Inducing Optimality in Prescribed Performance Control for Uncertain Euler–Lagrange Systems by Christos Vlachos, Ioanna Malli, Charalampos P. Bechlioulis, Kostas J. Kyriakopoulos

“…Subsequently, a successive approximation algorithm is applied, employing the acquired dynamics from the previous step, to find a near-optimal control law that takes into consideration prescribed performance specifications, such as convergence speed and steady-state error. …”
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Determining Position Inside Non-industrial Buildings Using Ultrasound Transducers by Juan Pérez, Josep M. Ribes, Maria Alsina, Sonia Luengo, Jordi Margalef, Francesc Escudero

“…The receivers circulating beneath the transmitters receive the codes ofthose within their detection range, translate the time delays into distances and then obtaintheir position by triangulation since the receivers know the position of every transmitter.Since the receivers are not synchronised with the common time signal or the actual speedof the sound, whose value varies appreciably with temperature, relative humidity andatmospheric pressure, a consecutive approximation algorithm has been introduced. This isbased on the fact that the Z coordinator of the receiver is known and constant and thus it is possible, with only three different identifiers received, to deduce the phase of the common time signal and estimate the speed of the sound with a fourth identifier.…”
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IMPROVEMENT OF EVOLUTIONARY STRUCTURAL OPTIMIZATION METHOD FOR 2-D MODEL by CHEN XiaoMing, LAI XiDe, TANG Jian, ZHU Li, ZHAO Xi

“…The other is interval approximation algorithm. Minimum initial rejection rate R_0minwhich fulfill removed conditions was ascertained by using this algorithm and it was used as starting value,thus other initial rejection rate R₀ were obtained by setting certain increment. …”
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Quasiperiodic Patterns of the Complex Dimensions of Nonlattice Self-Similar Strings, via the LLL Algorithm by Michel L. Lapidus, Machiel van Frankenhuijsen, Edward K. Voskanian

“…The Lattice String Approximation algorithm (or LSA algorithm) of M. L. Lapidus and M. van Frankenhuijsen is a procedure that approximates the complex dimensions of a nonlattice self-similar fractal string by the complex dimensions of a lattice self-similar fractal string. …”
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Multiobjective Optimization of a Metal Complex Catalytic Reaction Based on a Detailed Kinetic Model with Parallelization of Calculations by Sergey Koledin, Kamila Koledina, Irek Gubaydullin

“…The solution of the multiobjective optimization problem was performed with the help of the Pareto approximation algorithm. The problem of multiobjective optimization of the reaction process conditions for the olefin hydroalumination catalytic reaction, with the presence of organoaluminum compounds diisobutylaluminiumchloride, diisobutylaluminiumhydrate, and triisobutylaluminum, was solved. …”
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Parameter differentiation method in solution of axisymmetric soft shells stationary dynamics nonlinear problems by Ekaterina A. Korovaytseva

“…Three- and four-point finite difference schemes are used for acceleration approximation. Algorithm testing is carried out for the example of hinged hemisphere of neo-hookean material dynamic inflation. …”
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Service function chain embedding algorithm with wireless multicast in mobile edge computing network by Kan WANG, Nan ZHAO, Junhuai LI, Huaijun WANG

“…To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain (SFC) embedding in mobile edge computing (MEC) networks,a content-oriented joint wireless multicast and SFC embedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.By involving four kinds of system overhead,including service flow,server function sustaining power,server function service power and wireless transmission power,an optimization model was proposed to jointly design SFC embedding with multicast beamforming.Firstly,with Lagrangian dual decomposition,the problem was decoupled into two independent subproblems,namely,SFC embedding and multicast beamforming.Secondly,with the L<sub>p</sub> norm penalty term-based successive convex approximation algorithm,the integer programming-based SFC embedding problem was relaxed to an equivalent linear programming one.Finally,the non-convex beamforming optimization problem was transformed into a series of convex ones via the path following technique.Simulation results revealed that the proposed algorithm has good convergence,and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicasting in terms of system overhead.…”
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A Simulation-Based Optimization Method for Hybrid Frequency Regulation System Configuration by Jie Song, Xin Pan, Chao Lu, Hanchen Xu

“…Taking advantage of the gradient-based approximation algorithm, we are then able to optimize the capacity of a hybrid system. …”
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Fractional Set Cover in the Streaming Model by Indyk, Piotr, Mahabadi, Sepideh, Rubinfeld, Ronitt, Ullman, Jonathan, Vakilian, Ali, Yodipinyanee, Anak

