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Journal articles on the topic 'Optimisation nonconvexe'

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1

Smith, E. "Global Optimisation of Nonconvex MINLPs." Computers & Chemical Engineering 21, no. 1-2 (1997): S791—S796. http://dx.doi.org/10.1016/s0098-1354(97)00146-4.

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2

Smith, Edward M. B., and Constantinos C. Pantelides. "Global optimisation of nonconvex MINLPs." Computers & Chemical Engineering 21 (May 1997): S791—S796. http://dx.doi.org/10.1016/s0098-1354(97)87599-0.

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3

Martínez-legaz, J. E., and A. Seeger. "A formula on the approximate subdifferential of the difference of convex functions." Bulletin of the Australian Mathematical Society 45, no. 1 (1992): 37–41. http://dx.doi.org/10.1017/s0004972700036984.

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We give a formula on the ε−subdifferential of the difference of two convex functions. As a by-product of this formula, one recovers a recent result of Hiriart-Urruty, namely, a necessary and sufficient condition for global optimality in nonconvex optimisation.
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4

Scott, Carlton H., and Thomas R. Jefferson. "Duality for linear multiplicative programs." ANZIAM Journal 46, no. 3 (2005): 393–97. http://dx.doi.org/10.1017/s1446181100008336.

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AbstractLinear multiplicative programs are an important class of nonconvex optimisation problems that are currently the subject of considerable research as regards the development of computational algorithms. In this paper, we show that mathematical programs of this nature are, in fact, a special case of more general signomial programming, which in turn implies that research on this latter problem may be valuable in analysing and solving linear multiplicative programs. In particular, we use signomial programming duality theory to establish a dual program for a nonconvex linear multiplicative p
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5

SULTANOVA, NARGIZ. "AGGREGATE SUBGRADIENT SMOOTHING METHODS FOR LARGE SCALE NONSMOOTH NONCONVEX OPTIMISATION AND APPLICATIONS." Bulletin of the Australian Mathematical Society 91, no. 3 (2015): 523–24. http://dx.doi.org/10.1017/s0004972715000143.

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6

ALI, ELAF J. "CANONICAL DUAL FINITE ELEMENT METHOD FOR SOLVING NONCONVEX MECHANICS AND TOPOLOGY OPTIMISATION PROBLEMS." Bulletin of the Australian Mathematical Society 101, no. 1 (2019): 172–73. http://dx.doi.org/10.1017/s0004972719001205.

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7

Thi, Hoai An Le, Hoai Minh Le, and Tao Pham Dinh. "Fuzzy clustering based on nonconvex optimisation approaches using difference of convex (DC) functions algorithms." Advances in Data Analysis and Classification 1, no. 2 (2007): 85–104. http://dx.doi.org/10.1007/s11634-007-0011-2.

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8

Ma, Kai, Congshan Wang, Jie Yang, Chenliang Yuan, and Xinping Guan. "A pricing strategy for demand-side regulation with direct load control: a nonconvex optimisation approach." International Journal of System Control and Information Processing 2, no. 1 (2017): 74. http://dx.doi.org/10.1504/ijscip.2017.084264.

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9

Ma, Kai, Congshan Wang, Jie Yang, Chenliang Yuan, and Xinping Guan. "A pricing strategy for demand-side regulation with direct load control: a nonconvex optimisation approach." International Journal of System Control and Information Processing 2, no. 1 (2017): 74. http://dx.doi.org/10.1504/ijscip.2017.10005200.

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10

Smith, E. M. B., and C. C. Pantelides. "A symbolic reformulation/spatial branch-and-bound algorithm for the global optimisation of nonconvex MINLPs." Computers & Chemical Engineering 23, no. 4-5 (1999): 457–78. http://dx.doi.org/10.1016/s0098-1354(98)00286-5.

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11

Pereira-Neto, A., C. Unsihuay, and O. R. Saavedra. "Efficient evolutionary strategy optimisation procedure to solve the nonconvex economic dispatch problem with generator constraints." IEE Proceedings - Generation, Transmission and Distribution 152, no. 5 (2005): 653. http://dx.doi.org/10.1049/ip-gtd:20045287.

