237 results about "Constrained optimization problem" patented technology

Application of network flow techniques to constrained optimization problems is disclosed. The present of constrains may lead to infeasible solutions. The infeasible solutions can be removed by an iterative process of changing the structure of the network and/or the associated parameters. Specific applications of the invention to the placement of tristate buffers and clocks in integrated circuits are disclosed.

A system and method for projection number and radiation dose reduction in tomographic imaging that creates a three dimensional cross sectional image of an object by the reconstruction of its projections. Images of a superior quality can be obtained with a fewer number projections than seen with conventional methods by reconstruction of projections that have been pre-processed and preferably placed in the Fourier domain with a Fractional Fourier Transform (FrFT) or forward Pseudo-polar Fast Fourier transform (PPFFT). The projections are iteratively refined through formulation of a constrained optimization problem with constraints in object space and Fourier space preferably solved with a gradient descent algorithm incorporating a Bregman iterative regularization or a continuative regularization. By using an exact Fourier-based iterative algorithm as well as physical and mathematical constraints, convergence to a lowest-possible noise state that is also strictly consistent with the measured data can be obtained.

What is disclosed is a system and method for specifying a custom document as a multi-criteria constraint optimization problem, and a method to automatically create the specified document using existing constraint optimization solving algorithms. The present method specifies the document, its content components, its layout requirements, and its desired aesthetic criteria are expressed as elements of a constraint optimization problem which when solved, results in an automated document layout for the set of content components that satisfies not only certain primitive content and layout constraints, but which also advantageously fulfills desired design properties and which provides a way to ensure that the generated document is well designed. The method for automatic document layout comprises the steps of determining a set of variables that can be adjusted to achieve a satisfactory layout; expressing said satisfactory layout as a set of constraints on said determined set of variables wherein at least one of said set of constraints is expressed as being optimizable; and solving said constraints to find a layout which solves for the variables over the constraints. The system for automatic document layout on multi-function office equipment comprises means for document layout constraint acquisition; means for document layout variable specification which specifies a set of variables that can be adjusted to achieve a satisfactory layout; means for relationship-constraint to optimization-constraint conversion which expresses said satisfactory layout as a set of constraints on said set of variables wherein at least one of said set of constraints is expressed as being optimizable; and means for constraint optimization for solving said constraints to find a layout which solves for the variables over said constraints.

The invention relates to the field of computer vision. In order to propose a tensor structural missing filling method and realize the accurate filling of the structural missing tensor, the tensor structural missing filling method based on the combination of low rank and sparse representation and the TT low rank tensor filling theory are introduced into the TT low rank prior to constrain the potential tensor. At the same time, considering that the fiber signal along each dimension of the tensor can be sparsely represented by a dictionary, and the missing fiber of the previous dimension can be recovered by sparse constraint on the fiber signal in the next dimension, sparse constraint is introduced for each dimension of the fiber signal; based on the sparse apriori of the combined TT low rankand each dimension, the tensor filling problem with structural defects is formulated as a constrained optimization problem, so that the tensor filling problem with structural defects can be realized.The invention is mainly applied to video image inpainting, recommendation system, data mining and multi-classification learning occasions.

The invention discloses a robust mechanism research method of characteristic significance in image quality evaluation. The robust mechanism research method comprises the following steps: firstly, determining a target function of characteristic selection in the image quality evaluation, and initializing a model parameter; secondly, adding an optimal characteristic into a characteristic matrix, and removing a characteristic disturbance term; thirdly, calculating the significance of the characteristic selection in an image quality evaluation system; fourthly, judging whether the significance meets a system robust requirement or achieves an upper limit of a characteristic number; and finally, verifying a model classification effect. The characteristic significance is measured through an imported system characteristic signal to noise ratio, a constrained optimization problem of a smooth convex function in the image quality evaluation system is solved, interference on a classification face by non-significant characteristics is effectively lowered, the robustness of the image evaluation system is improved, and the self-adaptive optimization problem of characteristic attribute selection on the basis of an image quality evaluation network of a learning mechanism is solved.

The invention relates to an adaptive joint computing unloading and resource allocation method in Internet of Vehicles, and belongs to the field of Internet of Vehicles edge computing. The method comprises the step of considering the concurrent unloading condition of a plurality of vehicles under the combination of a local computing platform, an MEC server, a far-end cloud server and an idle vehicle multi-computing platform; comprehensively considering the size of a vehicle task, the maximum tolerance time delay, a computing resource under a RSU and a network bandwidth factor, and unloading thecomputing task of the vehicle to a plurality of computing platforms; according to the task number under the RSU, automatically adjusting an unloading platform and an unloading proportion of the vehicle computing task, and distributing computing resources of the MEC while the vehicle obtains the optimal unloading proportion; modeling the calculation unloading decision and the resource allocation into a multi-constraint optimization problem; utilizing a compressed particle swarm optimization algorithm, and proposing a particle matrix coding mode to jointly optimize unloading decision and resource allocation. Experimental results show that the method can minimize the total cost of the system while meeting the maximum tolerance time delay.

