Objective function, Convexity

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Related lectures (27)

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Convergence Criteria: Necessary Conditions

Explains necessary conditions for convergence in optimization problems.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Optimization Problems

Explores optimization problems to find global extrema of functions and shapes.

Extrema of Functions

Covers the discussion of local extrema, concavity, convexity, and inflection points in functions.

Optimization Techniques: Gradient Descent and Convex Functions

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Newton's local method: Geometric interpretation

Explores the geometric interpretation of Newton's method in optimization problems.

Optimization Methods: Convergence and Trade-offs

Covers optimization methods, convergence guarantees, trade-offs, and variance reduction techniques in numerical optimization.

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Energy optimization strategies

Covers brainstorming options for smart operation changes, heat recovery, and PV panel performance.

Faster Gradient Descent: Projected Optimization Techniques

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.