This paper puts forward a two-parameter family of nonlinear conjugate gradient(CG)method without line search for solving unconstrained optimization problem.The main feature of this method is that it does not rely on a...This paper puts forward a two-parameter family of nonlinear conjugate gradient(CG)method without line search for solving unconstrained optimization problem.The main feature of this method is that it does not rely on any line search and only requires a simple step size formula to always generate a sufficient descent direction.Under certain assumptions,the proposed method is proved to possess global convergence.Finally,our method is compared with other potential methods.A large number of numerical experiments show that our method is more competitive and effective.展开更多
As a generalization of the two-term conjugate gradient method(CGM),the spectral CGM is one of the effective methods for solving unconstrained optimization.In this paper,we enhance the JJSL conjugate parameter,initiall...As a generalization of the two-term conjugate gradient method(CGM),the spectral CGM is one of the effective methods for solving unconstrained optimization.In this paper,we enhance the JJSL conjugate parameter,initially proposed by Jiang et al.(Computational and Applied Mathematics,2021,40:174),through the utilization of a convex combination technique.And this improvement allows for an adaptive search direction by integrating a newly constructed spectral gradient-type restart strategy.Then,we develop a new spectral CGM by employing an inexact line search to determine the step size.With the application of the weak Wolfe line search,we establish the sufficient descent property of the proposed search direction.Moreover,under general assumptions,including the employment of the strong Wolfe line search for step size calculation,we demonstrate the global convergence of our new algorithm.Finally,the given unconstrained optimization test results show that the new algorithm is effective.展开更多
Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stocha...Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.展开更多
In this paper, the optimal control problem of parabolic integro-differential equations is solved by gradient recovery based two-grid finite element method. Piecewise linear functions are used to approximate state and ...In this paper, the optimal control problem of parabolic integro-differential equations is solved by gradient recovery based two-grid finite element method. Piecewise linear functions are used to approximate state and co-state variables, and piecewise constant function is used to approximate control variables. Generally, the optimal conditions for the problem are solved iteratively until the control variable reaches error tolerance. In order to calculate all the variables individually and parallelly, we introduce a gradient recovery based two-grid method. First, we solve the small scaled optimal control problem on coarse grids. Next, we use the gradient recovery technique to recover the gradients of state and co-state variables. Finally, using the recovered variables, we solve the large scaled optimal control problem for all variables independently. Moreover, we estimate priori error for the proposed scheme, and use an example to validate the theoretical results.展开更多
In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradien...In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.展开更多
Cadmium telluride(CdTe),which has a high average atomic number and a unique band structure,is a leading material for room-temperature X/γ-ray detectors.Resistivity and mobility are the two most important properties o...Cadmium telluride(CdTe),which has a high average atomic number and a unique band structure,is a leading material for room-temperature X/γ-ray detectors.Resistivity and mobility are the two most important properties of detector-grade CdTe single crystals.However,despite decades of research,the fabrication of high-resistivity and high-mobility CdTe single crystals faces persistent challenges,primarily because the stoichiometric composition cannot be well controlled owing to the high volatility of Cd under high-temperature conditions.This volatility introduces Te inclusions and cadmium vacancies(V_(Cd))into the as-grown CdTe ingot,which significantly degrades the device performance.In this study,we successfully obtained detector-grade CdTe single crystals by simultaneously employing a Cd reservoir and chlorine(Cl)dopants via a vertical gradient freeze(VGF)method.By installing a Cd reservoir,we can maintain the Cd pressure under the crystal growth conditions,thereby preventing the accumulation of Te in the CdTe ingot.Additionally,the existence of the Cl dopant helps improve the CdTe resistivity by minimizing V_(Cd)density through the formation of an acceptor complex(Cl_(Te)-V_(Cd))^(-1).The crystalline quality of the obtained CdTe(Cl)was evidenced by a reduction in large Te inclusions,high optical transmission(60%),and a sharp absorption edge(1.456 eV).The presence of substitutional Cl dopants,known as Cl_(Te)^(+),simultaneously supports the record high resistivity of 1.5×10^(10)Ω·cm and remarkable electron mobility of 1075±88 cm^(2)V^(-1)s^(-1)simultaneously,has been confirmed by photoluminescence spectroscopy.Moreover,using our crystals,we fabricated a planar detector withμτ_(e)of(1.11±0.04)×10^(-4)cm^(2)∕V,which performed with a decent radiation-detection feature.This study demonstrates that the vapor-pressure-controlled VGF method is a viable technical route for fabricating detector-grade CdTe crystals.展开更多
