Search Results for author:
Found 13 papers, 3 papers with code
VOPy: A Framework for Black-box Vector Optimization
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.
Vector Optimization with Gaussian Process Bandits
VOGP allows users to convey objective preferences through ordering cones while performing efficient sampling by exploiting the smoothness of the objective function, resulting in a more effective optimization process that requires fewer evaluations.
Systemic values-at-risk and their sample-average approximations
We assume that the systemic risk measure is defined using a general aggregation function with some continuity properties and value-at-risk applied as a monetary risk measure.
On the Separability of Vector-Valued Risk Measures
Risk measures for random vectors have been considered in multi-asset markets with transaction costs and financial networks in the literature.
Decomposable sums and their implications on naturally quasiconvex risk measures
The notion of convexity index, defined in 1980s for finite-dimensional vector spaces, plays a crucial role in the discussion of decomposable sums.
Vector Optimization with Stochastic Bandit Feedback
We introduce vector optimization problems with stochastic bandit feedback, in which preferences among designs are encoded by a polyhedral ordering cone $C$.
MAD Risk Parity Portfolios
In this paper, we investigate the features and the performance of the Risk Parity (RP) portfolios using the Mean Absolute Deviation (MAD) as a risk measure.
Dual representations of quasiconvex compositions with applications to systemic risk
Motivated by the problem of finding dual representations for quasiconvex systemic risk measures in financial mathematics, we study quasiconvex compositions in an abstract infinite-dimensional setting.
Portfolio optimization with two quasiconvex risk measures
We study a static portfolio optimization problem with two risk measures: a principle risk measure in the objective function and a secondary risk measure whose value is controlled in the constraints.
Pareto Active Learning with Gaussian Processes and Adaptive Discretization
We consider the problem of optimizing a vector-valued objective function $\boldsymbol{f}$ sampled from a Gaussian Process (GP) whose index set is a well-behaved, compact metric space $({\cal X}, d)$ of designs.
Set-Valued Risk Measures as Backward Stochastic Difference Inclusions and Equations
Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations.
Portfolio optimization with two coherent risk measures
We provide analytical results for a static portfolio optimization problem with two coherent risk measures.
Computation of systemic risk measures: a mixed-integer programming approach
Systemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system.
