A Framework Of Distributionally Robust Possibilistic Optimization Deepai

A Framework Of Distributionally Robust Possibilistic Optimization | DeepAI

A Framework Of Distributionally Robust Possibilistic Optimization | DeepAI In this paper, an optimization problem with uncertain constraint coefficients is considered. possibility theory is used to model the uncertainty. namely, a joint possibility distribution in constraint coefficient realizations, called scenarios, is specified. In this paper, we will use some relationships between possibility and probability theories, which allows us to define a class of admissible probability distributions for the problem parameters.

Unifying Distributionally Robust Optimization Via Optimal Transport Theory | DeepAI

Unifying Distributionally Robust Optimization Via Optimal Transport Theory | DeepAI In this paper, we will show how the possibility theory (dubois & prade, 1988) can be used in the context of distributionally robust optimization. possibility theory offers a framework for dealing with uncertainty. In this paper, an optimization problem with uncertain constraint coefficients is considered. possibility theory is used to model the uncertainty. namely, a joint possibility distribution in. In this paper a class of optimization problems with uncertain linear constraints is discussed. it is assumed that the constraint coefficients are random vectors whose probability distributions are only partially known. possibility theory is used to model the imprecise probabilities. A modular framework is presented to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules.

Optimal Algorithms For Group Distributionally Robust Optimization And Beyond | DeepAI

Optimal Algorithms For Group Distributionally Robust Optimization And Beyond | DeepAI In this paper a class of optimization problems with uncertain linear constraints is discussed. it is assumed that the constraint coefficients are random vectors whose probability distributions are only partially known. possibility theory is used to model the imprecise probabilities. A modular framework is presented to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. In this paper, an optimization problem with uncertain constraint coefficients is considered. possibility theory is used to model the uncertainty. namely, a joint possibility distribution in constraint coefficient realizations, called scenarios, is specified. A modeling framework, called distributionally robust optimization (dro), has recently received significant attention in both the operations research and statistical learning communities. Dro seeks decisions that perform best under the worst distribution in the ambiguity set. this worst case criterion is supported by findings in psychology and neuroscience, which indicate that many decision makers have a low tolerance for distributional ambiguity. In this paper, we have proposed a framework for handling uncertain constraints in optimization problems. we have applied fuzzy intervals to model uncertain con straint coeficients.

Computing The Optimal Distributionally-robust Strategy To Commit To | DeepAI

Computing The Optimal Distributionally-robust Strategy To Commit To | DeepAI In this paper, an optimization problem with uncertain constraint coefficients is considered. possibility theory is used to model the uncertainty. namely, a joint possibility distribution in constraint coefficient realizations, called scenarios, is specified. A modeling framework, called distributionally robust optimization (dro), has recently received significant attention in both the operations research and statistical learning communities. Dro seeks decisions that perform best under the worst distribution in the ambiguity set. this worst case criterion is supported by findings in psychology and neuroscience, which indicate that many decision makers have a low tolerance for distributional ambiguity. In this paper, we have proposed a framework for handling uncertain constraints in optimization problems. we have applied fuzzy intervals to model uncertain con straint coeficients.

(PDF) Doubly Robust Data-Driven Distributionally Robust Optimization

(PDF) Doubly Robust Data-Driven Distributionally Robust Optimization Dro seeks decisions that perform best under the worst distribution in the ambiguity set. this worst case criterion is supported by findings in psychology and neuroscience, which indicate that many decision makers have a low tolerance for distributional ambiguity. In this paper, we have proposed a framework for handling uncertain constraints in optimization problems. we have applied fuzzy intervals to model uncertain con straint coeficients.

A Framework Of Distributionally Robust Possibilistic Optimization | DeepAI

A Framework Of Distributionally Robust Possibilistic Optimization | DeepAI

Robust and Adaptive Deep Learning via Bayesian Principles (AAAI 2023 New Faculty Highlights)