This study aims to discuss the propagation characteristics of seismic Lamb wave and surface wave in a new radial gradient supercell seismic metamaterial(RGSSM).Different from the traditional seismic metamaterials with...This study aims to discuss the propagation characteristics of seismic Lamb wave and surface wave in a new radial gradient supercell seismic metamaterial(RGSSM).Different from the traditional seismic metamaterials with simple unit cells,the RGSSM consists of the supercells arranged periodically along the radial direction,and these supercells are composed of five kinds of unit cells with gradient filling rates.The dispersion curve and attenuation spectrum of the Lamb wave in RGSSM are studied with the finite element method combined with the supercell technology,generating a very low-frequency ultra-wide bandgap of 3.98-20 Hz,and a forbidden band generated by the localized modes forms multi-harmonic oscillators inside the supercell.Furthermore,it is found that the Young’s modulus of the soil is more sensitive to the effect of bandgap.In terms of surface wave,the RGSSM can produce rapid attenuation in the low frequency range of 5.2-8.5 Hz.Finally,a 3D model is designed to demonstrate the shielding performance of RGSSM against the seismic surface wave.The proposed RGSSM provides a new idea for the seismic isolation of ultra-wide and low-frequency seismic waves.展开更多
The quasi-rectangular tunnel represents a novel cross-section design,intended to supersede the traditional circular and rectangular tunnel formats.Due to the limited capacity of the tunnel vault to withstand vertical ...The quasi-rectangular tunnel represents a novel cross-section design,intended to supersede the traditional circular and rectangular tunnel formats.Due to the limited capacity of the tunnel vault to withstand vertical loads,an interior column is often installed at the center to enhance its load-bearing capacity.This study aims to develop a hyperstatic reaction method(HRM)for the analysis of deformation and structural integrity in this specific tunnel type.The computational model is validated through comparison with the corresponding finite element method(FEM)analysis.Following comprehensive validation,an ensemble machine learning(ML)model is proposed,using numerical benchmark data,to facilitate real-time design and optimization.Subsequently,three widely used ensemble models,i.e.random forest(RF),gradient boosting decision tree(GBDT),and extreme gradient boosting(XGBoost)are compared to identify the most efficient ML model for replacing the HRM model in the design optimization process.The performance metrics,such as the coefficient of determination R2 of about 0.999 and the mean absolute percentage error(MAPE)of about 1%,indicate that XGBoost outperforms the others,exhibiting excellent agreement with the HRM analysis.Additionally,the model demonstrates high computational efficiency,with prediction times measured in seconds.Finally,the HRM-XGBoost model is integrated with the well-known particle swarm optimization(PSO)for the real-time design optimization of quasi-rectangular tunnels,both with and without the interior column.A feature importance assessment is conducted to evaluate the sensitivity of design input features,enabling the selection of the most critical features for the optimization task.展开更多
In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is als...In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.展开更多
The conventional gravity gradient method to plot the geologic body location is fuzzy. When the depth is large and the geologic body is small, the Vzz and Vzx derivative errors are also large. We describe that using th...The conventional gravity gradient method to plot the geologic body location is fuzzy. When the depth is large and the geologic body is small, the Vzz and Vzx derivative errors are also large. We describe that using the status distinguishing factor to optimally determine the comer location is more accurate than the conventional higher-order derivative method. Thus, a better small geologic body and fault resolution is obtained by using the gravity gradient method and trial theoretical model calculation. The actual data is better processed, providing a better basis for prospecting and determination of subsurface geologic structure.展开更多
