Sweept

Sweept is a term used to describe a systematic approach to exploring a multidimensional parameter space by sweeping through one or more variables. It is applied in experimental design, optimization, and software testing to map how changes in inputs influence outputs, identify regions of interest, and compare configurations.

Concept and methods

A sweept plan involves selecting which dimensions to vary, determining the range and resolution for each, and choosing an execution order. Sweep strategies range from simple grid sweeps (varying several parameters at fixed steps) to one-dimensional sweeps across a single axis, and to adaptive sweeps that adjust focus based on interim results. In adaptive sweeps, preliminary findings guide subsequent sampling to efficiently allocate evaluation effort.

Variants

Static sweept refers to a fixed plan, such as a full or fractional grid, that does not change during execution. Dynamic or adaptive sweept uses feedback from collected data to refine the search, often slowing or stopping exploration in regions with diminishing returns and intensifying it where promising trends emerge. In practice, sweept methods may incorporate elements from design of experiments, surrogate modeling, or optimization heuristics to balance coverage with cost.

Applications

Sweept is employed to calibrate systems, optimize performance, and validate models. In engineering and sciences, it helps characterize how design choices affect outcomes. In machine learning and software testing, sweept approaches assist in hyperparameter tuning, performance benchmarking, and ensuring robust behavior across varied configurations.

Benefits and limitations

The primary advantage of sweept approaches is structured exploration that yields comparative data across configurations. Limitations include the curse of dimensionality in high-dimensional spaces and the computational or time costs of evaluating many configurations. Combining sweept with adaptive strategies or surrogate models can mitigate these challenges.

See also

Design of experiments, sweep-line algorithm, hyperparameter optimization, parameter sweep.