Zobrazeno 1 - 10
of 15 967
pro vyhledávání: '"Projected Gradient"'
We present a novel class of projected gradient (PG) methods for minimizing a smooth but not necessarily convex function over a convex compact set. We first provide a novel analysis of the "vanilla" PG method, achieving the best-known iteration comple
Externí odkaz:
http://arxiv.org/abs/2412.14291
Autor:
Otsuki, Yuta, Yagishita, Shotaro
This paper proposes a new approach using the stochastic projected gradient method and Malliavin calculus for optimal reinsurance and investment strategies. Unlike traditional methodologies, we aim to optimize static investment and reinsurance strateg
Externí odkaz:
http://arxiv.org/abs/2411.05417
This technical report introduces our top-ranked solution that employs two approaches, \ie suffix injection and projected gradient descent (PGD) , to address the TiFA workshop MLLM attack challenge. Specifically, we first append the text from an incor
Externí odkaz:
http://arxiv.org/abs/2412.15614
The Projected Gradient Descent (PGD) algorithm is a widely used and efficient first-order method for solving constrained optimization problems due to its simplicity and scalability in large design spaces. Building on recent advancements in the PGD al
Externí odkaz:
http://arxiv.org/abs/2412.07634
Recent advancements in diffusion models have been effective in learning data priors for solving inverse problems. They leverage diffusion sampling steps for inducing a data prior while using a measurement guidance gradient at each step to impose data
Externí odkaz:
http://arxiv.org/abs/2410.03463
Log-det semidefinite programming (SDP) problems are optimization problems that often arise from Gaussian graphic models. A log-det SDP problem with an l1-norm term has been examined in many methods, and the dual spectral projected gradient (DSPG) met
Externí odkaz:
http://arxiv.org/abs/2409.19743
This work investigates the performance limits of projected stochastic first-order methods for minimizing functions under the $(\alpha,\tau,\mathcal{X})$-projected-gradient-dominance property, that asserts the sub-optimality gap $F(\mathbf{x})-\min_{\
Externí odkaz:
http://arxiv.org/abs/2408.01839
Autor:
Atad, Matan, Gruber, Gabriel, Ribeiro, Marx, Nicolini, Luis Fernando, Graf, Robert, Möller, Hendrik, Nispel, Kati, Ezhov, Ivan, Rueckert, Daniel, Kirschke, Jan S.
Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional ca
Externí odkaz:
http://arxiv.org/abs/2408.06067
Adversarial attacks against deep learning models represent a major threat to the security and reliability of natural language processing (NLP) systems. In this paper, we propose a modification to the BERT-Attack framework, integrating Projected Gradi
Externí odkaz:
http://arxiv.org/abs/2407.21073
Autor:
Chen, Junren, Yuan, Ming
This paper provides a unified treatment to the recovery of structured signals living in a star-shaped set from general quantized measurements $\mathcal{Q}(\mathbf{A}\mathbf{x}-\mathbf{\tau})$, where $\mathbf{A}$ is a sensing matrix, $\mathbf{\tau}$ i
Externí odkaz:
http://arxiv.org/abs/2407.04951