A Unified Linear Programming Approach for Maximizing Vertex-Degree-Based Indices of Trees
The problem of maximizing vertex-degree-based topological indices over trees with a prescribed order and maximum degree $Δ$ has traditionally relied on ad-hoc combinatorial arguments. In this paper, we establish a unified linear programming approach that systematically resolves these extremal problems. By formulating the optimization over degree-pair multiplicities, the search for an extremal tree reduces to minimizing a discrete dual penalty. Treating vertex degrees as continuous variables yields an analytical single-variable test condition that guarantees global optimality. We apply this framework across a diverse collection of invariants. We prove that the standard basic feasible solution is optimal for the Tangent Sombor, $SO_1$, $SO_2$, Albertson, and $σ$-irregularity indices for all $Δ\ge 3$. However, the dual optimality conditions establish exact analytical thresholds at which the standard basis loses optimality, necessitating a transition to alternative basic feasible solutions. For the Arithmetic-Geometric ($AG$) index, the standard basis is optimal strictly for $Δ\ge 9$; for smaller maximum degrees ($3 \le Δ\le 8$), optimization shifts to an alternative edge basis, allowing us to explicitly construct and characterize all extremal trees across congruence classes, including chemical trees ($Δ\in \{3, 4\}$). Conversely, for the Triangle Volume index ($SO_{11}$), the standard basis is optimal exclusively for $Δ\le 8$, while for $Δ\ge 9$ we analytically derive a novel, degree-dependent optimal basis to explicitly construct its extremal configurations.