Jensenius, "A constrained optimization approach to preserving prior knowledge during incremental training," IEEE Trans. Neural Netw., vol. 19, no. 6, pp. 996-1009, Jun. 2008.S. Ferrari and M. Jensenius, "A constrained optimization approach to preserving prior knowledge during incremental ...
Spider dragline silk is known for its exceptional strength and toughness; hence understanding the link between its primary sequence and mechanics is crucial. Here, we establish a deep-learning framework to clarify this link in dragline silk. The method u
optimizationconstrainedcfdboundconclusionsstructures AutomaticDifferentiationforOptimizationShaunForthS.A.Forth@cranfield.ac.ukAppliedMathematics&ScientificComputationGroupEngineeringSystemsDepartmentCranfieldUniversity(RMCSShrivenham)SwindonSN68LA,UKMIR@WDAYOptimizationWarwickUniversity3rdOctober2005WorksupportedbyEPSRC...
elucidate that the optimization of physical properties (e.g., formation energy) can be integrated into the generative deep learning model as explicit constraints or back propagators. This allows further development of a multi-objective inverse design framework to optimize other physical properties by ...
More recently, Felberbauer, Gutjahr, & Doerner (2019) proposed two different optimization approaches for a multi-skilled project scheduling and personnel planning problem with stochastic demand. Extensions of the MSRCPSP: Bellenguez & Néron (2004) extended the MSRCPSP by introducing hierarchical ...
A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems 2008, Applied Mathematics and Computation Show abstract An integrated survey of deterministic project scheduling 2001, Omega Citation Excerpt : When we have such a performance measure, we can ...
Performance optimization of supply chain based on cooperative contract with disappointment-aversion strategic consumers Consumers' strategic behavior and psychological perception have impact on supply chains. In this paper, we consider a supply chain with one supplier and one retailer to study the influence...
optimization problems, including minimizing deep coalescence [4], maximizing quartet support [5,6] (see [7] for extensions to multi-copy genes), and maximizing bipartition support [8] (see [9] for extensions to multi-copy genes). All of these optimization problems take gene trees as input ...
R. Tapia, “Quasi-Newton method for equality constrained optimization: Equivalence of existing methods and a new implementation”, in: O. Mangasarian, R. Meyer and S. Robinson, eds., Nonlinear Programming 3 (Academic Press, New York, 1978) pp. 125–164....
By this model the phylogenetic tree problem turns into an optimization problem in a continuous vector space with probability constraints, and the reconstructed tree is called a probability phylogenetic tree. However, the Hamming distance often underestimates the number of substitutions that really occurred...