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학술지 Nonlinear optimization algorithm using monotonically increasing quantization resolution
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석진욱, 김정시
ETRI Journal, v.45 no.1, pp.119-130
한국전자통신연구원 (ETRI)
22HS2800, 신경망 응용 자동생성 및 실행환경 최적화 배포를 지원하는 통합개발 프레임워크 기술개발, 조창식
We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round-off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round-off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization-based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.
KSP 제안 키워드
Equation based, Fixed-point, Global optimization, Gradient Search, Nonlinear optimization algorithm, Numerical simulations, Optimization performance, Quantization resolution, Random variable, Round-off error, Search Algorithm(GSA)
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