The invention discloses a real beam scanning radar front-view super-resolution imaging method, which belongs to a method for realizing scanning radar front-view super-resolution imaging by using the total variation functional of the scattering coefficient of an imaging region as de-convolution of priori information. A scanning radar azimuth echo signal is modeled into a result obtained by superimposing noise onto the convolution of a sampling sequence of antenna beam along the azimuth and a sampling sequence of a target reflectance distribution function along the azimuth, and a problem of scanning radar front-view super-resolution imaging is converted into a de-convolution problem; then, the total variation functional of the scattering coefficient of an imaging region is integrated into priori information of the de-convolution problem, and the de-convolution problem is converted into a constrained optimization problem; and finally, a global optimal solution to the constrained optimization problem is obtained through a cross iteration method, and scanning radar front-view super-resolution imaging is realized.

The invention puts forward a finite-element reconstruction method for space weighting of auto-fluorescence imaging. The method uses a diffuse optical tomography (DOT) technique to precisely reconstruct optical parameters of main biological tissue. The method limits a possible real light-source region, and divides a whole reconstruction region into a light-source possible region and a light-source impossible region so as to improve the numerical stability and effectiveness of reconstruction problems and reduce the ill-conditioned property of BLT reconstruction problems. In order to avoid famous 'Inverse Crime' problems, the method adopts a Monte Carlo (MC) random method to simulate the process of transmitting light in the biological tissue. Then the invention puts forward a finite-element method based on a space-weighting unit and adopts a tolerant algorithm for linear constrained optimization problems to reconstruct light-source information.

The invention relates to an AOA-based two-dimensional wireless sensor network semi-definite programming positioning method comprising the following steps: assuming that there is no obstruction between a reference node and a target node to be positioned in a wireless sensor network, and measuring the angle of arrival of each signal; converting the angles of arrival of the signals into distance information between the nodes; converting a problem of wireless sensor network target node positioning into a mathematical optimization problem of maximum likelihood estimate MLE and solving the problem, and providing an optimized objective function for subsequent steps; introducing redundant variables to convert the mathematical optimization problem of maximum likelihood estimate MLE into a constrained optimization problem; converting the obtained constrained optimization problem into a semi-definite programming SDP convex optimization problem using a mini-max criterion and a semi-definite relaxation SDR method, and getting an optimal solution, thus completing positioning of the target node. Through the method, the precision of target node positioning is improved.

A computational device implemented method utilizes a genetic algorithm and modifies the offspring of the genetic algorithm that fall outside of the feasible search space after crossover so that the offspring will be within the feasible search space. To place the offspring in the feasible search space, NFC and HSQPC mechanisms are used.

The invention discloses a mathematical model for optimal configuration of power distribution network filtering devices. Under the condition that harmonic voltage, condenser capacity and the like meet constraint conditions, in terms of system average voltage total distortion and investment cost, a comprehensive objective function is given in a linear weighting manner, a penalty function is added to the objective function so that a constrained optimization problem is converted into a unconstrained optimization problem, and an improved simulated annealing-particle swarm optimization (namely introduction of an adaptive inertia coefficient and a memorizer) is used for solving. The model can be adapted to the random variation of a harmonic source and network parameters, can ensure that the harmonicration and average voltage total harmonic distortion of each node of the network are in the specified limits, can optimize the installation type, installation location, installation quantity, capacity parameter and the like of active filters and passive filters in a centralized manner in the whole network; and by the mathematical model, the investment cost of filtering devices in the whole network is reduced to the minimum.

The invention discloses a method for optimally designing graph filter banks on the basis of a two-step process. The method includes converting requirements on reconstruction characteristics and frequency characteristics of the filter banks into functions related to coefficients of prototype filters; converting design problems of the graph filter banks into a constrained optimization problem according to design requirements; ultimately solving the optimal coefficients of the prototype filters by the aid of the two-step process. The method has the advantages that optimization ideas are introduced into designs of the graph filter banks for the first time, and the reconstruction characteristics and the frequency characteristics of the graph filter banks are taken into consideration by the aid of factor regulating modes; the reconstruction characteristics and the frequency characteristics of the filter banks can be taken into consideration by optimization paths as compared with existing methods on the basis of polynomial decomposition; compromising effects can be realized between the reconstruction characteristics and the frequency characteristics by means of regulating parameters epsilon r under the condition of given lengths of graph filters.

In various embodiments, a training application generates a preference prediction model based on an interaction matrix and a closed-form solution for minimizing a Lagrangian. The interaction matrix reflects interactions between users and items, and the Lagrangian is formed based on a constrained optimization problem associated with the interaction matrix. A service application generates a first application interface that is to be presented to the user. The service application computes predicted score(s) using the preference prediction model, where each predicted score predicts a preference of the user for a different item. The service application then determines a first item from the items to present to the user via an interface element included in the application interface. Subsequently, the service application causes a representation of the first item to be displayed via the interface element included in the application interface.

