Gradient and Hessian of Joint Probability Function with Applications on Chance-Constrained Programs
Sign Up to like & getrecommendations! Published in 2017 at "Journal of the Operations Research Society of China"
DOI: 10.1007/s40305-017-0154-6
Abstract: Joint probability function refers to the probability function that requires multiple conditions to satisfy simultaneously. It appears naturally in chance-constrained programs. In this paper, we derive closed-form expressions of the gradient and Hessian of joint… read more here.
Keywords: probability; chance constrained; joint probability; probability function ... See more keywords
Data-Driven Approximations of Chance Constrained Programs in Nonstationary Environments
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DOI: 10.1109/lcsys.2022.3174132
Abstract: We study sample average approximations (SAA) of chance constrained programs. SAA methods typically approximate the actual distribution in the chance constraint using an empirical distribution constructed from random samples assumed to be independent and identically… read more here.
Keywords: constrained programs; actual distribution; distribution; chance constrained ... See more keywords
Sample-Based Neural Approximation Approach for Probabilistic Constrained Programs.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3102323
Abstract: This article introduces a neural approximation-based method for solving continuous optimization problems with probabilistic constraints. After reformulating the probabilistic constraints as the quantile function, a sample-based neural network model is used to approximate the quantile… read more here.
Keywords: based neural; sample based; neural approximation; probabilistic constrained ... See more keywords