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12

吴, 展雄. "Majorized Bregman Alternating Direction Method of Multipliers with Inertia Terms for Solving Nonconvex Optimisation Problems." Advances in Applied Mathematics 14, no. 06 (2025): 119–34. https://doi.org/10.12677/aam.2025.146306.

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13

Shaqfa, Mahmoud, and Katrin Beyer. "Pareto-like sequential sampling heuristic for global optimisation." Soft Computing 25, no. 14 (2021): 9077–96. http://dx.doi.org/10.1007/s00500-021-05853-8.

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AbstractIn this paper, we propose a simple global optimisation algorithm inspired by Pareto’s principle. This algorithm samples most of its solutions within prominent search domains and is equipped with a self-adaptive mechanism to control the dynamic tightening of the prominent domains while the greediness of the algorithm increases over time (iterations). Unlike traditional metaheuristics, the proposed method has no direct mutation- or crossover-like operations. It depends solely on the sequential random sampling that can be used in diversification and intensification processes while keeping
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14

Immanuel Selvakumar, A., and K. Thanushkodi. "Comment: Efficient evolutionary strategy optimisation procedure to solve the nonconvex economic dispatch problem with generator constraints." IET Generation, Transmission & Distribution 1, no. 2 (2007): 364. http://dx.doi.org/10.1049/iet-gtd:20060015.

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15

Ławryńczuk, Maciej. "Efficient Nonlinear Predictive Control Based on Structured Neural Models." International Journal of Applied Mathematics and Computer Science 19, no. 2 (2009): 233–46. http://dx.doi.org/10.2478/v10006-009-0019-1.

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Efficient Nonlinear Predictive Control Based on Structured Neural ModelsThis paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding cla
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16

Gustafson, Sven-Åke. "Investigating semi-infinite programs using penalty functions and Lagrangian methods." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 28, no. 2 (1986): 158–69. http://dx.doi.org/10.1017/s0334270000005270.

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AbstractIn this paper the relations between semi-infinite programs and optimisation problems with finitely many variables and constraints are reviewed. Two classes of convex semi-infinite programs are defined, one based on the fact that a convex set may be represented as the intersection of closed halfspaces, while the other class is defined using the representation of the elements of a convex set as convex combinations of points and directions. Extension to nonconvex problems is given. A common technique of solving a semi-infinite program computationally is to derive necessary conditions for
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17

Pereira-Neto, A., C. Unsihuay, and O. R. Saavedra. "Reply: Concerning the comments on ‘Efficient evolutionary strategy optimisation procedure to solve the nonconvex economic dispatch problem with generator constraints’." IET Generation, Transmission & Distribution 1, no. 2 (2007): 366. http://dx.doi.org/10.1049/iet-gtd:20060469.

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18

Riedl, Konstantin. "Leveraging memory effects and gradient information in consensus-based optimisation: On global convergence in mean-field law." European Journal of Applied Mathematics, October 20, 2023, 1–32. http://dx.doi.org/10.1017/s0956792523000293.

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Abstract In this paper, we study consensus-based optimisation (CBO), a versatile, flexible and customisable optimisation method suitable for performing nonconvex and nonsmooth global optimisations in high dimensions. CBO is a multi-particle metaheuristic, which is effective in various applications and at the same time amenable to theoretical analysis thanks to its minimalistic design. The underlying dynamics, however, is flexible enough to incorporate different mechanisms widely used in evolutionary computation and machine learning, as we show by analysing a variant of CBO which makes use of m
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19

Riis, Erlend S., Matthias J. Ehrhardt, G. R. W. Quispel, and Carola-Bibiane Schönlieb. "A Geometric Integration Approach to Nonsmooth, Nonconvex Optimisation." Foundations of Computational Mathematics, July 29, 2021. http://dx.doi.org/10.1007/s10208-020-09489-2.

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AbstractThe optimisation of nonsmooth, nonconvex functions without access to gradients is a particularly challenging problem that is frequently encountered, for example in model parameter optimisation problems. Bilevel optimisation of parameters is a standard setting in areas such as variational regularisation problems and supervised machine learning. We present efficient and robust derivative-free methods called randomised Itoh–Abe methods. These are generalisations of the Itoh–Abe discrete gradient method, a well-known scheme from geometric integration, which has previously only been conside
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20

Mafakheri, Behnam, Jonathan H. Manton, and Iman Shames. "On Distributed Nonconvex Optimisation Via Modified ADMM." IEEE Control Systems Letters, 2023, 1. http://dx.doi.org/10.1109/lcsys.2023.3341100.

