Projected-Gradient Analysis for Open-Domain Convex Optimization under Boundary Blow-Up:Application to Controllability Scoring

Abstract

We study convex optimization over a compact convex set when the objective is smooth and convex only on an open domain. Under a boundary-blow-up condition, every feasible initialization yields a compact invariant sublevel set separated from the complement of the objective domain, and an optimal solution exists. For a domain-aware Armijo projected-gradient method, a safe-neighborhood analysis establishes well-defined objective evaluations, finite backtracking, sufficient decrease, and a run-specific but iteration-independent positive lower bound on the accepted step sizes. These properties yield explicit sublinear objective and stationarity guarantees, together with convergence of the full iterate sequence. Positive curvature restricted to feasible displacement directions further guarantees uniqueness and linear convergence. We apply the framework to controllability scoring with prescribed input directions and compact convex allocation constraints. Feasibility is characterized exactly by controllability of the input directions eligible for positive allocation, while restricted injectivity of the Gramian map guarantees uniqueness of the optimal allocation and provides explicit strong-convexity bounds. A directed-network example illustrates how candidate exclusion can preserve or destroy feasibility and alter the optimal allocation in a criterion- and horizon-dependent manner.

Disclosure

“This work was supported by JST PRESTO, Japan, Grant 8 preserved both feasibility and restricted injectivity at the Number JPMJPR25K4. The author used OpenAI ChatGPT three reported horizons, whereas additionally excluding node 9 throughout the preparation and revision of this manuscript. destroyed feasibility. The computed optima”

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2026-08-23T19:07:53.803934Z

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