In this paper, we present a new hybrid conjugate gradient algorithm for unconstrained optimization. This method is a convex combination of Liu-Storey conjugate gradient method and Fletcher-Reeves conjugate gradient me...In this paper, we present a new hybrid conjugate gradient algorithm for unconstrained optimization. This method is a convex combination of Liu-Storey conjugate gradient method and Fletcher-Reeves conjugate gradient method. We also prove that the search direction of any hybrid conjugate gradient method, which is a convex combination of two conjugate gradient methods, satisfies the famous D-L conjugacy condition and in the same time accords with the Newton direction with the suitable condition. Furthermore, this property doesn't depend on any line search. Next, we also prove that, moduling the value of the parameter t,the Newton direction condition is equivalent to Dai-Liao conjugacy condition.The strong Wolfe line search conditions are used.The global convergence of this new method is proved.Numerical comparisons show that the present hybrid conjugate gradient algorithm is the efficient one.展开更多
In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Comb...In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Combining the quasi-Newton method with the new method, the former is modified to have global convergence property. Numerical results show that the new algorithm is efficient.展开更多
Fast solving large-scale linear equations in the finite element analysis is a classical subject in computational mechanics. It is a key technique in computer aided engineering （CAE） and computer aided manufacturing ...Fast solving large-scale linear equations in the finite element analysis is a classical subject in computational mechanics. It is a key technique in computer aided engineering （CAE） and computer aided manufacturing （CAM）. This paper presents a high-efficiency improved symmetric successive over-relaxation （ISSOR） preconditioned conjugate gradient （PCG） method, which maintains lelism consistent with the original form. Ideally, the by 50% as compared with the original algorithm. the convergence and inherent paralcomputation can It is suitable for be reduced nearly high-performance computing with its inherent basic high-efficiency operations. By comparing with the numerical results, it is shown that the proposed method has the best performance.展开更多
Gradiently denitrated gun propellant(GDGP)prepared by a“gradient denitration”strategy is obviously superior in progressive burning performance to the traditional deterred gun propellant.Currently,the preparation of ...Gradiently denitrated gun propellant(GDGP)prepared by a“gradient denitration”strategy is obviously superior in progressive burning performance to the traditional deterred gun propellant.Currently,the preparation of GDGP employed a tedious two-step method involving organic solvents,which hinders the large-scale preparation of GDGP.In this paper,GDGP was successfully prepared via a novelty and environmentally friendly one-step method.The obtained samples were characterized by FT-IR,Raman,SEM and XPS.The results showed that the content of nitrate groups gradiently increased from the surface to the core in the surface layer of GDGP and the surface layer of GDGP exhibited a higher compaction than that of raw gun propellant,with a well-preserved nitrocellulose structure.The denitration process enabled the propellant surface with regressive energy density and good progressive burning performance,as confirmed by oxygen bomb and closed bomb test.At the same time,the effects of different solvents on the component loss of propellant were compared.The result showed that water caused the least component loss.Finally,the stability of GDGP was confirmed by methyl-violet test.This work not only provided environmentally friendly,simple and economic preparation of GDGP,but also confirmed the stability of GDGP prepared by this method.展开更多
Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to de...Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to decrease the magnitude of network weights. In this paper, some weight boundedness and deterministic con- vergence theorems are proved for the online gradient method with penalty for BP neural network with a hidden layer, assuming that the training samples are supplied with the network in a fixed order within each epoch. The monotonicity of the error function with penalty is also guaranteed in the training iteration. Simulation results for a 3-bits parity problem are presented to support our theoretical results.展开更多
The aim of the study was to prepare berberine hydrochloride long-circulating liposomes and optimize the formulation and process parameters,and investigate the influence of different factors on the encapsulation effici...The aim of the study was to prepare berberine hydrochloride long-circulating liposomes and optimize the formulation and process parameters,and investigate the influence of different factors on the encapsulation efficiency.Berberine hydrochloride liposomes were prepared in response to a transmembrane ion gradient that was established by ionophore A23187.Free and liposomal drug were separated by cation exchange resin,and then the amount of intraliposomal berberine hydrochloride was determined by UV spectrophotometry.The optimized encapsulation efficiency of berberine hydrochloride liposomes was 94.3%2.1%when the drug-to-lipid ratio was 1:20,and the mean diameter was 146.9 nm3.2 nm.As a result,the ionophore A23187-mediated ZnSO_(4)gradient method was suitable for the preparation of berberine hydrochloride liposomes that we could get the desired encapsulation efficiency and drug loading.展开更多
