Introduction to Parameterized Algorithms, Lecture I
Gentle introduction to parameterized complexity, focusing on basic techniques like branching, color coding, and kernelization. Explores connections to linear programming and discrete optimization.
Michal Pilipczuk: Introduction to Parameterized Algorithms, Lecture II
Explore parameterized complexity, focusing on LP-related methods. Learn branching, color coding, kernelization, and dynamic programming techniques for algorithm design and optimization.
Computational Complexity in Theory and in Practice by Richard M. Karp
Explore the contrast between theoretical and practical approaches to algorithm efficiency, covering complexity classes, NP-completeness, approximation algorithms, and real-world applications.
The Ellipsoid Algorithm - Lecture 19a of CS Theory Toolkit
Explore the Ellipsoid Algorithm for solving Linear Programming and convex optimization, focusing on its polynomial-time efficiency and use of separation oracles in theoretical computer science.
IPM and Nested Dissection for Planar and Separable LPs
Exploring efficient LP solvers using nested dissection in IPM framework for planar min-cost flow, multicommodity flow, low treewidth LPs, and separable LPs. Focuses on optimization techniques and algorithm design.
Approximating k-Edge-Connected Spanning Subgraphs via a Near-Linear Time LP Solver
Explores a near-linear time algorithm for approximating k-edge-connected spanning subgraphs, improving on existing methods in both speed and accuracy for network resilience optimization.
Extension Complexity of Random Polytopes
Explores extension complexity of random polytopes, discussing results for fixed-dimension convex hulls of random points on unit sphere or ball. Joint work with Kwan and Zhao.
ReLU Hull Approximation - Fast and Precise Convex Hull Over-Approximation for Neural Network Verification
Explore a fast, precise approach to over-approximating ReLU function convex hulls in neural network verification, improving efficiency and precision for large-scale networks.
Revisiting Tardos’s Framework for Linear Programming - Faster Exact Solutions using Approximate Solvers
Explore advanced linear programming techniques, focusing on Tardos's framework and innovative approaches for faster exact solutions using approximate solvers.
Information, Computation, Optimization - Connecting the Dots in the Traveling Salesman Problem
Explore optimization techniques and their applications in solving complex problems like the Traveling Salesman, with insights from William Cook's research and real-world examples.
Linear Programming Made Easy with Boost Proto
Linear programming optimization using Boost Proto. Learn to model and solve complex decision problems efficiently with CVX++, a declarative domain-specific language embedded in C++.
Automatic Conference Scheduling with PuLP
Explore linear programming and its practical application in conference scheduling using PuLP, a Python-based solver. Learn how to optimize complex event planning with this powerful tool.
Data Analytics Explained for Business Leaders
Demystifying data analytics for business leaders, bridging the gap between technical and strategic understanding to drive innovation, productivity, and informed decision-making.
Building AI Solutions with Google OR-Tools
Learn to build AI solutions using Google OR-Tools for optimization problems. Explore constraint programming, linear programming, and mixed-integer programming through practical examples like Sudoku and scheduling.
Tight Cramër-Rao Type Bounds for Multiparameter Quantum Metrology Through Conic Programming
Delve into advanced quantum metrology concepts, focusing on Cramër-Rao bounds and conic programming for optimal measurement strategies in estimating incompatible parameters with maximum precision.
Using AI to Solve Complex Business Problems: Optimizing Fashion Industry Markdowns
Discover how AI-powered markdown optimization helped fashion retailers tackle inventory challenges during Covid-19, combining demand forecasting and linear programming for profitable stock clearance.
Octagons Revisited - Elegant Proofs and Simplified Algorithms
Explore simplified algorithms for Octagon domain closure, featuring elegant proofs, efficient computations, and improved abstract transformers for affine assignments in program analysis.
Introduction to Sensitivity Analysis - Week 12
Explore sensitivity analysis techniques to assess how changes in input variables affect model outcomes and decision-making processes.
A Fresh Look at Algorithms for Solving Smooth Multiobjective Optimization Problems
Explore new algorithms for smooth multiobjective optimization using linear programming to find decreasing directions, applicable to various constraint types and yielding substationary points.
Set-limited Functions and Polynomial-time Interior-point Methods
Explore polynomial-time interior-point methods and set-limited functions with Yurri Nesterov, delving into advanced optimization techniques and mathematical theory.
Primal-Dual Optimization Methods for Robust Machine Learning
Explore primal-dual optimization techniques for enhancing machine learning robustness, focusing on advanced mathematical approaches to improve model performance and reliability.