Lectures related to Closest Vector Problem: Voronoi Cells

Covers linear inequalities, active constraints, and equivalent linear programs.

Explores the Closest Vector Problem and Voronoi cells in lattice reduction algorithms.

Explores convex polyhedra, linear programs, and their optimization importance.

Explores Hamiltonian dynamics on convex polytopes, covering symplectic capacities, EHZ capacity, and systolic ratio maximizers.

Covers the fundamentals of integer programming and network flows in directed graphs.

Covers optimization problems in path finding and portfolio allocation.

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Explores integer programs, nonconvex optimization, constraints, and geometric aspects of linear programming for optimal solutions.

Explores mixed-integer linear programming, binary variables, 0-1 knapsack, assignment problems, and LP relaxation strength.

Covers the theory of linear and integer programming, focusing on the integer hull and rational polyhedra.

Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.

Covers the modelling and optimization of energy systems, focusing on solving optimization problems with constraints and variables.

Covers sensitivity analysis in linear programming, focusing on optimal solutions and their sensitivities to changes.

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Covers the basics of linear programming, defining corners, extreme points, and feasible solutions within polyhedrons.

Covers the basics of linear programming and the simplex method, focusing on finding optimal solutions and handling degeneracy.