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2001
In the previous chapter, we study the minimization problem of a convex function under convex constraints. In this chapter, we study the minimization problem for two other classes of functions. The first ones are quasi-convex functions. We know (see Chapter 5) that for a convex function f, any level set of f is convex. But the converse is not true.
Monique Florenzano, Cuong Le Van
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In the previous chapter, we study the minimization problem of a convex function under convex constraints. In this chapter, we study the minimization problem for two other classes of functions. The first ones are quasi-convex functions. We know (see Chapter 5) that for a convex function f, any level set of f is convex. But the converse is not true.
Monique Florenzano, Cuong Le Van
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SPIE Proceedings, 1986
A stochastic search technique called simulated annealing can solve a class of problems termed non-convex optimization by seeking the lowest minimum of a multi-minima function. Simulated annealing is a generalized Monte Carlo technique with a continuously decreasing variance controlled by the temperature parameter.
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A stochastic search technique called simulated annealing can solve a class of problems termed non-convex optimization by seeking the lowest minimum of a multi-minima function. Simulated annealing is a generalized Monte Carlo technique with a continuously decreasing variance controlled by the temperature parameter.
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Non-convex multi-objective optimization
Optimization Methods and Software, 2017During several decades, multi-objective optimization is a very active research area. The actuality of this subject stems from real-life applications as well as from its high theoretical importance....
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Non-Convex Optimization: A Review
2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 2020With the rapid development in technology, Artificial Intelligence is responsible for giving solution to every new problem in technology. Artificial Intelligence is the combat process of application, implementation and self- correction. The most potential application of Artificial Intelligence is Machine Learning.
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An Optimal Algorithm for Online Non-Convex Learning
Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems, 2018In many online learning paradigms, convexity plays a central role in the derivation and analysis of online learning algorithms. The results, however, fail to be extended to the non-convex settings, which are necessitated by tons of recent applications.
Lin Yang 0013 +4 more
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Convexity in Non-convex Optimizations of Streaming Applications
2012 IEEE 18th International Conference on Parallel and Distributed Systems, 2012Streaming data applications are frequently pipelined and deployed on application-specific systems to meet performance requirements and resource constraints. Typically, there are several design parameters in the algorithms and architectures used that impact the application performance as well as resource utilization. Efficient exploration of this design
Shobana Padmanabhan +2 more
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A Non-convex Optimization Model for Signal Recovery
Neural Processing Letters, 2020The electroencephalogram (EEG) signal is one of the most frequently used biomedical signals. In order to accurately exploit the cosparsity and low-rank property which is nature in multichannel EEG signals, motivated by the fact that weighted schatten-p norm and $${l_q}$$ norm can better approximate the matrix rank and $${l_0}$$ norm, in this paper, a ...
Changwei Chen, Xiaofeng Zhou
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On algorithms for non-convex optimization
2018Abstract: "A simple algorithm for the computation of local minima of non-convex problems in one dimension is proposed. This algorithm avoids certain well known local minima which are distant from the global minimum, and can be used to improve the minima obtained using classical descent methods. The calculated minima have mesh scale oscillations typical
Ma, Ling, Walkington, Noel
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Nonlinear non-convex optimization of hydraulic networks
2013 IEEE International Conference on Control Applications (CCA), 2013Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption in pumps and also to regulate the pressure at the end-user ...
Maryamsadat Tahavori +3 more
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Non-convex sparse regularization via convex optimization for impact force identification
Mechanical Systems and Signal Processing, 2023Baijie Qiao, Xuefeng Chen
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