Program

MA-01: Plenary 1
Stream: Plenaries
Room: B100/1001
Chair(s): Selin Ahipasaoglu

• Constrained Optimization via Frank-Wolfe Algorithms
  Sebastian Pokutta

TD-01: Plenary 3 (EUROPT Lecture)
Stream: Plenaries
Room: B100/1001
Chair(s): Giancarlo Bigi, Immanuel Bomze

• Contextual Stochastic Bilevel Optimization
  Daniel Kuhn

WA-01: Plenary 4
Stream: Plenaries
Room: B100/1001
Chair(s): Alain Zemkoho

• Non-Smooth Optimization on Stiefel Manifold and Beyond
  Anthony So

TA-01: Plenary 2
Stream: Plenaries
Room: B100/1001
Chair(s): Laura Palagi

• Bayesian optimization for mixed feature spaces using tree kernels and graph kernels
  Ruth Misener

WD-01: Closing
Stream: Plenaries
Room: B100/1001
Chair(s): Alain Zemkoho, Giancarlo Bigi, Selin Ahipasaoglu

MB-01: Advances in Large-Scale Derivative-Free Optimization
Stream: Zeroth and first-order optimization methods
Room: B100/1001
Chair(s): Francesco Rinaldi, Andrea Cristofari

• Dimensionality reduction techniques for derivative free optimization
  Coralia Cartis
• Solving 10,000-Dimensional Optimization Problems Using Inaccurate Function Values: An Old Algorithm
  Zaikun Zhang
• On the computation of the cosine measure in high dimensions.
  Scholar Sun
• A Novel Stochastic Derivative-Free Trust-Region Algorithm with Adaptive Sampling and Moving Ridge Functions
  Benjamin Rees, Christine Currie, Vuong Phan

WB-05: Recent advances in min-max optimization
Stream: Optimization for machine learning
Room: B100/4013
Chair(s): Ali Kavis, Aryan Mokhtari

• Steering Towards Success: Efficient Methods for Nonconvex-Nonconcave Minimax Problems
  Pontus Giselsson, Anton Åkerman, Max Nilsson, Manu Upadhyaya, Sebastian Banert
• A Universally Optimal Primal-Dual Method for Minimizing Heterogeneous Compositions
  Benjamin Grimmer
• Parameter-free second-order methods for min-max optimization
  Ali Kavis, Ruichen Jiang, Qiujiang Jin, Sujay Sanghavi, Aryan Mokhtari

MD-10: Interactions between optimization and machine learning
Stream: Zeroth and first-order optimization methods
Room: B100/8011
Chair(s): Cesare Molinari, Silvia Villa

• From learning to optimize to learning optimization algorithms
  Camille Castera, Peter Ochs
• The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
  Fabian Schaipp, Alexander Hägele, Adrien Taylor, Umut Simsekli, Francis Bach
• Greedy learning to optimise with convergence guarantees
  Patrick Fahy, Mohammad Golbabaee, Matthias J. Ehrhardt
• Learning from data via overparameterization
  Cesare Molinari, Silvia Villa, Lorenzo Rosasco, Cristian Vega, Hippolyte Labarrière

MC-01: Strategies to Improve Zeroth-Order Optimization Methods
Stream: Zeroth and first-order optimization methods
Room: B100/1001
Chair(s): Francesco Rinaldi, Andrea Cristofari

• Improving the robustness of zeroth-order optimization solvers
  Stefan M. Wild
• Derivative free optimization with structured random directions
  Silvia Villa, Marco Rando, Cheik Traoré, Cesare Molinari, Lorenzo Rosasco
• Enhancing finite-difference-based derivative-free optimization with machine learning
  Geovani Grapiglia, Timothé Taminiau, Estelle Massart
• A derivative-free algorithm based on resilient positive spanning sets
  Sébastien Kerleau, Clément Royer

MC-05: Optimization and machine learning II
Stream: Optimization for machine learning
Room: B100/4013
Chair(s): Laurent Condat

• Stabilized Proximal-Point Methods for Federated Optimization
  Xiaowen Jiang, Anton Rodomanov, Sebastian Stich
• A primal-dual algorithm for variational image reconstruction with learned convex regularizers
  Hok Shing Wong
• Entropic Mirror Descent for Linear Systems: Polyak’s Stepsize and Implicit Bias
  Alexander Posch, Yura Malitsky

MD-06: Smoothing techniques for nonsmooth optimization
Stream: Nonsmooth and nonconvex optimization
Room: B100/7013
Chair(s): Olivier Fercoq

• Second-order proximal-gradient methods for avoiding nonsmooth strict saddle points
  Alexander Bodard, Masoud Ahookhosh, Panagiotis Patrinos
• A Proximal Variable Smoothing for Nonsmooth Minimization of the Sum of Three Functions Including Weakly Convex Composite Function
  Keita Kume, Isao Yamada
• Analyzing the speed of convergence in nonsmooth optimization via the Goldstein epsilon-subdifferential
  Bennet Gebken
• Minimizing the smoothed gap to solve saddle point problems
  Olivier Fercoq

MB-08: Systematic and computer-aided analyses I: Analyses of proximal splittings methods & friends
Stream: Systematic and computer-aided analyses of optimization algorithms
Room: B100/7007
Chair(s): Aymeric Dieuleveut

• The Augmented Lagrangian Method for Infeasible Convex Optimization
  Roland Andrews
• Difference-of-convex algorithm with weakly convex functions: improved splitting technique and equivalence with proximal gradient descent
  Teodor Rotaru, Panagiotis Patrinos, François Glineur
• Forward-backward type splitting algorithms with minimal lifting I
  Anton Åkerman, Emanuele Naldi, Enis Chenchene, Pontus Giselsson
• Forward-backward type splitting algorithms with minimal lifting II
  Emanuele Naldi, Anton Åkerman, Enis Chenchene, Pontus Giselsson

