Inverse Cost Scaling in Iterative Convergence Systems

We demonstrate that iterative solvers with dimensional compression exhibit a counterintuitive property: computational cost decreases with iteration depth. Across three domains (radiosity rendering, financial equilibrium, differential equations), we observe 32x compute reduction on deep iterations through rank-adaptive compression and polar quantization. The hardest problems — those requiring the deepest convergence — become the cheapest to solve.