Optimization is a central field in artificial intelligence (AI) and applied mathematics, aiming to find the best possible solution to a given problem according to one or more quantifiable criteria. In AI, optimization often involves adjusting model parameters to minimize a cost (or loss) function or, more generally, to allocate resources optimally to achieve a goal. Optimization is distinguished from other technologies like brute-force search or heuristics by relying on formal mathematical methods to guarantee or approach an optimal solution. Its operation is based on algorithms capable of exploring a solution space, evaluating and progressively improving candidates according to precise rules.
Use Cases and Examples
In machine learning, optimization is used to adjust the weights of a neural network during training. It is also involved in optimal path planning for autonomous vehicles, intelligent energy management, logistics, and finance to maximize investment portfolios under constraints. Optimization also appears in automated design of structures or complex systems.
Main Software Tools, Libraries, and Frameworks
Key tools include scientific computing libraries like SciPy (Python), which offers various optimization solvers, and CVXPY for convex programming. In machine learning, frameworks such as TensorFlow and PyTorch include optimizers like SGD, Adam, or RMSProp. Gurobi, CPLEX, and Google's OR-Tools are powerful solvers for large-scale combinatorial and linear optimization.
Recent Developments, Evolutions, and Trends
Optimization benefits from advances in distributed and quantum computing, allowing larger and more complex problems to be tackled. Recent trends include differentiable optimization, Bayesian optimization for automated hyperparameter tuning, and hybrid approaches combining deep learning and classical optimization. The integration of optimization in autonomous systems and the rise of open-source solvers are also shaping the field.