Coarse Landscape Sampling¶
Method — instantiates Landscape-Aware Search Strategy Design
Samples diverse regions at low resolution before spending evaluation budget on detailed local improvement.
Before you can decide how to search, you need a cheap first look at where the good regions might be. Coarse Landscape Sampling spends a deliberately small slice of the evaluation budget scattering low-resolution probes across widely separated parts of the configuration space, so that the promising basins can be located before any fine-grained search is committed. The one idea that separates it from its method siblings is that it works by breadth — a wide, sparse scatter meant to find which regions are worth a closer look — not by characterizing the robustness of a region and not by fitting a predictive surface. Its output is a ranked shortlist of regions, each tagged with rough value and its assumptions, handed onward to whatever detailed search the terrain warrants.
Example¶
A metallurgy group is hunting for a corrosion-resistant alloy across five alloying elements, each dialable over a wide range — far too large a space to test exhaustively, and each real casting-and-corrosion test is slow and expensive. Rather than gridding the whole space, they lay down a space-filling scatter of about 40 compositions chosen to be maximally spread out, and evaluate each with a fast low-fidelity screen (a quick electrochemical proxy, not the full multi-week salt-spray trial). The coarse map that comes back is blurry but decisive: two clusters of compositions score well, a broad middle region is uniformly poor, and one corner is surprisingly promising despite being far from anything the team would have guessed. They spend the expensive salt-spray budget only in those three regions. The reconnaissance cost a small fraction of the total budget and redirected the whole search away from the flat middle it would otherwise have polished.
How it works¶
- Choose a spread, not a grid. Select points that fill the space as evenly as possible (a space-filling or maximin design), so no region is left dark.
- Evaluate cheaply and coarsely. Use the lowest-fidelity proxy that still ranks regions correctly; resolution is intentionally sacrificed for coverage.
- Rank regions, not points. Cluster the results into candidate basins and rank them by rough value and by how confident the sparse evidence allows.
- Promote the best regions to finer search and record which regions were ruled out and on how little evidence, so they can be reopened.
Tuning parameters¶
- Sample count — how many coarse probes; more samples shrink the blind spots between them but eat budget meant for refinement.
- Spread design — how the points are spaced; strong space-filling avoids redundancy but can straddle narrow features.
- Fidelity level — how cheap-and-rough each evaluation is; lower fidelity buys more coverage but risks mis-ranking regions.
- Promotion threshold — how many regions advance to detailed search; generous promotion hedges against a mis-rank, strict promotion concentrates budget.
When it helps, and when it misleads¶
It earns its keep when the space is large, evaluation is expensive, and the terrain is unknown — exactly the conditions under which committing to the first promising region is most dangerous. Its central failure is false landscape inference: a sparse scatter can miss a narrow high-value ridge that falls between probes, or mistake noise at a few points for real structure.[n1] The classic misuse is treating the coarse ranking as the final answer rather than as reconnaissance — polishing the best coarse region without ever checking whether a missed ridge nearby is higher. The discipline is to keep the coarse map explicitly provisional: carry each region's confidence, and reopen ruled-out territory whenever finer search near a boundary hints at more.
How it implements the components¶
landscape_probe_plan— it is a probe plan: the space-filling scatter is the sampling design that reveals terrain structure before commitment.feature_hypothesis_map— the clustered results form the first working hypothesis about where basins, plateaus, and dead regions lie.evaluation_budget_and_resolution— its defining discipline is spending a small budget at deliberately low resolution, trading fidelity for coverage.
It does not fit a predictive surrogate_model — that is Response Surface Model, which interpolates a continuous function rather than ranking a scatter — and it does not march inputs across ranges to expose constraint_and_safety_boundary cliffs or value_function_or_proxy fragility within a region, which is Parameter Sweep and Sensitivity Grid.
Related¶
- Instantiates: Landscape-Aware Search Strategy Design — supplies the cheap reconnaissance that tells later mechanisms where to look.
- Sibling mechanisms: Annealing or Perturbation Schedule · Gradient or Directional Probe · Objective Surface Sketch · Optimization Trace Dashboard · Parameter Sweep and Sensitivity Grid · Random Restart Plan · Response Surface Model · Search Algorithm Portfolio
Editorial Notes¶
Form Classification¶
Form family: Analysis, Modeling & Optimization
Rationale: Samples diverse regions at low resolution before spending evaluation budget on detailed local improvement, making its operative form a computation, comparison, model, or analytic representation used to infer, estimate, or choose.
Independent corroboration: The frozen evidence defines Coarse Landscape Sampling as 'Samples diverse regions at low resolution before spending evaluation budget on detailed local improvement', so its operative form is Analysis, Modeling & Optimization.
Review outcome: Independent reviewer agreement; high confidence.
Origin Attribution¶
Primary origin: Operations Research
Origin pattern: Cross-disciplinary synthesis
Present-day reach: Multi-domain
Rationale: Global optimization established spending an initial budget on widely dispersed low-resolution probes before local refinement.
Related originating lineages:
- Computer Science & Software Engineering — Algorithmic search supplies space-filling and adaptive-sampling implementations.
- Statistics & Experimental Design — Design of experiments contributes space-filling low-resolution sampling and uncertainty-aware comparison of regions.
Review resolution: Both reviewers agree on operations_research as primary. Reading the mechanism confirms that its defining operation belongs to that lineage; the final record retains computer_science, statistics_experimental_design only as materially formative origin and keeps present-day application breadth separate from provenance.
Review outcome: Reconciled after independent review; high confidence.
Notes¶
[n1] Coarse-to-fine (multiresolution) search first locates promising regions at low resolution, then refines only those — the discipline this method supplies the first stage of. Its known weakness is that features narrower than the coarse grid can slip through undetected between samples. ↩