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21

Sturm, Kevin. "First-order differentiability properties of a class of equality constrained optimal value functions with applications." Journal of Nonsmooth Analysis and Optimization, November 25, 2020. http://dx.doi.org/10.46298/jnsao-2020-6034.

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In this paper we study the right differentiability of a parametric infimum function over a parametric set defined by equality constraints. We present a new theorem with sufficient conditions for the right differentiability with respect to the parameter. Target applications are nonconvex objective functions with equality constraints arising in optimal control and shape optimisation. The theorem makes use of the averaged adjoint approach in conjunction with the variational approach of Kunisch, Ito and Peichl. We provide two examples of our abstract result: (a) a shape optimisation problem involv
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22

Liu, Chao, Yingjie Ma, Dongda Zhang, and Jie Li. "A feasible path-based branch and bound algorithm for strongly nonconvex MINLP problems." Frontiers in Chemical Engineering 4 (September 19, 2022). http://dx.doi.org/10.3389/fceng.2022.983162.

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In this paper, a feasible path-based branch and bound (B&B) algorithm is proposed to solve mixed-integer nonlinear programming problems with highly nonconvex nature through integration of the previously proposed hybrid feasible-path optimisation algorithm and the branch and bound method. The main advantage of this novel algorithm is that our previously proposed hybrid steady-state and time-relaxation-based optimisation algorithm is employed to solve a nonlinear programming (NLP) subproblem at each node during B&B. The solution from a parent node in B&B is used to initia
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23

Saravanan, Nagesh, A. González-Huici María, Bathelt Andreas, Heredia Conde Miguel, and Ender Joachim. "Sequence Optimisation for Compressed Sensing CDMA MIMO Radar via Mutual Coherence Minimisation." December 12, 2023. https://doi.org/10.5281/zenodo.13234726.

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The selection of waveform parameters in Compressed Sensing (CS) Multiple Input Multiple  Output (MIMO) radar systems strongly influences the Mutual Coherence (MC) of the sensing matrix.  This study proposes an optimization methodology to modify the phase components corresponding to code sequences used for a CS-based collocated Code Division Multiple Access (CDMA) MIMO  radar. The problem is initialized with the phase components for a sequence of Hadamard codes,  and the optimization problem is formulated to minimize the l∞ norm of the off-diagonal elements of the Gram
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24

Janakiraman, Bhavithra, S. Prabu, M. Senthil Vadivu, and Dhineshkumar Krishnan. "Detection of ovarian follicles cancer cells using hybrid optimization technique with deep convolutional neural network classifier." Journal of Intelligent & Fuzzy Systems, July 13, 2023, 1–16. http://dx.doi.org/10.3233/jifs-231322.

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Having one’s life threatened by a disease like ovarian cancer is the single most crucial thing in the whole world. It is difficult to achieve high performance without sacrificing computational efficiency; the results of the denoising process are not as good as they could be; the proposed models are nonconvex and involve several manually chosen parameters, which provides some leeway to boost denoising performance; the methods generally involve a complex optimisation problem in the testing stage; Here at DnCNN, we’ve developed our own version of the deep ii learning model, a discriminative learn
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25

Zhang, Yu, Xuemei Bai, and Zhijun Wang. "Integrated Beam Assignment Design for RIS-Assisted Sensing Based on Flow Shape Optimization." Recent Advances in Computer Science and Communications 18 (May 26, 2025). https://doi.org/10.2174/0126662558377034250524060337.

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Introduction: The rapid advancement of wireless technologies has led to the exploration of systems that integrate sensing and communication functionalities, with Reconfigurable Intelligent Surfaces (RIS) playing a key role in enhancing next-generation (6G) communication systems, particularly in Integrated Sensing and Communication (ISAC) systems. This paper investigates RIS-assisted ISAC systems and aims to address the non-convex optimisation problem associated with passive beamforming under manifold constraints. We propose a joint optimisation model to enhance both radar and communication per
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