Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods
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Publication:6120850
DOI10.1007/s10107-023-01973-1arXiv2203.07305OpenAlexW4379652848MaRDI QIDQ6120850
Ernest K. Ryu, Bart P. G. Van Parys, Shuvomoy Das Gupta
Publication date: 21 February 2024
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2203.07305
Analysis of algorithms and problem complexity (68Q25) Convex programming (90C25) Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57) Nonlinear programming (90C30)
Cites Work
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- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Optimized first-order methods for smooth convex minimization
- Smooth strongly convex interpolation and exact worst-case performance of first-order methods
- The exact information-based complexity of smooth convex minimization
- Enhancing RLT relaxations via a new class of semidefinite cuts
- Lectures on convex optimization
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Convex analysis and nonlinear optimization. Theory and examples.
- On the convergence rate of the Halpern-iteration
- The Boolean quadratic polytope: Some characteristics, facets and relatives
- Information-based complexity of linear operator equations
- Solving problems with semidefinite and related constraints using interior-point methods for nonlinear programming
- Exact worst-case convergence rates of the proximal gradient method for composite convex minimization
- On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions
- Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions
- On the oracle complexity of smooth strongly convex minimization
- Optimal complexity and certification of Bregman first-order methods
- Efficient first-order methods for convex minimization: a constructive approach
- Generalized monotone operators and their averaged resolvents
- Accelerated proximal point method for maximally monotone operators
- Performance of first-order methods for smooth convex minimization: a novel approach
- On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
- The exact worst-case convergence rate of the gradient method with fixed step lengths for \(L\)-smooth functions
- A Hierarchy of Relaxations between the Continuous and Convex Hull Representations for Zero-One Programming Problems
- Tight Sublinear Convergence Rate of the Proximal Point Algorithm for Maximal Monotone Inclusion Problems
- Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints
- Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems
- Another Look at the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA)
- Stochastic Model-Based Minimization of Weakly Convex Functions
- Accuracy and Stability of Numerical Algorithms
- MathOptInterface: A Data Structure for Mathematical Optimization Problems
- Worst-Case Convergence Analysis of Inexact Gradient and Newton Methods Through Semidefinite Programming Performance Estimation
- Operator Splitting Performance Estimation: Tight Contraction Factors and Optimal Parameter Selection
- Exact Worst-Case Performance of First-Order Methods for Composite Convex Optimization
- Line Search Filter Methods for Nonlinear Programming: Local Convergence
- JuMP: A Modeling Language for Mathematical Optimization
- On Richardson's Method for Solving Linear Systems with Positive Definite Matrices
- An optimal gradient method for smooth strongly convex minimization
- Factor-\(\sqrt{2}\) acceleration of accelerated gradient methods
- Principled analyses and design of first-order methods with inexact proximal operators
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