Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Sharma, Kartikey"'
Autor:
Paul, Kaushik, Maurya, Akash, Henry, Quentin, Sharma, Kartikey, Satheesh, Pranav, Divyajyoti, Kumar, Prayush, Mishra, Chandra Kant
We present a time-domain inspiral-merger-ringdowm (IMR) waveform model ESIGMAHM constructed within a framework we named ESIGMA for coalescing binaries of spinning black holes on moderately eccentric orbits (Huerta et al. (2018) [Phys. Rev. D 97, 0240
Externí odkaz:
http://arxiv.org/abs/2409.13866
For optimization models to be used in practice, it is crucial that users trust the results. A key factor in this aspect is the interpretability of the solution process. A previous framework for inherently interpretable optimization models used decisi
Externí odkaz:
http://arxiv.org/abs/2409.01869
In this paper, we consider a finite dimensional optimization problem minimizing a continuous objective on a compact domain subject to a multi-dimensional constraint function. For the latter, we only assume the availability of a Lipschitz property. In
Externí odkaz:
http://arxiv.org/abs/2403.11546
We tackle the network design problem for centralized traffic assignment, which can be cast as a mixed-integer convex optimization (MICO) problem. For this task, we propose different formulations and solution methods in both a deterministic and a stoc
Externí odkaz:
http://arxiv.org/abs/2402.00166
Autor:
Aigner, Kevin-Martin, Bärmann, Andreas, Braun, Kristin, Liers, Frauke, Pokutta, Sebastian, Schneider, Oskar, Sharma, Kartikey, Tschuppik, Sebastian
Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters. As the latter is often unknown, Distributionally Robust Optimization
Externí odkaz:
http://arxiv.org/abs/2304.05377
We propose an interactive multi-agent classifier that provides provable interpretability guarantees even for complex agents such as neural networks. These guarantees consist of lower bounds on the mutual information between selected features and the
Externí odkaz:
http://arxiv.org/abs/2206.00759
Autor:
Nohadani, Omid, Sharma, Kartikey
Publikováno v:
INFORMS Journal on Optimization (2022)
Robust optimization methods have shown practical advantages in a wide range of decision-making applications under uncertainty. Recently, their efficacy has been extended to multi-period settings. Current approaches model uncertainty either independen
Externí odkaz:
http://arxiv.org/abs/2202.10602
Autor:
Nohadani, Omid, Sharma, Kartikey
Publikováno v:
SIAM Journal on Optimization (2018)
The efficacy of robust optimization spans a variety of settings with uncertainties bounded in predetermined sets. In many applications, uncertainties are affected by decisions and cannot be modeled with current frameworks. This paper takes a step tow
Externí odkaz:
http://arxiv.org/abs/1611.07992
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Akademický článek
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