With the development of computational power, there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image, such as full-waveform inversion a...With the development of computational power, there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image, such as full-waveform inversion and least squares migration. However, though more advanced than conventional methods, these data fitting methods can be very expensive in terms of computational cost. Recently, various techniques to optimize these data-fitting seismic inversion problems have been implemented to cater for the industrial need for much improved efficiency. In this study, we propose a general stochastic conjugate gradient method for these data-fitting related inverse problems. We first prescribe the basic theory of our method and then give synthetic examples. Our numerical experiments illustrate the potential of this method for large-size seismic inversion application.展开更多
Let C be a nonempty closed convex subset of a 2-uniformly convex and uniformly smooth Banach space E and {An}n∈N be a family of monotone and Lipschitz continuos mappings of C into E＊. In this article, we consider th...Let C be a nonempty closed convex subset of a 2-uniformly convex and uniformly smooth Banach space E and {An}n∈N be a family of monotone and Lipschitz continuos mappings of C into E＊. In this article, we consider the improved gradient method by the hybrid method in mathematical programming [i0] for solving the variational inequality problem for {AN} and prove strong convergence theorems. And we get several results which improve the well-known results in a real 2-uniformly convex and uniformly smooth Banach space and a real Hilbert space.展开更多
This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better.Then an improved double-threshold...This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better.Then an improved double-threshold method is proposed,which is combined with the method of maximum classes variance,estimating-area method and double-threshold method.This method can automatically select two different thresholds to segment gradient images.The computer simulation is performed on the traditional methods and this algorithm and proves that this method can get satisfying result.展开更多
A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysi...A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysis, it is shown that search directions of the proposed method satisfy the sufficient descent condition, independent of the line search and the objective function convexity. Global convergence of the method is established under an Armijo–type line search condition. Numerical experiments show practical efficiency of the proposed method.展开更多
基金Supported by 2023 Inner Mongolia University of Finance and Economics,General Scientific Research for Universities directly under Inner Mon‐golia,China (NCYWT23026)2024 High-quality Research Achievements Cultivation Fund Project of Inner Mongolia University of Finance and Economics,China (GZCG2479)。
文摘This paper puts forward a two-parameter family of nonlinear conjugate gradient(CG)method without line search for solving unconstrained optimization problem.The main feature of this method is that it does not rely on any line search and only requires a simple step size formula to always generate a sufficient descent direction.Under certain assumptions,the proposed method is proved to possess global convergence.Finally,our method is compared with other potential methods.A large number of numerical experiments show that our method is more competitive and effective.
基金supported by the National Natural Science Foundation of China(No.72071202)the Key Laboratory of Mathematics and Engineering Applications,Ministry of Education。
文摘As a generalization of the two-term conjugate gradient method(CGM),the spectral CGM is one of the effective methods for solving unconstrained optimization.In this paper,we enhance the JJSL conjugate parameter,initially proposed by Jiang et al.(Computational and Applied Mathematics,2021,40:174),through the utilization of a convex combination technique.And this improvement allows for an adaptive search direction by integrating a newly constructed spectral gradient-type restart strategy.Then,we develop a new spectral CGM by employing an inexact line search to determine the step size.With the application of the weak Wolfe line search,we establish the sufficient descent property of the proposed search direction.Moreover,under general assumptions,including the employment of the strong Wolfe line search for step size calculation,we demonstrate the global convergence of our new algorithm.Finally,the given unconstrained optimization test results show that the new algorithm is effective.
文摘Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.
