185 results about "Unconstrained optimization" patented technology

A method for strategy independent optimization of a multi-objective function of a portfolio containing at least one investment is disclosed. The method involves the use of genetic algorithms to arrive at function optimization. A suite of strategies is provided enabling the user to select a strategy and optimize a function. Real world data is drawn from exchanges and is utilized for replication. The invention also discloses a novel combination of apparatus for carrying out the method of invention, typically, using parallel processing.

Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem. And, instead of solving the optimization problem via the KKT (Karush-Kuhn-Tucker) conditions and the Sherman-Morrison-Woodbury formula, embodiments of the invention use an iterative algorithm to solve an unconstrained optimization problem, which makes WPSVM suitable for classifying relatively high dimensional data.

A method determines a global optimal solution of a system defined by a plurality of nonlinear equations by applying a metaheuristic method to cluster a plurality of search instances into at least one group, selecting a center point and a plurality of top points from the search instances in each group and applying a local method, starting from the center point and top points for each group, to find a local optimal solution for each group in a tier-by-tier manner. Then a TRUST-TECH methodology is applied to each local optimal solution to find a set of tier-1 local optimal solutions, and the TRUST-TECH methodology is applied to each tier-1 local optimal solution to find a set of tier-2 local optimal solutions. A best solution is identified among all the local optimal solutions as the global optimal solution. The heuristic method can be a particle swarm optimization method or a genetic algorithm method.

The invention discloses a combined DOA and TOA single-station passive positioning method based on error correction. According to measurement information obtained by a receiving station of an externalradiation source radar system, by introducing an auxiliary variable, a nonlinear equation is converted into a pseudo-linear equation, a pseudo-linear model between the state of a target position and the state of an observation station position is established, an iterative least square method is adopted for estimation, a constrained total least square estimation model is constructed, a constrainedoptimization problem is converted into an unconstrained optimization problem, a Newton iteration method is used for optimization and solution, and the correlation between the auxiliary variable and the target position is used for constructing a correlation least square estimation model, so that the target positioning performance is further improved. The target positioning accuracy is improved through combined estimation of the target position and system errors. Convenience is provided for improving the target positioning performance, the complexity of error registration under the premise of ensuring the estimation performance is reduced, and good positioning performance in the case of large measurement noise is achieved.

An augmented Lagrangian genetic algorithm that may be used to generate solutions for optimization problems subject to linear, bound, and non-linear constraints is discussed. The augmented Lagrangian genetic algorithm uses an adaptive mutation operator to separately handle the linear, and bound constraints, and uses an augmented Lagrangian framework to handle non-linear constraints. The non-linear constraints are handled by creating a sub-problem without the linear and bound constraints and solving the sub-problem using Lagrange parameter estimates and a penalty factor. The exclusion of the linear constraints and boundary constraints from the sub-problem allows the sub-problem to be resolved in a more effective manner than is possible using conventional techniques.

The invention discloses a power distribution network planning double-layer optimization model construction method based on operation limitation, which comprises the following steps of: constructing anupper investment construction optimization model and a lower scheduling operation optimization model by taking input construction time and site selection constant volume of lines and energy storage as decision variables; adding constraint conditions into an objective function by using a Lagrangian multiplier, converting an original optimization problem containing equality and inequality constraints into an unconstrained optimization problem, and finally solving the problem by using a Kuhn-Tucker condition; screening a limited operation scene based on the shadow price, wherein the shadow pricerepresents the reduced scheduling cost of assuming that the resource is expanded by one unit; and carrying out decoupling processing on the upper investment construction optimization model and the lower scheduling operation optimization model.

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.

Determining adjustments to decrease vibrations caused by rotating blades from a plurality of rotors includes determining a first plurality of adjustment normalization values for a plurality of possible adjustments of blades of a first one of the plurality of rotors, where the first plurality of adjustment normalization values normalizes different types of adjustments having different units, determining a second plurality of adjustment normalization values for a plurality of possible adjustments of blades of a second one of the plurality of rotors, where the second plurality of adjustment normalization values normalizes different types of adjustments having different units and where, for a particular adjustment type, an adjustment normalization value of the first plurality of adjustment normalization values is independent of a corresponding adjustment normalization value of the second plurality of adjustment normalization values for the particular adjustment type, providing coefficient of vibration data that corresponds to effects on vibration caused by adjustments to the blades, receiving a plurality of vibration values corresponding to vibrations caused by the blades, applying the coefficient of vibration data and the first and second plurality of adjustment normalization values to the vibration values to provide a set of total normalized vibration values corresponding to sets of possible adjustments to be applied to the blades, and selecting a set of adjustments having a total normalized vibration value less than a predetermined value and corresponding to a lower total amount of adjustment than other sets of adjustments having a total normalized vibration value less than the predetermined value. The predetermined value may correspond to an expected value of all errors.