MC-08: Systematic and computer-aided analyses II: Systematic algorithmic design approaches
Stream: Systematic and computer-aided analyses of optimization algorithms
Room: B100/7007
Chair(s): François Glineur

• Exact Verification of First-Order Methods via Mixed-Integer Linear Programming
  Vinit Ranjan, Jisun Park, Stefano Gualandi, Andrea Lodi, Bartolomeo Stellato
• Tight Analysis of Second-Order Optimization Methods via Interpolation of generalized Hessian Lipschitz Univariate Functions
  François Glineur, Nizar Bousselmi, Julien Hendrickx, Anne Rubbens
• Forward-backward algorithms with deviations
  Sebastian Banert

TB-04: Stochastic and Deterministic Global Optimization
Stream: Global optimization
Room: B100/5013
Chair(s): Dmitri Kvasov, Eligius M.T. Hendrix

• Global Optimization Algorithm through High-Resolution Sampling
  Daniel Cortild, Claire Delplancke, Nadia Oudjane, Juan Peypouquet
• ɛ-subdifferential methods for global DC optimization
  Adil Bagirov
• On global optimization of some nonlinear problems involving disjunctive constraints
  Sonia Cafieri, Marcel Mongeau, Sebastien Bourguignon, Gwenaël Samain
• Benchmarking tools for global optimization
  Dmitri Kvasov, Yaroslav Sergeyev

TB-05: Randomized Optimization algorithms I
Stream: Optimization for machine learning
Room: B100/4013
Chair(s): Laurent Condat

• Scalable Second-Order Optimization Algorithms for Minimizing Low-Rank Functions
  Edward Tansley, Coralia Cartis, Zhen Shao
• Distributed Optimization with Communication Compression
  Yuan Gao, Sebastian Stich
• Stochastic Gradient Descent without Variance Assumption: A Tight Lyapunov Analysis
  Lucas Ketels, Daniel Cortild, Guillaume Garrigos, Juan Peypouquet
• Communication-Efficient Algorithms for Federated Learning and Weakly Coupled Games
  Sebastian Stich, Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova

WB-09: Variational Analysis III
Stream: Variational analysis: theory and algorithms
Room: B100/8013
Chair(s): Francisco Javier Aragón Artacho

• Geometry and the complexity of first-order methods for Lipschitz optimization
  Adrian Lewis
• Inner approximations of convex sets and intersections of projectionally exposed cones
  Vera Roshchina
• Nonlinear Separation of Coradiant Sets: Optimality Conditions for Approximate Solutions
  Miguel Angel Melguizo Padial, Fernando García Castaño
• Nonsmooth optimization techniques for computing projected quasi-equilibria
  Giancarlo Bigi

WC-10: Computational Aspects in Multiobjective Optimization
Stream: Multiobjective and Vector Optimization
Room: B100/8011
Chair(s): Felix Neussel

• Stochastic approximation in convex multiobjective optimization
  Elena Molho, Carlo Alberto De Bernardi, Enrico Miglierina, Jacopo Somaglia
• Benchmarking Nonlinear Multi-Objective Optimizers in Julia
  Manuel Berkemeier
• Parametrized convex MINLP: Warm-starting with Outer Approximation for Sequence of MINLPs
  Erik Tamm, Gabriele Eichfelder, Jan Kronqvist
• On image space transformations in multiobjective optimization
  Felix Neussel, Oliver Stein

TB-10: First order methods: new perspectives for machine learning
Stream: Large scale optimization: methods and algorithms
Room: B100/8011
Chair(s): Cesare Molinari, Silvia Villa, Lorenzo Rosasco

• Convergence Analysis of Nonlinear Parabolic PDE Models with Neural Network Terms Trained with Gradient Descent
  Konstantin Riedl, Justin Sirignano, Konstantinos Spiliopoulos
• Randomized trust-region method for non-convex mimimization
  Radu-Alexandru Dragomir
• Perspectives on the analysis and design of optimization algorithms: Lyapunov analyses and counter-examples
  Adrien Taylor
• Accelerated Gradient Methods via Inertial Systems with Hessian-driven Damping
  Juan Peypouquet

TB-03: Theoretical and algorithmic advances in large scale nonlinear optimization and applications Part 1
Stream: Large scale optimization: methods and algorithms
Room: B100/4011
Chair(s): Stefania Bellavia, Benedetta Morini

• Fully stochastic trust-region methods with Barzilai-Borwein steplengths
  Benedetta Morini, Mahsa Yousefi, Stefania Bellavia
• prunAdag: an adaptive pruning-aware gradient method
  Giovanni Seraghiti, Margherita Porcelli, Philippe L. Toint
• An acceleration strategy for gradient methods in convex quadratic programming
  Gerardo Toraldo, Serena Crisci, Anna De Magistris, Valentina De Simone
• Corrective Frank-Wolfe: Unifying and Extending Correction Steps
  Jannis Halbey, Seta Rakotomandimby, Mathieu Besançon, Sebastian Pokutta

WC-05: Recent Advances in Stochastic Optimization
Stream: Optimization for machine learning
Room: B100/4013
Chair(s): Chuan He

• Almost sure convergence rates for stochastic gradient methods
  Simon Weissmann
• A Hessian-Aware Stochastic Differential Equation for Modelling SGD
  Xiang Li
• Complexity guarantees for risk-neutral generalized Nash equilibrium problems
  Meggie Marschner, Mathias Staudigl
• Stochastic first-order methods can leverage arbitrarily higher-order smoothness for acceleration
  Chuan He