Results 41 to 50 of about 24,807 (199)
Reformulations of mathematical programming problems as linear complementarity problems [PDF]
, 1982 A family of complementarity problems are defined as extensions of the well known Linear Complementarity Problem (LCP). These are (i.) Second Linear Complementarity Problem (SLCP) which is an LCP extended by introducing further equality restrictions and ...
Judice, JJ, Mitra, G
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Advanced Intelligent Systems, EarlyView.A data‐efficient artificial intelligence‐assisted framework, which integrates experimental data with machine learning, is developed for the design of bimodal networked dielectric elastomers (DEs) as advanced artificial muscles. It adopts neural networks to predict DEs’ mechanical properties and support vector machines to classify electromechanical ...
Ofoq Normahmedov +8 more
wiley +1 more source
A Global Optimization Algorithm for Signomial Geometric Programming Problem
Abstract and Applied Analysis, 2014 This paper presents a global optimization algorithm for solving the signomial geometric programming (SGP) problem. In the algorithm, by the straight forward algebraic manipulation of terms and by utilizing a transformation of variables, the initial ...
Xue-Ping Hou +2 more
doaj +1 more source
A Cutting Plane Algorithm for Solving Bilinear Programs [PDF]
, 1974 Nonconvex programs which have either a nonconvex minimand and/or a nonconvex feasible region have been considered by most mathematical programmers as a hopelessly difficult area of research. There are, however, two exceptions where considerable effort to
Konno, H.
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Accounting for animal health in efficiency analysis: An application to Swedish dairy farms
American Journal of Agricultural Economics, EarlyView.Abstract Poor animal health is a central concern in modern livestock production. Despite the necessity to incorporate animal health in efficiency analysis, the theoretical and empirical developments are limited on this subject. This article appropriately characterizes the axiomatic properties of animal health within a production framework.
Frederic Ang +3 more
wiley +1 more source
Novel Model for The Energy Minimisation of Natural Gas Transmission Networks
Chemical Engineering Transactions, 2017 Natural gas transmission through an extensive pipeline network is an energy consuming process required gas compression due to significant pressure drop. The main difficulties in optimising a natural gas transmission network are the nonconvexities of the ...
Y. Liang, E. Pahija, C.-W. Hui
doaj +1 more source
Efficiency and Approachability of Nonconvex Bicriteria Programs
Journal of Mathematical Analysis and Applications, 2001 The paper presents some characterizations of efficient and proper efficient solutions of general bicriteria optimization problems via the concepts of modified lower envelopes and approachability [\textit{M. L. Tenhuisen} and \textit{M. M. Wiecek}, J. Glob. Optim. 11, 225-251 (1997; Zbl 0903.90147)]. By using two kinds of nonlinear Lagrangian functions,
Huang, X.X., Yang, X.Q.
openaire +1 more source
Primal-Dual Interior Methods for Nonconvex Nonlinear Programming [PDF]
SIAM Journal on Optimization, 1998 Summary: This paper concerns large-scale general (nonconvex) nonlinear programming when first and second derivatives of the objective and constraint functions are available. A method is proposed that is based on finding an approximate solution of a sequence of unconstrained subproblems parameterized by a scalar parameter. The objective function of each
Forsgren, Anders, Gill, Philip E.
openaire +2 more sources
The Canadian Journal of Chemical Engineering, EarlyView.Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos +3 more
wiley +1 more source
Block stochastic gradient iteration for convex and nonconvex optimization [PDF]
, 2014 The stochastic gradient (SG) method can minimize an objective function composed of a large number of differentiable functions, or solve a stochastic optimization problem, to a moderate accuracy.
Xu, Yangyang, Yin, Wotao
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