15 papers · ranked by Valyu relevance
Samet Uzun, Dayou Luo, Behçet Açıkmeşe, Aleksandr Y. Aravkin
We introduce prox-convex for minimizing F(x) = g(x) + h(C(x)) + s(R(x)), where g and h are convex, C and s are smooth, and each component of R is convex (possibly nonsmooth). Here g captures general convex objectives and indicator functions for convex constraints, while the composite template simultaneously models…
Leandro Farias Maia, Robert Baraldi, Drew Kouri
In [R. J. Baraldi and D. P. Kouri, Math. Program., 201:1 (2023), pp. 559-598], the authors introduced a trust-region method for minimizing the sum of a smooth nonconvex and a nonsmooth convex function, the latter of which has an analytical proximity operator. While many functions satisfy this criterion, e.g., the ℓ 1…
Sunbochen Tang, Andrea Goertzen, Navid Azizan
Linear matrix inequalities (LMIs) have played a central role in certifying stability, robustness, and forward invariance of dynamical systems. Despite rapid development in learning-based methods for control design and certificate synthesis, existing approaches often fail to preserve the hard matrix inequality…
Radu Ioan Boţ, Dang-Khoa Nguyen, Chunxiang Zong
In this paper, we derive a Fast Reflected Forward-Backward (Fast RFB) algorithm to solve the problem of finding a zero of the sum of a maximally monotone operator and a monotone and Lipschitz continuous operator in a real Hilbert space. Our approach extends the class of reflected forward-backward methods by introducing…
Nicholas Di, Eric C. Chi, Samy Wu Fung
Operator splitting algorithms are a cornerstone of modern first-order optimization, decomposing complex problems into simpler subproblems solved via proximal operators. However, most functions lack closed-form proximal operators, which has long restricted these methods to a narrow set of problems. Hamilton-Jacobi-based…
José Márcio Machado de Brito, Felipe Lara, Tran Van Thang
This paper addresses the minimization of a finite sum of proxconvex functions under Lipschitz continuity of each component. We propose two variants of the splitting proximal point algorithms proposed in [2, 5]: one deterministic with a fixed update order, and one stochastic with random sampling, and we extend them from…
Sören Bartels, Alex Kaltenbach
In this paper, we devise a $\operatorname{prox}$-based semi-smooth Newton method that is applicable to a finite element discretization of a broad class of nonsmooth convex variational problems, including the TV-minimization problem, the $p$-Dirichlet problem, the obstacle problem, and the elasto-plastic torsion…
Theodoros Anagnostopoulos, Evanthia Zervoudi, Christos Anagnostopoulos, Apostolos Christopoulos + 1 more
Linear regression analysis focuses on predicting a numeric regressand value based on certain regressor values. In this context, k-Nearest Neighbors (k-NN) is a common non-parametric regression algorithm, which achieves efficient performance when compared with other algorithms in literature. In this research effort an…
Anas Abdelkarim, Daniel Görges, Holger Voos
Factor graph optimization serves as a fundamental framework for robotic perception, enabling applications such as pose estimation, simultaneous localization and mapping (SLAM), structure-from-motion (SfM), and situational modeling. Traditionally, these methods solve unconstrained least squares problems using algorithms…
Mahmudur Rahman Hera, David Koslicki, Conrado Martínez
With the surge in sequencing data generated from an ever-expanding range of biological studies, designing scalable computational techniques has become essential. One effective strategy to enable large-scale computation is to split long DNA or protein sequences into k-mers, and summarize large k-mer sets into compact…
Thang V Pham, Chau TM Tran, Alex A Henneman, Long HC Pham + 5 more
Current methods for protein level quantification in mass spectrometry-based proteomics do not scale with the increasing number of samples because of limited system memory and algorithmic complexities. Here we propose a new data structure that supports parsing of input as data stream, improve state of the art…
Amir Hossein Salehi Shayegan
Time-fractional diffusion equations have emerged as powerful models for describing anomalous transport phenomena in physics, biology and engineering. To address the computational challenges arising from their non-local operators, we employ the WEB-spline finite element method, which provides a flexible and accurate…
Shreeharsha G Bhat, Daanish Mahajan, Chirag Jain
A key application of pangenome graphs is the characterization of small and large genomic variants represented as bubbles within the graph. Although bubbles have been extensively studied in directed graphs in the context of genome assembly, there remains a need for a rigorous definition and systematic analysis of…
Ke Chen, Abhishek Talesara, Sanchal Thakkar, Mingfu Shao
The minimum flow decomposition problem abstracts a set of key tasks in bioinformatics, including metagenome and transcriptome assembly. These tasks, collectively known as multi-assembly, aim to reconstruct multiple genomic sequences from reads obtained from mixed samples. The reads are first organized into a directed…
Xuan Lin
This paper presents a comparative study of data-driven acceleration techniques for mixed-integer bilinear programs (MIBLPs) applied to robot motion planning. MIBLPs combine discrete decision variables and nonlinear constraints, making them computationally challenging for real-time robotics applications. We investigate…