文摘In this paper, the optimal control problem of parabolic integro-differential equations is solved by gradient recovery based two-grid finite element method. Piecewise linear functions are used to approximate state and co-state variables, and piecewise constant function is used to approximate control variables. Generally, the optimal conditions for the problem are solved iteratively until the control variable reaches error tolerance. In order to calculate all the variables individually and parallelly, we introduce a gradient recovery based two-grid method. First, we solve the small scaled optimal control problem on coarse grids. Next, we use the gradient recovery technique to recover the gradients of state and co-state variables. Finally, using the recovered variables, we solve the large scaled optimal control problem for all variables independently. Moreover, we estimate priori error for the proposed scheme, and use an example to validate the theoretical results.
基金Supported by the Science and Technology Project of Guangxi(Guike AD23023002)。
文摘In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.
基金supported by the National Key R&D Program(Nos.2023YFE0108500 and 2023YFF0719500)the National Natural Science Foundation of China(Nos.52072300 and 52302199)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2022A1515110538)Key Research and Development Program of Shaanxi(No.2023-GHZD-48)the Fundamental Research Funds for the Central Universities.
文摘Cadmium telluride(CdTe),which has a high average atomic number and a unique band structure,is a leading material for room-temperature X/γ-ray detectors.Resistivity and mobility are the two most important properties of detector-grade CdTe single crystals.However,despite decades of research,the fabrication of high-resistivity and high-mobility CdTe single crystals faces persistent challenges,primarily because the stoichiometric composition cannot be well controlled owing to the high volatility of Cd under high-temperature conditions.This volatility introduces Te inclusions and cadmium vacancies(V_(Cd))into the as-grown CdTe ingot,which significantly degrades the device performance.In this study,we successfully obtained detector-grade CdTe single crystals by simultaneously employing a Cd reservoir and chlorine(Cl)dopants via a vertical gradient freeze(VGF)method.By installing a Cd reservoir,we can maintain the Cd pressure under the crystal growth conditions,thereby preventing the accumulation of Te in the CdTe ingot.Additionally,the existence of the Cl dopant helps improve the CdTe resistivity by minimizing V_(Cd)density through the formation of an acceptor complex(Cl_(Te)-V_(Cd))^(-1).The crystalline quality of the obtained CdTe(Cl)was evidenced by a reduction in large Te inclusions,high optical transmission(60%),and a sharp absorption edge(1.456 eV).The presence of substitutional Cl dopants,known as Cl_(Te)^(+),simultaneously supports the record high resistivity of 1.5×10^(10)Ω·cm and remarkable electron mobility of 1075±88 cm^(2)V^(-1)s^(-1)simultaneously,has been confirmed by photoluminescence spectroscopy.Moreover,using our crystals,we fabricated a planar detector withμτ_(e)of(1.11±0.04)×10^(-4)cm^(2)∕V,which performed with a decent radiation-detection feature.This study demonstrates that the vapor-pressure-controlled VGF method is a viable technical route for fabricating detector-grade CdTe crystals.
文摘This study aims to discuss the propagation characteristics of seismic Lamb wave and surface wave in a new radial gradient supercell seismic metamaterial(RGSSM).Different from the traditional seismic metamaterials with simple unit cells,the RGSSM consists of the supercells arranged periodically along the radial direction,and these supercells are composed of five kinds of unit cells with gradient filling rates.The dispersion curve and attenuation spectrum of the Lamb wave in RGSSM are studied with the finite element method combined with the supercell technology,generating a very low-frequency ultra-wide bandgap of 3.98-20 Hz,and a forbidden band generated by the localized modes forms multi-harmonic oscillators inside the supercell.Furthermore,it is found that the Young’s modulus of the soil is more sensitive to the effect of bandgap.In terms of surface wave,the RGSSM can produce rapid attenuation in the low frequency range of 5.2-8.5 Hz.Finally,a 3D model is designed to demonstrate the shielding performance of RGSSM against the seismic surface wave.The proposed RGSSM provides a new idea for the seismic isolation of ultra-wide and low-frequency seismic waves.
基金funded by the Hanoi University of Mining and Geology(Grant No.T23-44)The research is also funded by the German Research Foundation(DFG e Project number 518862444)in collaboration with the Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number DFG.105e2022.03The third author was funded by the Postdoctoral Scholarship Program of the Vingroup Innovation Foundation(VINIF)(VINIF.2023.STS.15).
