The Net Advance of Physics

The Net Advance of Physics:
OPTIMIZATION

General:
- A Collection of Challenging Optimization Problems in Science, Engineering and Economics by Dhagash Mehta and Crina Grosan [2015/04]
- A Survey of Optimization Methods from a Machine Learning Perspective by Shiliang Sung et al. [2019/06]
- Distributionally Robust Optimization: A Review by Hamed Rahimian and Sanjay Mehrotra [2019/08]
- Lecture Notes: Optimization for Machine Learning by Elad Hazan [2019/09]
- A Brief Tour of Logic and Optimization by John Hooker [2019/10]
- Optimization for Deep Learning: Theory and Algorithms by R. Sun [2019/12]
- Tutorials on advanced optimization methods by W. Wei [2020/07]
- Handbook of convergence theorems for (stochastic) gradient methods by G. Garrigos and R. M. Gower [2023/01]
- An overview of diffusion models [in AI]: Applications, guided generation, statistical rates, and optimization by M.-S. Chen et al. [2024/04]
- Learning To Optimize (L2O): A tutorial for continuous and mixed-integer optimization by X.-H. Chen et al. [2024/05]
- A survey of optimization methods for training DL models: Theoretical perspectives on convergence and generalization by J. Wang and A. Choromanska [2025/01]
- What is the best way to do something? A discreet tour of discrete optimization by T. Serra [2025/09]
Type: ANT COLONY:
- Evolution of Ant Colony Optimization Algorithm: A Brief Literature Review by Aleem Akhtar Asif [2019/08]
Type: BAYESIAN:
- A Tutorial on Bayesian Optimization by Peter I. Frazier [2018/07]
- Bayesian optimization in materials science: A survey by L. Kotthoff et al. [2021/08]
Type: BILEVEL:
- A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications by Ankur Sinha et al. [2017/05]
Type: BIO-INSPIRED:
- Nature-inspired optimization algorithms: challenges and open problems by X.-S. Yang [2020/03]
Type: COMBINATORIAL:
- Reinforcement learning for combinatorial optimization: a survey by N. Mazyavkina et al. [2020/03]
Type: CONVEX:
- Lecture notes: Convex optimization by A. Habring [2026/07]
Type: CUCKOO:
- Cuckoo Search: Recent Advances and Applications by Xin-She Yang and Suash Deb [2014/08]
- Cuckoo Search: A Brief Literature Review by I. Fister Jr. et al. [2014/08]
Type: DYNAMIC:
- A course in dynamic optimization by B. Light [2024/08]
Type: HIDDEN CONVEX:
- A Survey of Hidden Convex Optimization by Yong Xia [2019/02]
Type: INTERPOLATION-BASED:
- Introduction to interpolation-based optimization by L. Roberts [2025/10]
Type: LAGRANGIAN:
- The augmented Lagrangian methods [of optimization]: Overview and recent advances by K.-K. Deng et al. [2025/10]
Type: MULTIMODAL:
- Evolutionary Multimodal Optimization: A Short Survey by Ka-Chun Wong [2015/08]
Type: MULTI-TIME-SCALE:
- A tutorial on multi-time-scale optimization models and algorithms by A. Ramanujam and C. Li [2025/02]
Type: QUANTUM:
- A review on Quantum Approximate Optimization Algorithm and its variants by K. Blekos et al. [2023/06]
Type: QUBO MODELS:
- A Tutorial on Formulating Quadratic Unconstrained Binary Optimization Models by Fred Glover and Gary Kochenberger [2018/11]
Type: ROBUST:
- A Practical Guide to Robust Optimization by Bram L. Gorissen et al. [2015/01]
Type: SHAPE:
- A survey on design-space dimensionality-reduction methods for shape optimization by A. Serani and M. Diez [2024/05]
Type: SPARSE:
- A first course in sparse optimization by J. Lu [2026/01]
Type: STOCHASTIC:
- A guide to stochastic optimisation for large-scale inverse problems by M. J. Ehrhardt et al. [2024/06]
- A short survey of averaging techniques in stochastic gradient methods by K. Lakshmanan [2026/03]
- Introduction to stochastic gradient methods by S. Weissmann [2026/06]
Type: TIME-VARYING:
- Time-Varying Optimization: Algorithms and Engineering Applications by Andrea Simonetto [2018/07]
Type: WIRTINGER:
- A short tutorial on Wirtinger calculus with applications in quantum information by K. Koor et al. [2023/12]
Aspects: CYCLIC MONOTONICITY:
- Sixty years of cyclic monotonicity: A survey by A. Kausamo et al. [2023/08]
Aspects: FRANK--WOLFE ALGORITHM:
- The Frank--Wolfe algorithm: A short introduction by S. Pokutta [2023/11]
Re: HYPERSPECTRAL UNMIXING:
- Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches by José M. Bioucas-Dias et al. [2012/02]
Re: NON-CONVEX PROBLEMS:
- An overview of optimal control optimization problems driven by non-convexity measures by WX Wang [2020/12]
- Lecture notes on non-convex algorithms for low-rank matrix recovery by I. Waldspurger [2021/05]
Re: QUANTUM ANNEALING:
- Quantum Annealing and Quantum Fluctuation Effect in Frustrated Ising Systems by Shu Tanaka and Ryo Tamura [2012/02]
- Notes on Adiabatic Quantum Computers by Boaz Tamir and Eliahu Cohen [2015/12]
- Quantum annealing: Optimisation, sampling, and many-body dynamics by S. Abel et al. [2026/05]
Re: QUANTUM COMPUTING:
- An introduction to variational quantum algorithms on gate-based quantum computing for combinatorial optimization problems by C. Grange et al. [2022/12]
Re: QUANTUM INFORMATION:
- A short tutorial on Wirtinger calculus with applications in quantum information by K. Koor et al. [2023/12]
Re: SET-VALUED FUNCTIONS:
- Set optimization - a rather short introduction by Andreas H. Hamel et al. [2014/04]
Re: SIGNAL PROCESSING:
- Tutorial on Stochastic Simulation and Optimization Methods in Signal Processing by Marcelo Pereyra et al. [2015/05]
Re: SIMULATION:
- Review of large-scale simulation optimization by W.-W. Fan et al. [2024/03]
Re: SYMMETRIC MANIFOLDS:
- On the works of Avi Wigderson by B. Barak et al. [2023/07]

THE NET ADVANCE OF PHYSICS