budget-constrained-design

Productivity

Optimize experiment design under compute and time budget constraints

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Tactic: Budget-Constrained Design

Orchestration Pattern

Assess Budget → Determine available GPU-hours, wall-clock time, and cost ceiling
factor-identification → Identify all candidate factors
Estimate Cost Per Run → Calculate time/compute for a single experiment run
Compute Maximum Runs → budget / cost_per_run = max feasible runs
level-specification → Constrain levels to fit within run budget
Select Design Type → Choose most information-efficient design for the budget
design-matrix-construction → Build the constrained design matrix

Decision Criteria

Available Runs	Recommended Approach
< 10	One-factor-at-a-time or Plackett-Burman screening
10-30	Fractional factorial (Resolution III-IV)
30-60	Fractional factorial (Resolution V) or Taguchi
60-120	Full factorial on top factors + screening on rest
120+	Full factorial or RSM with replication

Optimization Strategies

Sequential Design: Run screening first, then detailed study on important factors
Adaptive Allocation: Allocate more runs to high-variance conditions
Early Stopping: Define stopping criteria for clearly dominated configurations
Transfer from Pilot: Use pilot study results to inform main study design
Shared Controls: Reuse control/baseline runs across multiple comparisons

Quality Checks

Does the design have sufficient power for the primary hypothesis?
Are the most important factors given priority in the allocation?
Is there a contingency plan if budget is cut mid-experiment?
Are early stopping criteria pre-defined (not post-hoc)?
Is the design balanced despite budget constraints?

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
design-matrix-construction	Build the experiment design matrix with proper orthogonality and balance
factor-identification	Identify independent, dependent, and control variables for an experiment
level-specification	Determine appropriate levels for each experimental factor