173 research outputs found
A distributionally robust perspective on uncertainty quantification and chance constrained programming
The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability. A more ambitious goal is to actively influence the system so as to guarantee and maintain its safety, a scenario which can be modeled through a chance constrained program. In this paper we assume that the parameters of the system are governed by an ambiguous distribution that is only known to belong to an ambiguity set characterized through generalized moment bounds and structural properties such as symmetry, unimodality or independence patterns. We delineate the watershed between tractability and intractability in ambiguity-averse uncertainty quantification and chance constrained programming. Using tools from distributionally robust optimization, we derive explicit conic reformulations for tractable problem classes and suggest efficiently computable conservative approximations for intractable ones
Computing semiparametric bounds on the expected payments of insurance instruments via column generation
It has been recently shown that numerical semiparametric bounds on the
expected payoff of fi- nancial or actuarial instruments can be computed using
semidefinite programming. However, this approach has practical limitations.
Here we use column generation, a classical optimization technique, to address
these limitations. From column generation, it follows that practical univari-
ate semiparametric bounds can be found by solving a series of linear programs.
In addition to moment information, the column generation approach allows the
inclusion of extra information about the random variable; for instance,
unimodality and continuity, as well as the construction of corresponding
worst/best-case distributions in a simple way
Distributionally Robust Optimization: A Review
The concepts of risk-aversion, chance-constrained optimization, and robust
optimization have developed significantly over the last decade. Statistical
learning community has also witnessed a rapid theoretical and applied growth by
relying on these concepts. A modeling framework, called distributionally robust
optimization (DRO), has recently received significant attention in both the
operations research and statistical learning communities. This paper surveys
main concepts and contributions to DRO, and its relationships with robust
optimization, risk-aversion, chance-constrained optimization, and function
regularization
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