Navigate the constraint landscape before it navigates your budget.

For semiconductor EDA leads, logistics operations directors, and quantitative game-theory engineers: instrument high-dimensional search with topology, exact residual records, and a shared benchmark boundary.

Scope a solver benchmark →Read the research atlas ↗

Exact constraint residuals are checkable; global optimality and runtime require workload-specific proof.

Operational use cases

Semiconductor EDA: floorplanning and macro placement under dense geometry constraints.
Global logistics: allocation and routing across coupled capacity constraints.
Multi-agent systems: feasible strategy search under coupled objectives.

Solver comparison

Inspect what the search can prove.

Traditional MILP / SAT

Strong tools for well-scoped formulations and established solver assumptions.

High-dimensional NP-hard instances can exhaust memory, branching budgets, or time.

Local methods can return locally useful states without proving global structure.

Maha Landscape-Opt

Topology-aware candidate filtering and critical-point instrumentation.

Supports declared execution bounds and exact residual reporting for submitted constraints.

Billion-variable capability is a benchmark target, not a universal performance claim.

A bounded solver engagement

Bring an instance, a baseline, and a definition of feasible.

01 / Declare variables, polynomial constraints, objective, integer bounds, hardware, and time budget.
02 / Compile filters and critical-point diagnostics against the stated problem representation.
03 / Compare feasibility, objective, runtime, memory, and residuals using identical acceptance rules.