The invention discloses a migration sparse coding image classification method based on dictionary domain adaptation, belonging to the machine learning field. This method introduces dictionary alignment mechanism based on traditional sparse coding model and constructs feature transfer classification model to solve the problem of image cross-domain classification. When the samples in source domain and target domain obey different distributions, the traditional sparse-coded image classification algorithm can not learn from the samples in source domain to obtain the dictionary which can encode thesamples in target domain effectively, so the classification performance is degraded. At first, that dictionary alignment mechanism is introduce into a sparse coding model, then the constraint term ofthe dictionary are converted into an unconstrained optimization problem by L2 regularization, and the knowledge transfer performance of the model is improved by adopting an inter-domain dictionary approximation as a regularization term. The invention can effectively extract cross-domain image sparse feature representation and obtain higher classification accuracy.

Systems and methods are provided to engage in multi-tiered optimization where there may be a first multi-objective optimization and a second constraint optimization. The multi-objective optimization may be used to drive to one or more goals of the optimization problem. The constraint optimization or minimization may be used to drive towards a reduced and/or no constraint situation where the solution to the overall problem is feasible or near-feasible.

The invention relates to the field of laser-induced breakdown spectroscopy data pretreatment analysis. Specifically, with the method, based on a reasonable spectral peak mathematical model, with an unconstrained optimization algorithm, spectral peak related information after overlapped peak resolution is obtained by calculation. According to the method, based on a spectral peak mathematical model, and with an unconstrained optimization algorithm, spectral line related information after overlapped peak resolution is obtained by calculation, and overlapped spectral lines are separated. According to the invention, selective calculation can be carried out according to actual spectral line situations, and spectral line information can be determined with a small amount of parameters. Therefore, sample component analysis performance and accuracy are improved. Spectral peak position is visually selected, and initialization parameters needed by all optimization algorithms can be determined. The method also has the advantages of high calculation speed and easy implementation. With the method, a final processing result is selected by visual comparison, such that substance component quantitative analysis accuracy is improved.

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 discloses a rapid planar sparse array synthesis method. The method has the advantages that a conventional planar array synthesis constraint optimization model is converted into an unconstraint optimization problem by constructing a Lagrange function, so that calculation ill-posed problems in the iterative optimization process of a planar array are avoided, an array weight vector can be updated by the aid of closed-form solution in each time of iteration, planar array synthesis problems can be solved without an optimization tool, the method is more universal and transportability, an inverse matrix in the closed-form solution is quite large in scale as a two-dimensional space angle sample number of the planar array is increased in a quadratic manner, the inversion problem of the large-scale matrix is solved by introducing a conjugate gradient method, and convergence of planar array synthesis is accelerated, so that the method is higher in real-time performance and particularly applicable to occasions with high requirements on array optimization real-time performance and universality.

The invention provides an inversion method for the parameters of wires at special pole and tower positions of an electric transmission line based on a current magnetic field. The inversion method comprises a magnetic field sensor measuring and sampling part, line segmented inversion parts and a self-adaptive step-length adjusting part, wherein the magnetic field sensor measuring and sampling part,the line segmented inversion parts and the self-adaptive step-length adjusting part are implemented successively. The invention has the following beneficial effects: a magnetic field sensor is mounted on an unmanned aerial vehicle for measuring the magnetic field intensity data of each point in space during flight, and an unconstrained optimization algorithm is employed for segmented processing of the data; at the same time, since the situation that electric transmission lines stretch across poles, towers and corners extensively exists in reality, a self-adaptive manner is adopted for automatic adjustment of the segment length of data, so the algorithm is better intelligentized; and the positional information of the electric transmission wires obtained after inversion can be used for guiding the subsequent flight of the unmanned aerial vehicle along the line, so intelligent line inspection of the unmanned aerial vehicle can be realized.

