Prerequisite chain
Prerequisites for Convex Optimization Basics
Topics you need before working through Convex Optimization Basics. Direct prerequisites are listed first; transitive prerequisites (the chain reachable through them) follow.
Direct prerequisites (9)
- Differentiation in Rⁿlayer 0A, tier 1
- Matrix Operations and Propertieslayer 0A, tier 1
- Common Inequalitieslayer 0A, tier 1
- Continuity in Rⁿlayer 0A, tier 1
- Dynamic Programminglayer 0A, tier 1
- Inverse and Implicit Function Theoremlayer 0A, tier 2
- Positive Semidefinite Matriceslayer 0A, tier 1
- Taylor Expansionlayer 0A, tier 1
- The Hessian Matrixlayer 0A, tier 1
Reachable through the chain (20)
These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.
- Sets, Functions, and Relationslayer 0A, tier 1
- Basic Logic and Proof Techniqueslayer 0A, tier 2
- Vectors, Matrices, and Linear Mapslayer 0A, tier 1
- Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
- Linear Independencelayer 0A, tier 1
- Common Probability Distributionslayer 0A, tier 1
- Exponential Function Propertieslayer 0A, tier 1
- Integration and Change of Variableslayer 0A, tier 2
- Measure-Theoretic Probabilitylayer 0B, tier 1
- Cardinality and Countabilitylayer 0A, tier 2
- Kolmogorov Probability Axiomslayer 0A, tier 1
- Random Variableslayer 0A, tier 1
- Zermelo-Fraenkel Set Theorylayer 0A, tier 2
- Graph Algorithms Essentialslayer 0A, tier 2
- Greedy Algorithmslayer 0A, tier 2
- The Jacobian Matrixlayer 0A, tier 1
- Eigenvalues and Eigenvectorslayer 0A, tier 1
- Inner Product Spaces and Orthogonalitylayer 0A, tier 1
- Matrix Normslayer 0A, tier 1
- Vector Calculus Chain Rulelayer 0A, tier 1