Efficient parameter-free restarted accelerated gradient methods for convex and strongly convex optimization

  • Arnesh Sujanani
  • Renato D.C. Monteiro
    • Categories Convex and Nonsmooth Optimization, Convex Optimization, Nonlinear Optimization Tags , , , , ,

    This paper develops a new parameter-free restarted method, namely RPF-SFISTA, and a new parameter-free aggressive regularization method, namely A-REG, for solving strongly convex and convex composite optimization problems, respectively. RPF-SFISTA has the major advantage that it requires no knowledge of both the strong convexity parameter of the entire composite objective and the Lipschitz constant of … Read more

    Accessible Theoretical Complexity of the Restarted Primal-Dual Hybrid Gradient Method for Linear Programs with Unique Optima

  • Zikai Xiong
    • Categories Convex Optimization, Linear Programming, Nonsmooth Optimization Tags , , ,

    The restarted primal-dual hybrid gradient method (rPDHG) has recently emerged as an important tool for solving large-scale linear programs (LPs). For LPs with unique optima, we present an iteration bound of \(\widetilde{O}\left(\kappa\Phi\cdot\ln\left(\frac{\|w^*\|}{\varepsilon}\right)\right)\), where \(\varepsilon\) is the target tolerance, \(\kappa\) is the standard matrix condition number, \(\|w^*\|\) is the norm of the optimal solution, and \(\Phi\) … Read more

    A Dynamic Strategic Plan for the Transition to a Clean Bus Fleet using Multi-Stage Stochastic Programming with a Case Study in Istanbul

  • Neman Karimi
  • Burak Kocuk
  • Tuğçe Yüksel
    • Categories Energy, Stochastic Programming, Transportation Tags , , ,

    In recent years, the transition to clean bus fleets has accelerated. Although this transition might bring environmental and economic benefits, it requires a long-term strategic plan due to the large investment costs involved. This paper proposes a multi-stage stochastic program to optimize strategic plans for the clean bus fleet transition that explicitly considers the uncertainty … Read more

    Dual Spectral Projected Gradient Method for Generalized Log-det Semidefinite Programming

  • Charles Namchaisiri
  • Makoto Yamashita
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , ,

    Log-det semidefinite programming (SDP) problems are optimization problems that often arise from Gaussian graphic models. A log-det SDP problem with an l1-norm term has been examined in many methods, and the dual spectral projected gradient (DSPG) method by Nakagaki et al.~in 2020 is designed to efficiently solve the dual problem of the log-det SDP by … Read more

    An optimization framework to provide volunteers with task selection autonomy and group opportunities

  • Milan Preet Kaur
  • Jennifer Pazour
    • Categories Applications - OR and Management Sciences Tags , , , , , , , ,

    Abstract Nonprofit Organizations (NPOs) rely on volunteers to support community needs but struggle with making strategic volunteer-to-task assignments to enable volunteer satisfaction, and completion of complex tasks. Creation of volunteer groups and their assignment to NPO tasks can help achieve these goals by providing volunteers with opportunity for networking, collaboration, and peer learning. However, strategically … Read more

    Simple and Effective: A Deterministic Auction with Support Information

  • Rishikesh Parma
  • Çağıl Koçyiğit
    • Categories Applications - OR and Management Sciences, Finance and Economics, Yield Management Tags , , ,

    We study an auction design problem where a seller aims to sell a single item to multiple bidders with independent private values. The seller knows only an upper bound on these values and does not know their distribution. The objective is to devise a deterministic auction mechanism effective across a broad set of distributions. We … Read more

    Integrated Optimization of Timetabling and Electric Vehicle Scheduling: A Case Study of Aachen, Germany

  • Vladimir Stadnichuk
  • Jenny Segschneider
  • Arie M.C.A. Koster
  • Grit Walther
    • Categories Applications - OR and Management Sciences, Approximation Algorithms, Transportation Tags , , , ,

    We tackle the integrated planning problem of periodic timetabling and electric vehicle scheduling, crucial for cities transitioning to electric bus fleets. Given existing timetables, we allow only minor modifications and propose an iterative solution approach that addresses the Electric Vehicle Scheduling Problem (EVSP) in each iteration. Due to the NP-hard nature of EVSP, we employ … Read more

    A graphical framework for global optimization of mixed-integer nonlinear programs

  • Danial Davarnia
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization Applications, Global Optimization Theory Tags , , , , ,

    While mixed-integer linear programming and convex programming solvers have advanced significantly over the past several decades, solution technologies for general mixed-integer nonlinear programs (MINLPs) have yet to reach the same level of maturity. Various problem structures across different application domains remain challenging to model and solve using modern global solvers, primarily due to the lack … Read more

    Bounding the Optimal Number of Policies for Robust K-Adaptability

  • Jannis Kurtz
    • Categories Robust Optimization Tags ,

    In the realm of robust optimization the k-adaptability approach is one promising method to derive approximate solutions for two-stage robust optimization problems. Instead of allowing all possible second-stage decisions, the k-adaptability approach aims at calculating a limited set of k such decisions already in the first-stage before the uncertainty reveals. The parameter k can be … Read more

    Robust Appointment Scheduling for General Convex Uncertainty Sets

  • Judith Brugman
  • Dick den Hertog
  • Johan Van Leeuwaarden
    • Categories Applications - OR and Management Sciences, Robust Optimization, Scheduling Tags , , , , ,

    The Appointment Scheduling Problem (ASP) involves scheduling a finite number of customers with uncertain service times, served consecutively by a single server, with the goal of minimizing the weighted costs of waiting time, idle time, and overtime. Previous studies employing stochastic programming were limited to small instances or constrained by restrictive assumptions. We introduce a … Read more