The solution of a constrained optimization problem is easily solved by linearly approximating a point of interest to a first hypersurface given as a constraint and finding the extreme value of the objective function by moving a point r along a curved line having second-order osculation with the geodetic line of a hypersurface S(r)=c and passing through the point r.

The invention discloses a power allocation algorithm in a multi-beam satellite communication system. The power allocation algorithm comprises the following steps: at first, creating a downlink capacity model of the multi-beam satellite communication system for a user terminal according to a power bandwidth balancing principle in the multi-beam satellite communication system; then, creating a constrained optimization problem model for the power allocation problem; and finally, solving through the Lagrangian duality theory and a subgradient algorithm. The power allocation algorithm provided by the invention has excellence on the aspects of total capacity of the system and the total refusal amount of two-order services, good system performance can be obtained, and valuable power resources can be effectively utilized.

The invention discloses a CTLS-based external radiation source radar net bistatic distance error registration method. First, according to bistatic information obtained by an external radiation sourceradar, an intermediate variable is introduced, a bistatic distance nonlinear equation is converted into a pseudo linear equation so as to be suitable for a least square method, and then a recursive least square method is utilized to perform estimation. Then structured measurement noise in the bistatic nonlinear equation is extracted, and a CTLS estimation model is built. The abovementioned constrained optimization problem is converted into an unconstrained optimization problem and a Newton iteration method is utilized to perform optimization solution. On the basis of a CTLS estimation result,relevance of solved vectors is utilized to further optimize an estimation error. The method provided by the invention fully considers the problem of architecture of originally independent noise afterequation pseudo linear processing through a CTLS method, thus the estimation result is more accurate, and good performance can be achieved when noise is even bigger.

The invention relates to an optimization method in the field of electric systems, in particular to an optimization method of extra-high voltage transient stability control. The optimization method of the extra-high voltage transient stability control comprises the following steps: (1) setting up a power grid coordination control optimization model, (2) converting a constrained optimization problem of the power grid coordination control optimization model to an unconstrained optimization problem, (3) solving the unconstrained optimization problem by adopting an optimization method of a particle swarm with wavelet mutation, and (4) confirming a globally optimal solution in the particle swarm. The optimization method of the extra-high voltage transient stability control facilitates that electric power system planners and analysts grasp transient stability emergency control, and provides the basis for confirmation of emergency safety control measures of ultra-high voltage grids. The optimization method of the extra-high voltage transient stability control has the advantages of increasing wavelet mutation operation, improving global convergence characteristics, being capable of processing optimization problems of thousands of nodes, conveniently combining parallel computing techniques, and further improving computation speed by using large-scale computers.

Disclosed are embodiments of a method and an associated computer program product for finding the statistical bounds, the corresponding parameter corners and the probability density function of one or more performance targets for a circuit without requiring Monte Carlo simulation runs. To accomplish this, a joint probability density function for independent parameters that affect the performance target can be constructed. Then, based on the joint probability density function, the statistical bounds of the performance target can be found by constructing an equal-probability-density surface of the joint probability density function and solving a constrained optimization problem on that equal-probability-density surface. Once the statistical bounds are determined, the corresponding parameter corners for the performance target can also be determined. After obtaining multiple statistical bounds corresponding to different accumulated probability density, the probability density function of the performance target can also be obtained.

An encoder generates an estimate of apparent inter-image motion utilizing a dense motion field in a sequence of video frames or other images. The estimate in an illustrative embodiment includes a motion vector for each pixel of the dense motion field. The estimate is generated at least in part as a solution to a constrained optimization problem based on a multiscale data model characterizing the motion between pixels of the dense motion field and designated neighbor pixels. The estimate is used for motion compensation in encoding the corresponding images, and is itself encoded for transmission to a decoder for use in decoding the encoded images. In the illustrative embodiment, the multiscale data model characterizes spatial, temporal and scale coherence properties of the motion as a Markov random field (MRF), thereby ensuring that the resulting motion vectors can be efficiently coded for transmission to the decoder.

A disclosed radio communication apparatus includes a preceding unit, a optimum transmit power calculation unit and a transmission unit. The optimum transmit power calculation unit receives the transmit weight related information from the preceding unit and calculates a transmit power level to optimize an objective function in accordance with an interior point method. The objective function results from addition of a barrier function adjusted with a barrier parameter to an original objective function associated with a constrained optimization problem. The original objective function includes a function of respective channel capacities for individual users derived from the transmit weight related information. The optimum transmit power calculation unit adaptively updates a logarithm of the barrier parameter and uses a gradient obtained through a high-order derivative with the logarithm of the barrier parameter to calculate the transmit power level to optimize the resulting objective function.