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This paper presents a novel restarted version of Nesterov's accelerated gradient method and establishes its optimal iteration-complexity for solving convex smooth composite optimization problems. The proposed restart accelerated gradient…

Optimization and Control · Mathematics 2025-01-09 Jiaming Liang

In this article, we propose an algorithm for the nonlinear conjugate gradient method to find a Pareto critical point of unconstrained multiobjective interval optimization problems. In this algorithm, we use the Wolfe line search procedure…

Optimization and Control · Mathematics 2026-03-09 Tapas Mondal , Debdas Ghosh , Jingxin Liu , Jie Li

Employing the ideas of non-linear preconditioning and testing of the classical proximal point method, we formalise common arguments in convergence rate and convergence proofs of optimisation methods to the verification of a simple…

Optimization and Control · Mathematics 2020-10-06 Tuomo Valkonen

In this paper we address the numerical solution of the quadratic optimal transport problem in its dynamical form, the so-called Benamou-Brenier formulation. When solved using interior point methods, the main computational bottleneck is the…

Numerical Analysis · Mathematics 2024-01-22 Enrico Facca , Gabriele Todeschi , Andrea Natale , Michele Benzi

Variable order structures model situations in which the comparison between two points depends on a point-to-cone map. In this paper, an inexact projected gradient method for solving smooth constrained vector optimization problems on…

Optimization and Control · Mathematics 2019-08-09 Jose Yunier Bello Cruz , Gemayqzel Bouza Allende

The first order condition of the constrained minimization problem leads to a saddle point problem. A multigrid method using a multiplicative Schwarz smoother for saddle point problems can thus be interpreted as a successive subspace…

Numerical Analysis · Mathematics 2016-01-19 Long Chen

Motivated by the theory of self-duality which provides a variational formulation and resolution for non self-adjoint partial differential equations \cite{G1, G2}, we propose new templates for solving large non-symmetric linear systems. The…

Numerical Analysis · Mathematics 2008-01-28 Nassif Ghoussoub , Amir Moradifam

A trajectory-following primal--dual interior-point method solves nonlinear optimization problems with inequality and equality constraints by approximately finding points satisfying perturbed Karush--Kuhn--Tucker optimality conditions for a…

Optimization and Control · Mathematics 2025-07-03 Pim Heeman , Anders Forsgren

This note proposes an efficient preconditioner for solving linear and semi-linear parabolic equations. With the Crank-Nicholson time stepping method, the algebraic system of equations at each time step is solved with the conjugate gradient…

Numerical Analysis · Mathematics 2021-05-11 Jordi Feliu-Fabà , Lexing Ying

We propose conditioning field initialization for neural network based topology optimization. In this work, we focus on (1) improving upon existing neural network based topology optimization, (2) demonstrating that by using a prior initial…

Machine Learning · Computer Science 2023-05-19 Hongrui Chen , Aditya Joglekar , Levent Burak Kara

We develop an interior-point method for nonsmooth regularized bound-constrained optimization problems. Our method consists of iteratively solving a sequence of unconstrained nonsmooth barrier subproblems. We use a variant of the proximal…

Optimization and Control · Mathematics 2024-02-29 Geoffroy Leconte , Dominique Orban

Equilibrium equations in the form of complementarity conditions often appear as constraints in optimization problems. Problems of this type are commonly referred to as mathematical programs with complementarity constraints (MPCCs). A…

Optimization and Control · Mathematics 2025-10-20 Sven Leyffer

Topology optimization problems generally support multiple local minima, and real-world applications are typically three-dimensional. In previous work [I. P. A. Papadopoulos, P. E. Farrell, and T. M. Surowiec, Computing multiple solutions of…

Numerical Analysis · Mathematics 2022-11-23 Ioannis P. A. Papadopoulos , Patrick E. Farrell

In this paper, we propose a conditional gradient method for solving constrained vector optimization problems with respect to a partial order induced by a closed, convex and pointed cone with nonempty interior. When the partial order under…

Optimization and Control · Mathematics 2022-04-12 Wang Chen , Xinmin Yang , Yong Zhao

Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are…

Recently, saddle point problems have received much attention due to their powerful modeling capability for a lot of problems from diverse domains. Applications of these problems occur in many applied areas, such as robust optimization,…

Optimization and Control · Mathematics 2022-02-15 Mohammad Alkousa , Alexander Gasnikov , Pavel Dvurechensky , Abdurakhmon Sadiev , Lama Razouk

The discontinuous Petrov-Galerkin (DPG) methodology of Demkowicz and Gopalakrishnan [15,17] guarantees the optimality of the solution in an energy norm, and provides several features facilitating adaptive schemes. A key question that has…

Numerical Analysis · Mathematics 2016-08-09 Nathan V. Roberts , Jesse Chan

In this work, we consider the modeling of inclusions in the material using an unfitted finite element method. In the unfitted methods, structured background meshes are used and only the underlying finite element space is modified to…

Numerical Analysis · Mathematics 2021-03-26 Hardik Kothari , Rolf Krause

We present a level-set based topology optimization algorithm for design optimization problems involving an arbitrary number of different materials, where the evolution of a design is solely guided by topological derivatives. Our method can…

Optimization and Control · Mathematics 2020-06-24 Peter Gangl

Gradient-free optimizers allow for tackling problems regardless of the smoothness or differentiability of their objective function, but they require many more iterations to converge when compared to gradient-based algorithms. This has made…

Machine Learning · Computer Science 2024-09-24 Gawel Kus , Miguel A. Bessa