Maximizing Monotone Submodular Functions over the Integer Lattice
Explore advanced algorithms for maximizing monotone submodular functions on integer lattices, with applications in machine learning and combinatorial optimization.
Andreas E. Feldmann - A -embedding of Low Highway Dimension Graphs into Bounded Treewidth Graphs
Explores embedding low highway dimension graphs into bounded treewidth graphs, with applications in transportation network optimization problems like Travelling Salesman and Facility Location.
Ahmad Abdi- Packing Odd T-Joins With at Most Two Terminals
Explore graph theory with Ahmad Abdi as he delves into packing odd T-joins, covering applications, proof strategies, and open questions in combinatorial optimization.
Reassembling Trees for the Traveling Salesman
Explore advanced approximation algorithms for traveling salesman problems, focusing on tree reassembly techniques to improve solutions beyond traditional methods.
Zoltán Szigeti: On -2k,k--connected Graphs
Exploration of (2k,k)-connected graphs, focusing on orientation and construction methods. Covers arc-connectivity, vertex-connectivity, and splitting-off techniques for various graph structures.
On LP-Relaxations for the Tree Augmentation Problem
Explore LP-relaxations for tree augmentation, including dual-fitting algorithms, shadow-minimal solutions, and blocking trees. Gain insights into improved approximation techniques for combinatorial optimization problems.
On Element Connectivity Preserving Graph Simplification
Explores element-connectivity in graph simplification, focusing on reduction techniques, algorithmic results, and applications in network design. Highlights open problems and submodularity properties for faster algorithms.
The Short-Path Algorithm for Combinatorial Optimization
Explore the Short-path Algorithm's applications in combinatorial optimization, focusing on quantum computation challenges and potential solutions.
Decision-Focused Learning - Integrating Downstream Combinatorics in ML
Explore the integration of machine learning and discrete optimization to enhance problem-solving in real-world scenarios, focusing on decision-focused learning and tailored solution strategies.
Fast Semidefinite Programming for Differentiable Combinatorial Optimization
Explore advanced techniques in semidefinite programming for combinatorial optimization, focusing on fast and differentiable methods presented by Carnegie Mellon expert Zico Kolter.
Structured ML Training via Conditional Gradients
Explore conditional gradient methods for structured optimization in machine learning, covering applications in traditional and deep learning, with a focus on incorporating polyhedral structures into solutions.
Polyhedral Techniques in Combinatorial Optimization: Matchings and Tours
Explore recent advancements in combinatorial optimization, focusing on matching and traveling salesman problems. Learn about polyhedral techniques and linear programming formulations for designing efficient algorithms.
Noriyoshi Sukegawa- On the Diameter of Polyhedra and Related Topics
Explore polyhedra diameter and its impact on simplex method complexity. Overview previous findings, recent results, and open questions in this mathematical field.
Performance of Steepest Descent in 0/1 LPs
Explores steepest descent in 0/1 LPs, proving polynomial-length paths and developing a new pivot rule for the Simplex method. Unifies circuit and edge steps, advancing understanding of combinatorial optimization.
Approximation Algorithms for Hard Augmentation Problems - Lecture II
Explore advanced approximation algorithms for network design, focusing on connectivity augmentation problems and recent developments in combinatorial optimization techniques.
Comparing Polyhedral Relaxations via Volume
Explore polyhedral relaxations in combinatorial optimization through volume calculations, examining fixed-charge problems and trilinear monomials for practical spatial branch-and-bound implementation guidance.
Panorama of Mathematics - A Panoramic Tour Through Combinatorial Optimization
Explore combinatorial optimization with Michel Goemans in this insightful lecture, covering new trends, results, and challenges in mathematical sciences.
Extensions and Ramifications of Discrete Convexity Concepts
Explore discrete convexity concepts, including L-convex and M-convex functions, their properties, and related notions in combinatorial optimization and discrete mathematics.
Non TDI Optimization with Supermodular Functions
Explores supermodular covering theorem's applications in non-TDI graph optimization, including degree-constrained problems, connectivity augmentation, and extensions of disjoint arborescences theorem.
Combinatorial Polynomial Algorithms for Skew Bisubmodular Function Minimization
Explores combinatorial polynomial algorithms for skew-bisubmodular function minimization, extending previous work on bisubmodular functions and introducing new concepts in graph theory and optimization.
Linear Matroid Matching in the Oracle Model
Explores linear matroid matching in the oracle model, replacing linear algebraic computation with repeated oracle-calls along alternating paths to solve challenging problems in connectivity, rigidity, and count matroids.