Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Congying Qin, Yunhai Xiao, Peili Li
The linearly constrained convex composite programming problems whose objective function contains two blocks with each block being the form of nonsmooth+smooth arises frequently in multiple fields of applications. If both of the smooth terms are quadratic, this problem can be solved efficiently by using the symmetric…
Hao Yu, Michael J. Neely
This paper considers large scale constrained convex (possibly composite and nonseparable) programs, which are usually difficult to solve by interior point methods or other Newtontype methods due to the non-smoothness or the prohibitive computation and storage complexity for Hessians and matrix inversions. Instead, they…
James V. Burke, Tim Hoheisel, Quang Van Nguyen
In this note we provide a full conjugacy and subdifferential calculus for convex convex-composite functions in finite-dimensional space. Our approach, based on infimal convolution and cone-convexity, is straightforward and yields the desired results under a verifiable Slater-type condition, with relaxed monotonicity…
James V. Burke, Abraham Engle
This work concerns the local convergence theory of Newton and quasi-Newton methods for convex-composite optimization: minimize f(x) := h(c(x)), where h is an infinite-valued proper convex function and c is C 2 -smooth. We focus on the case where h is infinite-valued piecewise linear-quadratic and convex. Such problems…
James V. Burke, Abraham Engle
We consider descent methods for solving non-finite valued nonsmooth convexcomposite optimization problems that employ Gauss-Newton subproblems to determine the iteration update. Specifically, we establish the global convergence properties for descent methods that use a backtracking line search, a weak Wolfe line…
Nikita Doikov, Yurii Nesterov
In this paper, we propose a new Fully Composite Formulation of convex optimization problems. It includes, as a particular case, the problems with functional constraints, max-type minimization problems, and problems of Composite Minimization, where the objective can have simple nondifferentiable components. We treat all…
Nikita Doikov, Yurii Nesterov
In this paper, we study local convergence of high-order Tensor Methods for solving convex optimization problems with composite objective. We justify local superlinear convergence under the assumption of uniform convexity of the smooth component, having Lipschitz-continuous high-order derivative. The convergence both in…
Axel Böhm, Stephen J. Wright
We study minimization of a structured objective function, being the sum of a smooth function and a composition of a weakly convex function with a linear operator. Applications include image reconstruction problems with regularizers that introduce less bias than the standard convex regularizers. We develop a variable…
Nikita Doikov, Yurii Nesterov
In this paper, we study the iteration complexity of cubic regularization of Newton method for solving composite minimization problems with uniformly convex objective. We introduce the notion of second-order condition number of a certain degree and justify the linear rate of convergence in a nondegenerate case for the…
Jing Liu, Yongrui Duan, Min Sun
This paper introduces a symmetric version of the generalized alternating direction method of multipliers for two-block separable convex programming with linear equality constraints, which inherits the superiorities of the classical alternating direction method of multipliers (ADMM), and which extends the feasible set…
Gyeongcheol Cho, Heungsun Hwang
Generalized structured component analysis (GSCA) is a multivariate method for examining theory-driven relationships between variables including components. GSCA can provide the deterministic component score for each individual once model parameters are estimated. As the traditional GSCA always standardizes all…
Zhiyuan Lin, Raymond S. K. Kwan
Based on previous work in rolling stock scheduling problems (Alfieri et al. in Transp Sci 40:378-391, [2]; Cacchiani et al. in Math Progr B 124:207-231, [6]; Lin and Kwan in Electron Notes Discret Math 41:165-172, [14]; Schrijver in CWI Q 6:205-217, [23]; Ziarati et al. in Manag Sci 45:1156-1168, [29]), we generalize a…
Richard D. Paul, Johann F. Jadebeck, Anton Stratmann, Wolfgang Wiechert + 1 more
Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models…
Radu Ioan Boţ, Ernö Robert Csetnek
In this paper, we propose two proximal-gradient algorithms for fractional programming problems in real Hilbert spaces, where the numerator is a proper, convex and lower semicontinuous function and the denominator is a smooth function, either concave or convex. In the iterative schemes, we perform a proximal step with…