“…We present a randomized (1+)- approximation algorithm that makes p passes over the data, and uses eO(mnO(1/p) +n) memory space. …”
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Approximating weighted completion time via stronger negative correlation by Baveja, Alok, Qu, Xiaoran, Srinivasan, Aravind

“…The first (3/2−𝑐) –approximation algorithm for this problem, for some constant 𝑐>0 , was obtained in the work of Bansal et al. …”
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On Chebyshev radius of a set in Hamming space and the closest string problem by Mazumdar, Arya, Polyanskiy, Yury, Saha, Barna

“…This results in a simple polynomial-time approximation algorithm that attains the performance of the best known such algorithms with shorter running time.…”
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Optimal mean-variance portfolio selection with mean-field reinforcement learning by Cheng, Zhengxing

“…After that, we propose and implement the multiple-period mean-field Q-learning with function approximation algorithm to obtain the optimal strategies. …”
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Receding horizon cache and extreme learning machine based reinforcement learning by Shao, Zhifei, Er, Meng Joo, Huang, Guang-Bin

“…Together with Extreme Learning Machine (ELM), a new RL with function approximation algorithm termed as RHC and ELM based RL (RHC-ELM-RL) is proposed. …”
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Approximate weighted model integration on DNF structures by Abboud, R, Ceylan, İİ, Dimitrov, R

“…Building on classical results from approximate weighted model counting and approximate volume computation, we show that weighted model integration on DNF structures can indeed be approximated for a class of weight functions. Our approximation algorithm is based on three subroutines, each of which can be a weak (i.e., approximate), or a strong (i.e., exact) oracle, and in all cases, comes along with accuracy guarantees. …”

A fixed-parameter perspective on #BIS by Curticapean, R, Dell, H, Fomin, F, Goldberg, L, Lapinskas, J

“…It is believed that #BIS does not have an efficient approximation algorithm but also that it is not NP-hard. We study the robustness of the intermediate complexity of #BIS by considering variants of the problem parameterised by the size of the independent set. …”

A fixed-parameter perspective on #BIS by Curticapean, R, Dell, H, Fomin, F, Goldberg, L, Lapinskas, J

“…It is believed that #BIS does not have an efficient approximation algorithm but also that it is not NP-hard. We study the robustness of the intermediate complexity of #BIS by considering variants of the problem parameterised by the size of the independent set. …”

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors by Anxing Shan, Xianghua Xu, Zongmao Cheng, Wensheng Wang

“…We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. …”
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APPROXIMATELY SINGULAR WAVELET by V. M. Romanchak

“…To illustrate the effectiveness of the numerical approximation algorithm, we consider an example of the quasi-interpolation of the Runge function by wavelets with a uniform distribution of interpolation nodes.…”
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Research on Logistics Distribution Vehicle Path Optimization Based on Simulated Annealing Algorithm by Li Yang

“…The simulated annealing algorithm is an effective approximation algorithm for solving optimization problems, and the application of this algorithm to path optimization problems can be of practical value in solving problems in urban road traffic and logistics distribution. …”
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Function approximation method based on weights gradient descent in reinforcement learning by Xiaoyan QIN, Yuhan LIU, Yunlong XU, Bin LI

“…Function approximation has gained significant attention in reinforcement learning research as it effectively addresses problems with large-scale, continuous state, and action space.Although the function approximation algorithm based on gradient descent method is one of the most widely used methods in reinforcement learning, it requires careful tuning of the step size parameter as an inappropriate value can lead to slow convergence, unstable convergence, or even divergence.To address these issues, an improvement was made around the temporal-difference (TD) algorithm based on function approximation.The weight update method was enhanced using both the least squares method and gradient descent, resulting in the proposed weights gradient descent (WGD) method.The least squares were used to calculate the weights, combining the ideas of TD and gradient descent to find the error between the weights.And this error was used to directly update the weights.By this method, the weights were updated in a new manner, effectively reducing the consumption of computing resources by the algorithm enhancing other gradient descent-based function approximation algorithms.The WGD method is widely applicable in various gradient descent-based reinforcement learning algorithms.The results show that WGD method can adjust parameters within a wider space, effectively reducing the possibility of algorithm divergence.Additionally, it achieves better performance while improving the convergence speed of the algorithm.…”
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