SUMMARY
The optimization problem involves minimizing the function f(x,y) = √(x² + y²) under the constraint x + y ≤ 0. The trivial solution at (0,0) is confirmed as the only minimizer, as any other point would yield f(x,y) > 0. Additionally, the function MP(z), defined as the infimum of f(x,y) for points satisfying the constraint, is shown to be non-differentiable at z = 0. The discussion emphasizes the application of the Karush-Kuhn-Tucker conditions to validate the global minimum at the origin.
PREREQUISITES
- Understanding of convex optimization principles
- Familiarity with the Karush-Kuhn-Tucker conditions
- Knowledge of differentiability in mathematical functions
- Basic concepts of infimum and convex sets in R²
NEXT STEPS
- Study the Karush-Kuhn-Tucker conditions in detail
- Learn about differentiability and its implications in optimization
- Explore the concept of infimum in the context of convex sets
- Investigate geometric interpretations of optimization problems
USEFUL FOR
Students and professionals in mathematics, particularly those focusing on optimization theory, convex analysis, and mathematical programming.