@article{rogozin2023decentralized,

title = {Decentralized saddle point problems via non-Euclidean mirror prox},

author = {Alexander Rogozin and Alexander Beznosikov and Darina Dvinskikh and Dmitry Kovalev and Pavel Dvurechensky and Alexander Gasnikov},

doi = {10.1080/10556788.2023.2280062},

year  = {2024},

date = {2024-01-01},

journal = {Optimization Methods and Software},

volume = {0},

number = {0},

pages = {1-26},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1080/00029890.2023.2263299,

title = {The Discrete Theorema Egregium},

author = {Thomas F. Banchoff and Felix Günther},

url = {https://doi.org/10.1080/00029890.2023.2263299},

doi = {10.1080/00029890.2023.2263299},

year  = {2024},

date = {2024-01-01},

journal = {The American Mathematical Monthly},

volume = {131},

number = {1},

pages = {30-47},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dvurechensky2023near-optimal,

title = {Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal-dual tensor methods},

author = {Pavel Dvurechensky and Petr Ostroukhov and Alexander Gasnikov and César A. Uribe and Anastasiya Ivanova},

url = {https://doi.org/10.1080/10556788.2023.2296443},

doi = {10.1080/10556788.2023.2296443},

year  = {2024},

date = {2024-01-01},

journal = {Optimization Methods and Software},

volume = {0},

number = {0},

pages = {1-36},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dvurechensky2024hessian,

title = {Hessian barrier algorithms for non-convex conic optimization},

author = {Pavel Dvurechensky and Mathias Staudigl},

url = {https://doi.org/10.1007/s10107-024-02062-7},

doi = {10.1007/s10107-024-02062-7},

year  = {2024},

date = {2024-01-01},

journal = {Mathematical Programming},

note = {(accepted)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hanu_2023,

title = {Subsampling in ensemble Kalman inversion},

author = {Matei Hanu and Jonas Latz and Claudia Schillings},

url = {https://dx.doi.org/10.1088/1361-6420/ace64b},

doi = {10.1088/1361-6420/ace64b},

year  = {2023},

date = {2023-07-01},

journal = {Inverse Problems},

volume = {39},

number = {9},

pages = {094002},

publisher = {IOP Publishing},

abstract = {We consider the ensemble Kalman inversion (EKI) which has been recently introduced as an efficient, gradient-free optimisation method to estimate unknown parameters in an inverse setting. In the case of large data sets, the EKI becomes computationally infeasible as the data misfit needs to be evaluated for each particle in each iteration. Here, randomised algorithms like stochastic gradient descent have been demonstrated to successfully overcome this issue by using only a random subset of the data in each iteration, so-called subsampling techniques. Based on a recent analysis of a continuous-time representation of stochastic gradient methods, we propose, analyse, and apply subsampling-techniques within EKI. Indeed, we propose two different subsampling techniques: either every particle observes the same data subset (single subsampling) or every particle observes a different data subset (batch subsampling).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PolyAuct,

title = {The Polyhedral Geometry of Truthful Auctions},

author = {Michael Joswig and Max Klimm and Sylvain Spitz},

url = {https://arxiv.org/abs/2211.01907},

year  = {2023},

date = {2023-05-22},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bougetof:FCAA2023,

title = {On the existence and uniqueness of the solution to multifractional stochastic delay differential equation},

author = {K. Bouguetof and Z. Mezdoud and O. Kebiri and C. Hartmann},

url = {https://www.math.tu-cottbus.de/INSTITUT/lswr/downloads/bib/mfBM.pdf},

year  = {2023},

date = {2023-05-10},

journal = {Fract. Calc. Appl. Anal.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Saci:PMS2023,

title = {Fractional stochastic differential equations driven by G-Brownian motion with delays},

author = {A. Saci and A. Redjil and H. Boutabia and O. Kebiri},

url = {https://www.sciencedirect.com/science/article/pii/S2405896321021194},

year  = {2023},

date = {2023-04-13},

journal = {Probab. Math. Stat.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Symmetry,

title = {Exploiting symmetry in variational quantum machine learning},

author = {J. J. Meyer and M. Mularski and E. Gil-Fuster and A. Anna Mele and F. Arzani and A. Wilms and J. Eisert},

year  = {2023},

date = {2023-03-17},

note = {arXiv:2205.06217},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{del2022chemical,

title = {Chemical diffusion master equation: formulations of reaction–diffusion processes on the molecular level},

author = {Mauricio J Razo and Stefanie Winkelmann and Rupert Klein and Felix Höfling},

year  = {2023},

date = {2023-01-20},

journal = {Cascades of Scales: Applications and Mathematical Methodologies},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Entwistle2022,

title = {Electronic excited states in deep variational Monte Carlo},

author = {Mike Entwistle and Zeno Schätzle and Paolo A Erdman and Jan Hermann and Frank Noé},

doi = {10.1038/s41467-022-35534-5},

year  = {2023},

date = {2023-01-17},

urldate = {2022-01-01},

journal = {Nature Communications},

volume = {14},

number = {1},

abstract = {Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the Schrödinger equation that scales favorably for large systems is variational quantum Monte Carlo (QMC). The recently introduced deep QMC approach uses ansatzes represented by deep neural networks and generates nearly exact ground-state solutions for molecules containing up to a few dozen electrons, with the potential to scale to much larger systems where other highly accurate methods are not feasible. In this paper, we extend one such ansatz (PauliNet) to compute electronic excited states. We demonstrate our method on various small atoms and molecules and consistently achieve high accuracy for low-lying states. To highlight the method’s potential, we compute the first excited state of the much larger benzene molecule, as well as the conical intersection of ethylene, with PauliNet matching results of more expensive high-level methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{thiesb,

title = {Modelling altered signalling of G-protein coupled receptors in inflamed environment to advance drug design},

author = {Arne Thies and Vikram Sunkara and Sourav Ray and Hanna Wulkow and M. Özgür Celik and Fatih Yergöz and Christof Schütte and Christoph Stein and Marcus Weber and Stefanie Winkelmann},

year  = {2023},

date = {2023-01-12},

journal = {Scientific Reports},

keywords = {},

pubstate = {published},

tppubtype = {article}

}