Under construction. This area is a framework being built: most courses have no teaching material yet, and things may change or break. Private self-study. Not an official programme and not recognised by any ministry or school. No qualification, mark or document is issued, and “years 1 to 3” and “advanced” are names of study paths only.
B20 Optimization I
Years 1 to 3 pathB: Years 1 to 3 coreplannedabout 120 h (planning estimate)
Draft lectures and exercises are available for this course. They were written by AI and checked by a second AI; no person has reviewed them yet. Use them as a study aid, not as an authority.
Named in: Years 1 to 3 pathway
Why this course
Convexity, optimality conditions and basic algorithms; links directly to gradient-based learning.
Prerequisites
Concept-level units
Units follow the syllabus order, as a recommended sequence rather than a gate.
- Convex sets and functions
- Optimality conditions
- Quadratic problems
- Gradient and Newton methods
- Constraints and multipliers
- Convergence examples
Assessable outcome
Derive an optimization method and justify its behaviour on a suitable model.
Assessment design
- topic exercises
- mixed problem sets
- unseen proof or derivation problems
- computational project
- cumulative examination
cumulative; includes a project; uses spaced retrieval
Resources
Every title here is candidate, unverified unless marked verified. Titles were suggested while planning. Nobody has checked that they exist, what they cost or how they are licensed.
- Boyd and Vandenberghe · Convex Optimization
- selected chapters
Content record
- Version
- 0.0.0
- Review
- no review of any kind
- Provenance
- catalogue imported from the owner brief of 29 Oct 2026; no teaching content authored
- Known issues
- none recorded
- Type
- course
Bridges and laboratories
No bridge or laboratory is attached to this course yet.
You and this course
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