Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Ararat, Cagin"'
We introduce VOPy, an open-source Python library designed to address black-box vector optimization, where multiple objectives must be optimized simultaneously with respect to a partial order induced by a convex cone. VOPy extends beyond traditional m
Externí odkaz:
http://arxiv.org/abs/2412.06604
Learning problems in which multiple conflicting objectives must be considered simultaneously often arise in various fields, including engineering, drug design, and environmental management. Traditional methods for dealing with multiple black-box obje
Externí odkaz:
http://arxiv.org/abs/2412.02484
Autor:
AlAli, Wissam, Ararat, Çağın
This paper investigates the convergence properties of sample-average approximations (SAA) for set-valued systemic risk measures. We assume that the systemic risk measure is defined using a general aggregation function with some continuity properties
Externí odkaz:
http://arxiv.org/abs/2408.08511
Autor:
Ararat, Çağın, Feinstein, Zachary
Risk measures for random vectors have been considered in multi-asset markets with transaction costs and financial networks in the literature. While the theory of set-valued risk measures provide an axiomatic framework for assigning to a random vector
Externí odkaz:
http://arxiv.org/abs/2407.16878
Autor:
Ararat, Çağın, Ma, Jin
In this paper we study the path-regularity and martingale properties of the set-valued stochastic integrals defined in our previous work Ararat et al. (2023). Such integrals have some fundamental differences from the well-known Aumann-It\^{o} stochas
Externí odkaz:
http://arxiv.org/abs/2308.13110
Autor:
Ararat, Çağın
We study the composition of two set-valued functions defined on locally convex topological linear spaces. We assume that these functions map into certain complete lattices of sets that have been used to establish a conjugation theory for set-valued f
Externí odkaz:
http://arxiv.org/abs/2306.15906
In this work, we propose an outer approximation algorithm for solving bounded convex vector optimization problems (CVOPs). The scalarization model solved iteratively within the algorithm is a modification of the norm-minimizing scalarization proposed
Externí odkaz:
http://arxiv.org/abs/2302.08723
Autor:
Ararat, Çağın, Cetin, Umur
As appropriate generalizations of convex combinations with uncountably many terms, we introduce the so-called Choquet combinations, Choquet decompositions and Choquet convex decompositions, as well as their corresponding hull operators acting on the
Externí odkaz:
http://arxiv.org/abs/2201.06141
Convexity and quasiconvexity are two properties that capture the concept of diversification for risk measures. Between the two, there is natural quasiconvexity, an old but not so well-known property weaker than convexity but stronger than quasiconvex
Externí odkaz:
http://arxiv.org/abs/2201.05686
Autor:
Ararat, Çağın, Tekin, Cem
We introduce vector optimization problems with stochastic bandit feedback, in which preferences among designs are encoded by a polyhedral ordering cone $C$. Our setup generalizes the best arm identification problem to vector-valued rewards by extendi
Externí odkaz:
http://arxiv.org/abs/2110.12311