There are many innovative ideas for the optimization of soft tissue simulation [1, 2].
The fly visual system proved to be a very fertile testing ground for the idea of optimization in neural coding and computation; look here for details.
In order to optimize the shafting alignment quality and vibration characteristics simultaneously by adjusting the vertical position of each bearing, optimization weight coefficient was present, which provided a new idea for comprehensive optimization of vibration damping and shafting alignment.
This paper presents a novel Lagrangian relaxation neural network (LRNN) for the scheduling of SCCP by combing recurrent neural network optimization ideas for constraint handling.
Our main contribution is to propose a novel algorithm for finding a fixed point of a nonexpansive mapping, for which we use the ideas of accelerated conjugate gradient methods for optimization over the fixed point set [17, 18], and prove that the algorithm converges to some fixed point in the sense of the strong topology of a real Hilbert space.
Research to address next generation issues in computer architecture to address the grand challenge of parallel computation, bringing in techniques from other fields (e.g. machine learning for high impact optimizations), ideas for architecture based on novel substrates, power budget issues, micro-architectural circuit level issues, and architectural issues in the development of sensor networks.
With this, we present some ideas for solving special set-valued optimization problems in our paper (see Section 4).
We will show how tools and ideas from convex optimization give simple, robust algorithms for recovering low-rank matrices from incomplete, corrupted and noisy observations.
Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for a wide class of bilevel problems.
Finally, we discuss ideas and optimizations for reducing the execution time of our algorithm.
Finally, we discuss ideas and optimizations for improving the run-time efficiency of our algorithm.
