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Decision variable

A decision variable is a quantity whose value is chosen by an optimization model to represent an available decision.

How 2 Manning books explain it

Optimization Algorithms: AI techniques for design, planning, and control problems

A decision variable is a variable whose value is chosen to optimize an objective function subject to constraints.

How it is used
Frames controllable inputs as the choices optimization methods assign while searching for strong feasible solutions.
How deep it goes
Defines variables, bounds, and their effects on objectives, then applies them to pricing, vehicle design, and loading examples.
Examples
Uses ticket price, electric-vehicle design parameters, routing choices, and cargo-bike parcel selection as concrete decision variables.
What you can do after
Evaluate optimization models by identifying controllable inputs, their bounds, affected objectives, and applicable constraints.

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Statistics Every Programmer Needs

A decision variable is an unknown quantity whose value represents a selectable decision in an optimization problem.

How it is used
Models selectable alternatives within linear optimization for project prioritization and resource allocation.
How deep it goes
Defines binary and continuous roles, specifies bounds, and connects variables to objectives and constraints without deriving solver algorithms.
Examples
Uses project-feature selection, feature cost and value data, and Python bounds of `(0, 1)` for each feature.
What you can do after
Apply it via `scipy.optimize.linprog` by defining variable bounds alongside objectives and linear constraints.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.