment on each individual by adopting a block coordinate descent algorithm; and taking the obtained new individuals as the individuals of the next generation population.According to the Rayleigh wave inversion method, the accuracy of simulated annealing inversion can be effectively improved and the controllability of simulated annealing on the accuracy is achieved through building the inversion objective function, optimizing the inversion objective function by adopting the differential evolution algorithm and dividing a multi-parameter problem into a local iterative optimization problem of multiple single parameters by adopting the block coordinate descent algorithm, thereby improving the stability of simulated annealing; and the Rayleigh wave inversion method can be applied to inversion of seismic data of actual shallow exploration, and the prediction accuracy of a near-surface underground structure is improved.

A method recovers an uncorrupted low-rank matrix, noise in corrupted data and a subspace from the data in a form of a high-dimensional matrix. An objective function minimizes the noise to solve for the low-rank matrix and the subspace without estimating the rank of the low-rank matrix. The method uses group sparsity and the subspace is orthogonal. Random subsampling of the data can recover subspace bases and their coefficients from a much smaller matrix to improve performance. Convergence efficiency can also be improved by applying an augmented Lagrange multiplier, and an alternating stepwise coordinate descent. The Lagrange function is solved by an alternating direction method.