Local Minimum

Local minima represent suboptimal solutions in optimization problems, hindering the search for global optima across diverse fields like robotics, machine learning, and quantum computing. Current research focuses on developing algorithms and strategies to escape these minima, employing techniques such as neural network augmentation, evolutionary search, and modified gradient descent methods tailored to specific problem structures (e.g., low-rank matrix optimization, training of neural networks). Overcoming the challenge of local minima is crucial for improving the efficiency and effectiveness of optimization algorithms, impacting the performance of various applications from robot navigation to training complex machine learning models.