Zobrazeno 1 - 10
of 247
pro vyhledávání: '"90c53"'
Penalty methods are a well known class of algorithms for constrained optimization. They transform a constrained problem into a sequence of unconstrained penalized problems in the hope that approximate solutions of the latter converge to a solution of
Externí odkaz:
http://arxiv.org/abs/2410.02188
We propose a new stochastic proximal quasi-Newton method for minimizing the sum of two convex functions in the particular context that one of the functions is the average of a large number of smooth functions and the other one is nonsmooth. The new m
Externí odkaz:
http://arxiv.org/abs/2409.16971
In recent years, variational quantum algorithms have garnered significant attention as a candidate approach for near-term quantum advantage using noisy intermediate-scale quantum (NISQ) devices. In this article we introduce kernel descent, a novel al
Externí odkaz:
http://arxiv.org/abs/2409.10257
Autor:
Yudin, Nikita, Hildebrand, Roland, Bakhurin, Sergey, Degtyarev, Alexander, Lisachenko, Anna, Kuruzov, Ilya, Semenov, Andrei, Alkousa, Mohammad
In this paper, we modify and apply the recently introduced Mixed Newton Method, which is originally designed for minimizing real-valued functions of complex variables, to the minimization of real-valued functions of real variables by extending the fu
Externí odkaz:
http://arxiv.org/abs/2407.20367
This paper explores a specific type of nonconvex sparsity-promoting regularization problems, namely those involving $\ell_p$-norm regularization, in conjunction with a twice continuously differentiable loss function. We propose a novel second-order a
Externí odkaz:
http://arxiv.org/abs/2407.17216
Autor:
Gurin, Nikita
This paper will discuss new ways of representing integers raised to integral powers as sums of smaller integral powers. The idea of expressing high powers in terms of lower powers turns out to have many interesting consequences and useful application
Externí odkaz:
http://arxiv.org/abs/2407.17121
In this paper, we aim to study non-convex minimization problems via second-order (in-time) dynamics, including a non-vanishing viscous damping and a geometric Hessian-driven damping. Second-order systems that only rely on a viscous damping may suffer
Externí odkaz:
http://arxiv.org/abs/2407.12518
Our approach is part of the close link between continuous dissipative dynamical systems and optimization algorithms. We aim to solve convex minimization problems by means of stochastic inertial differential equations which are driven by the gradient
Externí odkaz:
http://arxiv.org/abs/2407.04562
This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a novel non-convex algorithm, coined Hankel Structured Newton-Like Descent (
Externí odkaz:
http://arxiv.org/abs/2406.07409
Autor:
Müller, Johannes, Cayci, Semih
We study the error introduced by entropy regularization of infinite-horizon discrete discounted Markov decision processes. We show that this error decreases exponentially in the inverse regularization strength both in a weighted KL-divergence and in
Externí odkaz:
http://arxiv.org/abs/2406.04163