The invention discloses a comprehensive energy system optimization method, which comprises the following steps of obtaining system parameters of a comprehensive energy system, and constructing a jointoptimization model based on two-stage random optimization by utilizing the system parameters, wherein the system parameters comprise equipment parameters and line parameters during planning and source load parameters during operation; deconstraining the constraint optimization problem in the joint optimization model to obtain a target joint optimization model of the unconstrained optimization problem; carrying out iterative solution on the target joint optimization model by utilizing an accelerated gradient descent algorithm to obtain system optimization parameters; and utilizing the system optimization parameters to optimize the planning and operation of the comprehensive energy system. According to the method, rapid, effective, real-time and dynamic planning optimization and operation optimization can be carried out on the comprehensive energy system. The invention further discloses a comprehensive energy system optimization device and equipment and a readable storage medium which have corresponding technical effects.

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.

The invention discloses a non-Gauss noise-against blind equalization method, aiming at providing the non-Gauss noise-against blind equalization method for wireless communication network, in particular between wireless sensor network nodes, in order to guarantee corrective data transmission between wireless sensor nodes. The maximal signal-to-noise energy ratio of an output signal of an equalizer is utilized as a starting point to construct a new cost function. Based on a method for converting the form of constraint condition, an alternative constraint condition is found, the constraint optimization problem is converted into a non-constraint optimization problem, secondary [epsilon]-insensitive loss functions are adopted to construct a solving method conforming to iterative reweighted least squares method, resulting in a global optimal solution for the cost function. The method can lower cell consumption of the wireless sensor network nodes and takes the requirement on small number of data as well as rapid convergence into consideration in the aspect of algorithm design. The algorithm is suitable for the problem of blind equalization for low-order and high-order quadrature amplitude modulation QAM and PSK signals. Drawings attached represent a model for signal transmission between the wireless sensor network nodes according to the invention.

The invention discloses a low-speed impact locating method for a composite material cell structure, and belongs to the field of composite material structure engineering structure health monitoring. According to the method, an unconstrained optimization algorithm based on a Newton method is applied to solving the low-speed impact recognition problem of the composite material cell structure, and the low-speed impact fast recognition of the structure is achieved. Based on a sensor array technology, impact induction is applied to the structure to generate Lamb wave signals, and impact response signals of the composite material cell structure are collected. A certain sensor is used as the criterion, the wavelet analysis means is adopted, and the time differences of Lamb waves reaching the other sensors are extracted. The unconstrained optimization algorithm based on the Newton method is adopted to solve and locate a system of nonlinear equations, and therefore the position of an impact source is obtained. The impact source of the composite material cell structure can be recognized fast and effectively, recognition precision is high, and good engineering application value is achieved.

A method and system for generated an optimized placement of orthodontic brackets on a set of teeth with the goal of smoothing the orthodontic archwire associated with the brackets. The optimized placement takes into account at least one constraint, such as minimum height of the brackets or minimum distance from the center of the tooth. Generating the optimized placement may take the form of iteratively re-positioned the brackets on the underlying teeth to derive the most optimal placement within the bounds of the given constraints.

Described is a system for position estimation. A set of raw sensor outputs are acquired from a sensor of a platform. The set of raw sensor outputs are stored in non-transitory memory. A set of optimized sensor measurements is generated by deducing errors in the raw sensor outputs using an unconstrained optimization algorithm. The system determines a position of the platform based on the set of optimized sensor measurements.

The invention discloses a near-earth satellite redundant flywheel angular momentum autonomous management method, including the steps of S1. dividing given angular momentum increments of a flywheel group into an angular momentum increment of attitude control output by each flywheel in the flywheel group and an angular momentum increment of zero motion; S2. obtaining the central angular momentum of the flywheels; S3. converting a problem of solving flywheel group zero movement into a constrained quadratic form optimization problem, adopting QR decomposition to convert the constrained quadratic form optimization problem into an unconstrained optimization problem, and obtaining a zero movement angular momentum increment of each flywheel, so that the angular momentum of each flywheel approaches the central angular momentum; and S4. according to a relation between flywheel group null space after part of flywheels fail and flywheel group null space when no flywheel fails, completing angular momentum reconstruction of the flywheel group. The advantages of the method are that while the flywheel group generates given moment of force, the rotating speed of each flywheel can approach preset desired rotating speed, thereby preventing frequent zero passage or saturation of the rotating speed of flywheels and realizing on-line reconstruction of a whole system when part of the flywheels fail.