mathematical optimization
Let an agent explain mathematical optimisation, relay problem classes, methods and applications from operations research and mathematics sources, describe the methods and senses the registry aliases name, and distinguish optimisation from search, estimation, program optimisation in software and satisficing, with metaheuristic claims presented with attribution.
Research draft, second pass
A second pass drafted this model: the structure a model of this thing needs, and what is known about it in the world. The line under this one says how the second half was obtained - researched against sources, or recalled without web access, in which case nothing here was read anywhere and every claim is a lead to verify. Unreviewed either way.
written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify
Researched by: Claude
Purpose and description
Let an agent explain mathematical optimisation, relay problem classes, methods and applications from operations research and mathematics sources, describe the methods and senses the registry aliases name, and distinguish optimisation from search, estimation, program optimisation in software and satisficing, with metaheuristic claims presented with attribution.
The selection of a best element from a set of alternatives according to an objective function, subject to constraints, studied in mathematics, operations research, computer science and engineering, including continuous and discrete optimisation, combinatorial optimisation, goal programming with multiple targets, metaheuristics such as invasive weed optimisation, computer-aided optimisation in engineering design and program optimisation in compilers; the registry aliases mix mathematical and software senses of optimisation.
What it is for: Finding best solutions under constraints.
It can be explain problem classes; relay methods; describe named methods; distinguish related concepts.
Distinguishing features
Objective and constraints
Continuous and discrete
Exact and heuristic methods
Wide application
What it looks like
Not a visible object; mathematical models and algorithms.
Physical character
simplex method: 1947 year - George Dantzig
Karmarkar interior point: 1984 year
invasive weed optimisation: 2006 year - proposed metaheuristic
How it is recognised
Choosing a best solution by an objective and constraints
Combinatorial and discrete optimisation, goal programming, invasive weed optimisation, computer-aided optimisation, program optimisation
Search finds feasible items; estimation fits parameters; program optimisation improves code; satisficing accepts good enough
Related models
is a kind of - in registry terms
includes -
is used in -
is distinct from - in compilers
In practice
Families and kinds
linear and nonlinear programming
convex optimisation
combinatorial and discrete optimisation
multi-objective and goal programming
metaheuristics such as genetic algorithms and invasive weed optimisation
engineering design optimisation
compiler program optimisation as a separate sense
Standards and regulation
No regulation
Failure modes and hazards
Overstating metaheuristic novelty
Local optima mistaken for global
Registry aliases mixing mathematical and software senses
Also called
+2
Where this came from
wikidata · CC0 1.0
Drafted structure
Bundle to layer to finding to question, as the second pass will find it: 4 bundles · 8 layers · 8 findings · 16 questions.
Understand What optimisation is.
Science.
Definition
Definition.
Definition
Definition.
- What is mathematical optimisation, and how does it differ from search, estimation, program optimisation and satisficing? definition
- Is the question about theory, a method, software or an application? boundary
Kinds
Kinds and methods.
Kinds
Kinds.
- What are combinatorial and discrete optimisation, goal programming, invasive weed optimisation and computer-aided optimisation? definition
- Which entry fits the specific method? action
Theory Theory.
Science.
Conditions
Optimality conditions.
Conditions
Conditions.
- What are KKT conditions, duality and convexity? provenance
- Which references are standard? provenance
Complexity
Complexity.
Complexity
Complexity.
- Why are some optimisation problems NP-hard? provenance
- Which sources are cited? provenance
Methods Methods.
Attribution.
Exact
Exact methods.
Exact
Exact.
- How do simplex, interior point and branch and bound methods work? provenance
- Which entry fits branch and bound? action
Heuristic
Metaheuristics.
Heuristic
Heuristic.
- What are metaheuristics, and what criticisms exist of nature-inspired variants, with positions attributed? provenance
- Is the presentation attributed? boundary
Context Applications and history.
Context.
Applications
Applications.
Applications
Applications.
- How is optimisation used in logistics, finance, engineering and machine learning? provenance
- Which entry fits operations research? action
History
History.
History
History.
- How did optimisation develop from calculus to Dantzig and beyond? provenance
- Which entry fits George Dantzig? action
What the second pass must settle
- Should combinatorial optimisation and program optimisation be separate primary entries?
- How should operations research sources be linked?
- The registry entry has merged aliases from software engineering; should they be split off?