Zobrazeno 1 - 10
of 935
pro vyhledávání: '"90c46"'
This paper studies reinforcement learning (RL) in infinite-horizon dynamic decision processes with almost-sure safety constraints. Such safety-constrained decision processes are central to applications in autonomous systems, finance, and resource man
Externí odkaz:
http://arxiv.org/abs/2411.19193
Autor:
Fersztand, David, Sun, Xu Andy
The proximal bundle algorithm (PBA) is a fundamental and computationally effective algorithm for solving optimization problems with nonsmooth components. We investigate its convergence rate, focusing on composite settings where one function is smooth
Externí odkaz:
http://arxiv.org/abs/2411.15926
Autor:
Mceowen, Skye, Calderone, Daniel J., Tiwary, Aman, Zhou, Jason S. K., Kim, Taewan, Elango, Purnanand, Acikmese, Behcet
This paper presents auto-tuned primal-dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically-feasible trajectory solutions for constrained hypersonic reentry optimal control problems across a large mission
Externí odkaz:
http://arxiv.org/abs/2411.08361
Autor:
Yang, Meijia, Xia, Yong
The generalized trace ratio problem {\rm (GTRP)} is to maximize a quadratic fractional objective function in trace formulation over the Stiefel manifold. In this paper, based on a newly developed matrix S-lemma, we show that {\rm (GTRP)}, if a redund
Externí odkaz:
http://arxiv.org/abs/2411.09187
Autor:
Tang, Tianyun, Toh, Kim-Chuan
In this paper, we study linearly constrained optimization problems (LCP). After applying Hadamard parametrization, the feasible set of the parametrized problem (LCPH) becomes an algebraic variety, with conducive geometric properties which we explore
Externí odkaz:
http://arxiv.org/abs/2410.23874
Autor:
Hsieh, Chung-Han, Yu, Xiao-Rou
This paper addresses a novel \emph{cost-sensitive} distributionally robust log-optimal portfolio problem, where the investor faces \emph{ambiguous} return distributions, and a general convex transaction cost model is incorporated. The uncertainty in
Externí odkaz:
http://arxiv.org/abs/2410.23536
Autor:
Ju, Caleb, Lan, Guanghui
This paper proposes a novel termination criterion, termed the advantage gap function, for finite state and action Markov decision processes (MDP) and reinforcement learning (RL). By incorporating this advantage gap function into the design of step si
Externí odkaz:
http://arxiv.org/abs/2409.19437
In this paper, we study the local-nonglobal minimizers of the Generalized Trust-Region subproblem $(GTR)$ and its Equality-constrained version $(GTRE)$. Firstly, the equivalence is established between the local-nonglobal minimizers of both $(GTR)$ an
Externí odkaz:
http://arxiv.org/abs/2409.01697
Autor:
Lahiri, Somdeb
Our first result is a statement of a somewhat general form of a non-substitution theorem for linear programming problems, along with a very easy proof of the same. Subsequently, we provide an easy proof of theorem 1 in a 1979 paper of Olvi L. Mangasa
Externí odkaz:
http://arxiv.org/abs/2408.14150
In this paper, we study the Aubin property of the Karush-Kuhn-Tucker solution mapping for the nonlinear semidefinite programming (NLSDP) problem at a locally optimal solution. In the literature, it is known that the Aubin property implies the constra
Externí odkaz:
http://arxiv.org/abs/2408.08232