#convex optimization
13 results$L^0$--convex compactness and its applications to random convex optimization and random variational inequalities
Tiexin Guo, Erxin Zhang, Yachao Wang +1
The paper introduces and studies $L^0$‑convex compactness for $L^0$‑convex subsets of topological modules over the algebra of random variables, provides characterization theorems (…
Optimization-based Settingless Algorithm Combining Protection and Fault Identification
Nadezhda Davydova, Dmitry Shchetinin, Gabriela Hug
The paper proposes a settingless time‑domain protection algorithm for medium‑voltage lines that uses small convex optimization problems to decide whether the line is healthy or fau…
Lifting methods for manifold-valued variational problems
Thomas Vogt, Evgeny Strekalovskiy, Daniel Cremers +1
The paper reviews and extends lifting techniques that turn difficult manifold-valued variational problems, such as segmentation and optical flow, into convex formulations that can…
Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence under Bregman Distance Growth Conditions
Hui Zhang, Yu-Hong Dai, Lei Guo +1
The paper proposes a unified proximal-like incremental aggregated gradient (PLIAG) framework for convex optimization that achieves sublinear and linear convergence under Bregman di…
CvxPnPL: A Unified Convex Solution to the Absolute Pose Estimation Problem from Point and Line Correspondences
Sérgio Agostinho, João Gomes, Alessio Del Bue
The paper introduces a convex optimization framework that estimates a camera's 3D pose from any mix of 2D‑3D point and line correspondences by formulating the problem as a QCQP and…
The Chen-Teboulle algorithm is the proximal point algorithm
Stephen Becker
The paper reexamines the Chen‑Teboulle algorithm in light of recent developments and demonstrates that it can be interpreted as a proximal point method, leading to an improved boun…
Convex programs for minimal-area problems
Matthew Headrick, Barton Zwiebach
The paper formulates the minimal‑area problem for Riemann surfaces—requiring all non‑contractible curves to have length ≥ 2π—as a local convex optimization problem using calibratio…
Implementing Convex Optimization in R: Two Econometric Examples
Zhan Gao, Zhentao Shi
The paper demonstrates how to implement high‑dimensional convex optimization in R using the MOSEK solver, illustrating the approach with two econometric estimation examples and sho…
On semi-infinite systems of convex polynomial inequalities and polynomial
Feng Guo, Xiaoxia Sun
The paper studies semi‑infinite systems of convex polynomial inequalities and proposes a method to build approximate semidefinite representations of the feasible set, then uses the…
Max-Min Fairness Design for MIMO Interference Channels: a Minorization-Maximization Approach
Mohammad Mahdi Naghsh, Maryam Masjedi, Arman Adibi +1
The paper proposes an efficient algorithm using minorization-maximization to design linear precoders for MIMO interference channels that achieve max‑min fairness among users, even…
A Push-Pull Gradient Method for Distributed Optimization in Networks
Shi Pu, Wei Shi, Jinming Xu +1
The paper proposes a push‑pull gradient algorithm for solving convex optimization problems over a network, where each node exchanges decision variables and gradient information wit…
A Distributed Stochastic Gradient Tracking Method
Shi Pu, Angelia NediÄ
The paper proposes a distributed stochastic gradient tracking algorithm for multi‑agent convex optimization, showing that agents’ iterates converge exponentially fast to a neighbor…
AsySPA: An Exact Asynchronous Algorithm for Convex Optimization Over Digraphs
Jiaqi Zhang, Keyou You
The paper introduces AsySPA, an exact asynchronous distributed subgradient-push algorithm that solves convex optimization problems over directed graphs, allowing nodes to update at…