pith. sign in

Nonlinear programming.Journal of the Operational Research Society, 48(3):334–334, 1997

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

years

2026 2 2025 2

verdicts

UNVERDICTED 4

representative citing papers

Disjunctive Benders Decomposition

math.OC · 2025-06-04 · unverdicted · novelty 7.0

Disjunctive Benders decomposition integrates disjunctive programming with Benders cuts to produce convex hull inequalities via existing oracles, removing the need for MIP master problems in mixed-binary linear programs.

Global Optimization via Softmin Energy Minimization

cs.LG · 2025-09-22 · unverdicted · novelty 6.0

A stochastic gradient flow on particle swarms driven by a softmin energy approximation converges to global minima for strongly convex functions and exhibits faster hitting times between wells than overdamped Langevin dynamics.

citing papers explorer

Showing 4 of 4 citing papers.

  • CMC-Opt: Constraint Manifold with Corners for Inequality-Constrained Optimization cs.RO · 2026-05-20 · unverdicted · none · ref 2

    Proposes constraint manifolds with corners as a new topological structure for mixed-constraint optimization in robotics, enabling direct unconstrained optimization on the feasible set.

  • Simulation-Ready Cluttered Scene Estimation via Physics-aware Joint Shape and Pose Optimization cs.RO · 2026-02-23 · unverdicted · none · ref 7

    SPARCS uses a differentiable contact model and sparse Hessian solver to jointly optimize shapes and poses of up to five interacting objects, producing physically valid simulation-ready reconstructions.

  • Disjunctive Benders Decomposition math.OC · 2025-06-04 · unverdicted · none · ref 9

    Disjunctive Benders decomposition integrates disjunctive programming with Benders cuts to produce convex hull inequalities via existing oracles, removing the need for MIP master problems in mixed-binary linear programs.

  • Global Optimization via Softmin Energy Minimization cs.LG · 2025-09-22 · unverdicted · none · ref 14

    A stochastic gradient flow on particle swarms driven by a softmin energy approximation converges to global minima for strongly convex functions and exhibits faster hitting times between wells than overdamped Langevin dynamics.