Convex Optimization
E451067
Convex Optimization is a widely used graduate-level textbook that systematically develops the theory, algorithms, and applications of convex optimization problems in engineering, statistics, and applied mathematics.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Convex Optimization canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4539860 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Convex Optimization Context triple: [Stephen P. Boyd, notableWork, Convex Optimization]
-
A.
Nonlinear programming
Nonlinear programming is a branch of mathematical optimization focused on finding optimal solutions to problems where the objective function or constraints are nonlinear.
-
B.
Karush–Kuhn–Tucker conditions
The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
-
C.
CVX
CVX is the stock ticker symbol for Chevron Corporation, a major American multinational energy and oil company.
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D.
Carathéodory’s theorem in convex geometry
Carathéodory’s theorem in convex geometry is a fundamental result stating that any point in the convex hull of a set in ℝⁿ can be expressed as a convex combination of at most n+1 points from that set.
-
E.
Optimal Transport: Old and New
"Optimal Transport: Old and New" is a comprehensive monograph by Cédric Villani that develops the theory of optimal transport and its applications across analysis, geometry, and probability.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Convex Optimization Target entity description: Convex Optimization is a widely used graduate-level textbook that systematically develops the theory, algorithms, and applications of convex optimization problems in engineering, statistics, and applied mathematics.
-
A.
Nonlinear programming
Nonlinear programming is a branch of mathematical optimization focused on finding optimal solutions to problems where the objective function or constraints are nonlinear.
-
B.
Karush–Kuhn–Tucker conditions
The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
-
C.
CVX
CVX is the stock ticker symbol for Chevron Corporation, a major American multinational energy and oil company.
-
D.
Carathéodory’s theorem in convex geometry
Carathéodory’s theorem in convex geometry is a fundamental result stating that any point in the convex hull of a set in ℝⁿ can be expressed as a convex combination of at most n+1 points from that set.
-
E.
Optimal Transport: Old and New
"Optimal Transport: Old and New" is a comprehensive monograph by Cédric Villani that develops the theory of optimal transport and its applications across analysis, geometry, and probability.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
graduate-level textbook
ⓘ
textbook ⓘ |
| applicationArea |
circuit design
ⓘ
communications engineering ⓘ control systems ⓘ finance ⓘ machine learning ⓘ signal processing ⓘ statistics ⓘ |
| author |
Lieven Vandenberghe
ⓘ
Stephen Boyd ⓘ
linked to:
Stephen P. Boyd
|
| emphasis |
modeling of optimization problems
ⓘ
practical algorithms ⓘ |
| field |
applied mathematics
ⓘ
convex optimization ⓘ engineering ⓘ optimization ⓘ statistics ⓘ |
| hasOnlineResources | yes ⓘ |
| language | English ⓘ |
| level | graduate ⓘ |
| notableFor |
influence in engineering and applied mathematics education
ⓘ
systematic treatment of convex optimization ⓘ |
| publisher | Cambridge University Press ⓘ |
| structure |
algorithms
ⓘ
applications ⓘ theory ⓘ |
| topic |
Karush–Kuhn–Tucker conditions
ⓘ
Lagrange duality ⓘ cone programming ⓘ convex functions ⓘ convex optimization problems ⓘ convex sets ⓘ duality theory ⓘ ellipsoid method ⓘ geometric programming ⓘ gradient methods ⓘ interior-point methods ⓘ least-squares problems ⓘ linear programming ⓘ proximal methods ⓘ quadratic programming ⓘ semidefinite programming ⓘ subgradient methods ⓘ |
| usedAs | university course textbook ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Convex Optimization Description of subject: Convex Optimization is a widely used graduate-level textbook that systematically develops the theory, algorithms, and applications of convex optimization problems in engineering, statistics, and applied mathematics.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.