Morteza Kimiaei – Optimization Online

A particle swarm optimization with a reduced-subspace trust-region safeguard for contact-rich controller tuning

Published: 2026/10/23

Bound-constrained Optimization, Control Applications, Optimization of Simulated Systems adaptive subspace technique, contact-rich robotic assembly, controller tuning, derivative-free optimization, derivative-free policy optimization, particle swarm optimization, trust-region methods

Contact-rich robotic insertion is challenging because uncertainty in alignment, friction, and contact conditions can lead to unsuccessful assembly, excessive contact forces, and jamming. Designing a reliable controller therefore requires balancing insertion performance, robustness, and recovery from unexpected contact. In this paper, we propose PSO+TR, a derivative-free optimization (DFO) method that combines the exploratory search of … Read more

Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing

Published: 2026/10/10

Constrained Nonlinear Optimization, Optimization of Simulated Systems, Optimization of Systems modeled by PDEs chance-constrained controller tuning, derivative-free line search, evolution strategy, finite-sample validation, Model Predictive Path Integral, rank uncertainty, statistical racing

We formulate offline tuning of Model Predictive Path Integral (MPPI) controllers as a chance-constrained black-box optimization problem and develop an update-aware framework based on Matrix Adaptation Evolution Strategy (MA-ES). We use statistical information from noisy controller evaluations to determine when the evolutionary update is sufficiently reliable. When the recombination direction is certified, a derivative-free nonmonotone … Read more

A Direction Adaptation Evaluation Strategy for Noisy Derivative-Free Optimization

Published: 2026/07/08

Global Optimization, Nonlinear Optimization Complexity Results, Direction Adaptation, evolution strategy, heuristic optimization, Noisy derivative-free optimization, randomized optimization

In this paper, we develop a direction adaptation evolution strategy (DAES)—a new MAES-type method—for noisy derivative-free optimization, designed to reconcile the population-based search mechanisms of evolution strategies with rigorous complexity analysis. Unlike standard MAES schemes, DAES fixes the adaptation matrix to the identity and replaces matrix adaptation with a structured direction-generation mechanism based on symmetric … Read more

An efficient penalty decomposition algorithm for minimization over sparse symmetric sets

Published: 2026/01/17

Nonlinear Optimization, Nonsmooth Optimization diagonal quasi-Newton method, global convergence, line search method, penalty decomposition method, sparse optimization

This paper proposes an improved quasi-Newton penalty decomposition algorithm for the minimization of continuously differentiable functions, possibly nonconvex, over sparse symmetric sets. The method solves a sequence of penalty subproblems approximately via a two-block decomposition scheme: the first subproblem admits a closed-form solution without sparsity constraints, while the second subproblem is handled through an efficient … Read more

AI for Enhancing Operations Research of Agriculture and Energy

Published: 2025/12/29

(Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Global Optimization Agricultural Planning, energy systems, Learning-Enhanced Optimization, mixed-integer nonlinear programming, optimal power flow, reinforcement learning, stochastic optimization, unit commitment

This paper surveys optimization problems arising in agriculture, energy systems, and water-energy coordination from an operations research perspective. These problems are commonly formulated as integer nonlinear programs, mixed-integer nonlinear programs, or combinatorial set optimization models, characterized by nonlinear physical constraints, discrete decisions, and intertemporal coupling. Such structures pose significant computational challenges in large-scale and repeated-solution … Read more

Learning-Augmented Combinatorial Solvers: A Solver-Architecture Taxonomy

Published: 2025/11/09, Updated: 2026/10/05

Applications - OR and Management Sciences, Combinatorial Optimization, Optimization in Data Science Boolean Satisfiability, Conflict-Driven Clause Learning, Constraint Satisfaction Problem, Graph Neural Networks, Hybrid Solvers, Lazy Clause Generation, Learned Heuristics, machine learning, Neuro-Symbolic Optimization, reinforcement learning, Transformer Models

This paper presents a solver-architecture taxonomy of machine-learning and reinforcement-learning interventions in combinatorial solving. Rather than treating learning-augmented solving as a collection of isolated applications, we organize existing methods according to the solver-level decisions they influence, including propagation, branching, conflict analysis, restart control, structural analysis, and formulation-level reasoning. The discussion focuses on SAT/CDCL, Lazy Clause … Read more

Machine Learning Algorithms for Assisting Solvers for Decision Optimization Problems

Published: 2025/11/09

Applications - OR and Management Sciences, Integer Programming, Optimization in Data Science backward induction, combinatorial optimization, disjunctive programming, global optimization, machine learning, neural networks, probabilistic programming, reinforcement learning, submodular optimization

Combinatorial decision problems lie at the intersection of Operations Research (OR) and Artificial Intelligence (AI), encompassing structured optimization tasks such as submodular selection, dynamic programming, planning, and scheduling. These problems exhibit exponential growth in decision complexity, driven by interdependent choices coupled through logical, temporal, and resource constraints. Classical optimization frameworks—including integer programming, submodular optimization, and … Read more

Machine Learning Algorithms for Improving Black Box Optimization Solvers

Published: 2025/09/29

Global Optimization, Nonlinear Optimization, Optimization in Data Science black-box optimization, machine learning, Meta-Black-Box Optimization, reinforcement learning, robust optimization, surrogate models

Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods—line search, direct search, and model-based solvers such as Bayesian optimization—form the backbone of BBO, yet often struggle in high-dimensional, noisy, or mixed-integer settings. Recent advances use machine learning (ML) and reinforcement learning (RL) to … Read more

Restarting nonlinear conjugate gradient methods

Published: 2025/01/14

Nonlinear Optimization global convergence, local convergence, nonlinear conjugate gradient method, nonlinear optimization, restart procedure

In unconstrained optimization, due to the nonlinearity of the objective function or rounding errors in finite precision arithmetic, it can happen that NaN or infinite step sizes appear in the nonlinear conjugate gradient (NCG) method, or otherwise the step violates the sufficient descent condition (SDC). In this case the conjugate gradient (CG) direction must often … Read more

A class of diagonal quasi-Newton penalty decomposition algorithms for sparse bound-constrained nonconvex optimization

Published: 2025/01/14

Bound-constrained Optimization, Constrained Nonlinear Optimization, Quadratic Programming Cardinality bound-constrained optimization, diagonal quasi-Newton method, global convergence, penalty decomposition method

This paper discusses an improved quasi-Newton penalty decomposition algorithm for the cardinality bound-constrained optimization problems whose simple bounds on the variables are assumed to be finite. Until an approximate stationary point is found, this algorithm approximates the solutions of a sequence of penalty subproblems by a two-block decomposition scheme. This scheme finds an approximate solution … Read more