A flexible block coordinate descent method for unconstrained optimization under Hölder continuity

  • V. S. Amaral
  • Roberto Andreani
  • Leonardo D. Secchin
  • Gilson Silva
    • Categories Nonlinear Optimization, Unconstrained Optimization

    In this work, we propose a flexible block coordinate method for unconstrained optimization problems under Hölder continuity assumptions. The method guarantees convergence to stationary points and has worst-case complexity results comparable to those obtained by single-block methods that assume Lipschitz or Hölder continuity. The approach is based on quadratic models of the objective function combined … Read more

    Adaptive Newton-CG methods with global and local analysis for unconstrained optimization with Hölder continuous Hessian

  • Ziyang Zeng
  • Junyu Zhang
  • Chuan He
    • Categories Nonlinear Optimization, Unconstrained Optimization Tags , , , ,

    In this paper, we study Newton-conjugate gradient (Newton-CG) methods for minimizing a nonconvex function $f$ whose Hessian is $(H_f,\nu)$-H\”older continuous with modulus $H_f>0$ and exponent \(\nu\in(0,1]\). Recently proposed Newton-CG methods for this problem \cite{he2025newton} adopt (i) non-adaptive regularization and (ii) a nested line-search procedure, where (i) often leads to inefficient early progress and the loss … Read more

    Separable QCQPs and Their Exact SDP Relaxations

  • Masakazu Kojima
  • Sunyoung Kim
  • Naohiko Arima
    • Categories Global Optimization Theory, Quadratic Programming, Semi-definite Programming Tags , , , , ,

    This paper studies exact semidefinite programming relaxations (SDPRs) for separable quadratically constrained quadratic programs (QCQPs). We consider the construction of a larger separable QCQP from multiple QCQPs with exact SDPRs. We show that exactness is preserved when such QCQPs are combined through a separable horizontal connection, where the coupling is induced through the right-hand-side parameters … Read more

    On vehicle routing problems with stochastic demands — Scenario-optimal recourse policies

  • Matheus Jun Ota
  • Ricardo Fukasawa
    • Categories Branch and Cut Algorithms, Stochastic Programming, Transportation Tags , ,

    Two-Stage Vehicle Routing Problems with Stochastic Demands (VRPSDs) form a class of stochastic combinatorial optimization problems where routes are planned in advance, demands are revealed upon vehicle arrival, and recourse actions are triggered whenever capacity is exceeded. Following recent works, we consider VRPSDs where demands are given by an empirical probability distribution of scenarios. Existing … Read more

    Sequential Nonlinear-Programming Approach to Thermal-Aware VLSI Floorplanning using Multi-boundary Shapes

  • Paul Morton
    • Categories Applications - Science and Engineering, VLSI layout Tags , , , ,

    In this paper we develop and implement sequential nonlinear-programming methods for solving the thermal-aware soft-macro VLSI floorplanning problem with IO-block placement and a dynamic floorplan-boundary.  We develop a multi-stage nonlinear-programming approach to this floorplanning problem.   We break the floorplanning process into two main stages, a simplified first-stage, which omits any consideration of the floorplan boundary … Read more

    The value of storage in electricity distribution: The role of markets

  • Dirk Lauinger
  • Deepjyoti Deka
  • Sungho Shin
    • Categories Energy Tags , , , ,

    Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. … Read more

    A Gradient Sampling Algorithm for Noisy Nonsmooth Optimization

  • Lara Zebiane
  • Frank E. Curtis
  • Albert S. Berahas
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization, Nonsmooth Optimization Tags , ,

    An algorithm is proposed, analyzed, and tested for minimizing locally Lipschitz objective functions that may be nonconvex and/or nonsmooth. The algorithm, which is built upon the gradient-sampling methodology, is designed specifically for cases when objective function and generalized gradient values might be subject to bounded uncontrollable errors. Similarly to state-of-the-art guarantees for noisy smooth optimization … Read more

    Bilevel Learning

  • Alain Zemkoho
    • Categories Bilevel Optimization, Optimization in Data Science Tags , , ,

    Bilevel learning refers to machine learning problems that can be formulated as bilevel optimization models, where decisions are organized in a hierarchical structure. This paradigm has recently gained considerable attention in machine learning, as gradient-based algorithms built on the implicit function reformulation have enabled the computation of large-scale problems involving possibly millions of variables. Despite … Read more

    KDE Robust Satisficing for Optimal Load Shedding Under Renewable Uncertainty

  • Min Xin Shu
  • Wei Liu
  • Qiao Yi Wang
  • Bo Yu
    • Categories Applications - Science and Engineering, Energy, Robust Optimization Tags , , , ,

    Abstract—Renewable-driven direct-current optimal load shedding (DC-OLS) requires a model that is interpretable to operators, data driven under continuous forecast errors, sensitive to severe security failures, and computationally tractable. This paper develops a budgeted KDE-ϕ-HMCR-RS-OLS framework for that purpose. Robust satisficing (RS) replaces ambiguity-radius tuning with an admissible shedding budget. A one-dimensional KDE reference family with … Read more

    An objective-function-free algorithm for nonconvex stochastic optimization with deterministic equality and inequality constraints

  • Serge Gratton
  • Philippe L. Toint
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Programming Tags , , , ,

    An algorithm is proposed for solving optimization problems with stochastic objective and deterministic equality and inequality constraints. This algorithm is objective-function-free in the sense that it only uses the objective’s gradient and never evaluates the function value. It is based on an adaptive selection of function-decreasing and constraint-improving iterations, the first ones using an Adagrad-type … Read more