文摘The quasi-rectangular tunnel represents a novel cross-section design,intended to supersede the traditional circular and rectangular tunnel formats.Due to the limited capacity of the tunnel vault to withstand vertical loads,an interior column is often installed at the center to enhance its load-bearing capacity.This study aims to develop a hyperstatic reaction method(HRM)for the analysis of deformation and structural integrity in this specific tunnel type.The computational model is validated through comparison with the corresponding finite element method(FEM)analysis.Following comprehensive validation,an ensemble machine learning(ML)model is proposed,using numerical benchmark data,to facilitate real-time design and optimization.Subsequently,three widely used ensemble models,i.e.random forest(RF),gradient boosting decision tree(GBDT),and extreme gradient boosting(XGBoost)are compared to identify the most efficient ML model for replacing the HRM model in the design optimization process.The performance metrics,such as the coefficient of determination R2 of about 0.999 and the mean absolute percentage error(MAPE)of about 1%,indicate that XGBoost outperforms the others,exhibiting excellent agreement with the HRM analysis.Additionally,the model demonstrates high computational efficiency,with prediction times measured in seconds.Finally,the HRM-XGBoost model is integrated with the well-known particle swarm optimization(PSO)for the real-time design optimization of quasi-rectangular tunnels,both with and without the interior column.A feature importance assessment is conducted to evaluate the sensitivity of design input features,enabling the selection of the most critical features for the optimization task.
基金With the support of the key project of Knowledge Innovation, CAS(KZCX1-y01, KZCX-SW-18), Fund of the China National Natural Sciences and the Daqing Oilfield with Grant No. 49894190
文摘In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.
基金support by the "Eleventh Five-Year" National Science and Technology Support Program (No. 2006BAB01A02)the Pivot Program of the National Natural Science Fund (No. 40930314)
文摘The conventional gravity gradient method to plot the geologic body location is fuzzy. When the depth is large and the geologic body is small, the Vzz and Vzx derivative errors are also large. We describe that using the status distinguishing factor to optimally determine the comer location is more accurate than the conventional higher-order derivative method. Thus, a better small geologic body and fault resolution is obtained by using the gravity gradient method and trial theoretical model calculation. The actual data is better processed, providing a better basis for prospecting and determination of subsurface geologic structure.
文摘In this paper, we present a new hybrid conjugate gradient algorithm for unconstrained optimization. This method is a convex combination of Liu-Storey conjugate gradient method and Fletcher-Reeves conjugate gradient method. We also prove that the search direction of any hybrid conjugate gradient method, which is a convex combination of two conjugate gradient methods, satisfies the famous D-L conjugacy condition and in the same time accords with the Newton direction with the suitable condition. Furthermore, this property doesn't depend on any line search. Next, we also prove that, moduling the value of the parameter t,the Newton direction condition is equivalent to Dai-Liao conjugacy condition.The strong Wolfe line search conditions are used.The global convergence of this new method is proved.Numerical comparisons show that the present hybrid conjugate gradient algorithm is the efficient one.
文摘In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Combining the quasi-Newton method with the new method, the former is modified to have global convergence property. Numerical results show that the new algorithm is efficient.
基金Project supported by the National Natural Science Foundation of China(Nos.5130926141030747+3 种基金41102181and 51121005)the National Basic Research Program of China(973 Program)(No.2011CB013503)the Young Teachers’ Initial Funding Scheme of Sun Yat-sen University(No.39000-1188140)
文摘Fast solving large-scale linear equations in the finite element analysis is a classical subject in computational mechanics. It is a key technique in computer aided engineering （CAE） and computer aided manufacturing （CAM）. This paper presents a high-efficiency improved symmetric successive over-relaxation （ISSOR） preconditioned conjugate gradient （PCG） method, which maintains lelism consistent with the original form. Ideally, the by 50% as compared with the original algorithm. the convergence and inherent paralcomputation can It is suitable for be reduced nearly high-performance computing with its inherent basic high-efficiency operations. By comparing with the numerical results, it is shown that the proposed method has the best performance.
文摘Gradiently denitrated gun propellant(GDGP)prepared by a“gradient denitration”strategy is obviously superior in progressive burning performance to the traditional deterred gun propellant.Currently,the preparation of GDGP employed a tedious two-step method involving organic solvents,which hinders the large-scale preparation of GDGP.In this paper,GDGP was successfully prepared via a novelty and environmentally friendly one-step method.The obtained samples were characterized by FT-IR,Raman,SEM and XPS.The results showed that the content of nitrate groups gradiently increased from the surface to the core in the surface layer of GDGP and the surface layer of GDGP exhibited a higher compaction than that of raw gun propellant,with a well-preserved nitrocellulose structure.The denitration process enabled the propellant surface with regressive energy density and good progressive burning performance,as confirmed by oxygen bomb and closed bomb test.At the same time,the effects of different solvents on the component loss of propellant were compared.The result showed that water caused the least component loss.Finally,the stability of GDGP was confirmed by methyl-violet test.This work not only provided environmentally friendly,simple and economic preparation of GDGP,but also confirmed the stability of GDGP prepared by this method.
基金The NSF (10871220) of Chinathe Doctoral Foundation (Y080820) of China University of Petroleum
文摘Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to decrease the magnitude of network weights. In this paper, some weight boundedness and deterministic con- vergence theorems are proved for the online gradient method with penalty for BP neural network with a hidden layer, assuming that the training samples are supplied with the network in a fixed order within each epoch. The monotonicity of the error function with penalty is also guaranteed in the training iteration. Simulation results for a 3-bits parity problem are presented to support our theoretical results.
文摘The aim of the study was to prepare berberine hydrochloride long-circulating liposomes and optimize the formulation and process parameters,and investigate the influence of different factors on the encapsulation efficiency.Berberine hydrochloride liposomes were prepared in response to a transmembrane ion gradient that was established by ionophore A23187.Free and liposomal drug were separated by cation exchange resin,and then the amount of intraliposomal berberine hydrochloride was determined by UV spectrophotometry.The optimized encapsulation efficiency of berberine hydrochloride liposomes was 94.3%2.1%when the drug-to-lipid ratio was 1:20,and the mean diameter was 146.9 nm3.2 nm.As a result,the ionophore A23187-mediated ZnSO_(4)gradient method was suitable for the preparation of berberine hydrochloride liposomes that we could get the desired encapsulation efficiency and drug loading.
基金partially supported by the National Natural Science Foundation of China (No.41230318)
文摘With the development of computational power, there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image, such as full-waveform inversion and least squares migration. However, though more advanced than conventional methods, these data fitting methods can be very expensive in terms of computational cost. Recently, various techniques to optimize these data-fitting seismic inversion problems have been implemented to cater for the industrial need for much improved efficiency. In this study, we propose a general stochastic conjugate gradient method for these data-fitting related inverse problems. We first prescribe the basic theory of our method and then give synthetic examples. Our numerical experiments illustrate the potential of this method for large-size seismic inversion application.
文摘Let C be a nonempty closed convex subset of a 2-uniformly convex and uniformly smooth Banach space E and {An}n∈N be a family of monotone and Lipschitz continuos mappings of C into E＊. In this article, we consider the improved gradient method by the hybrid method in mathematical programming [i0] for solving the variational inequality problem for {AN} and prove strong convergence theorems. And we get several results which improve the well-known results in a real 2-uniformly convex and uniformly smooth Banach space and a real Hilbert space.
基金Supported by the National Nature Science Foundation of China(50099620)the Project of Chenguang Plan in Wuhan(985003062)
文摘This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better.Then an improved double-threshold method is proposed,which is combined with the method of maximum classes variance,estimating-area method and double-threshold method.This method can automatically select two different thresholds to segment gradient images.The computer simulation is performed on the traditional methods and this algorithm and proves that this method can get satisfying result.
基金Supported by Research Council of Semnan University
文摘A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysis, it is shown that search directions of the proposed method satisfy the sufficient descent condition, independent of the line search and the objective function convexity. Global convergence of the method is established under an Armijo–type line search condition. Numerical experiments show practical efficiency of the proposed method.
