Nonlinear Gradient Descent Methods
Metron scientists use nonlinear gradient descent methods to find optimal solutions to complex resource allocation problems and train neural networks.Join Our Team
Optimization can lead to savings in time, reduction in computational costs, as well as helping discover solutions to complex mechanical, resource allocation and positioning, and machine learning problems.
Nonlinear Gradient Descent
Nonlinear gradient descent methods are one type of optimization tool useful for finding the minimum (or maximum) value of a function. They involve taking a sequence of steps, each of which generally moves in the direction of steepest decrease of the function. Metron data scientists have applied nonlinear gradient descent to a wide range of problems from positioning of mobile assets to satisfy a joint goal, training deep neural networks, and solving joint control strategies for mechanical systems.
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Metron hires research scientists with experience developing novel approaches that advance the state of the art in mathematics and artificial intelligence. Our scientists work alongside subject matter experts applying these innovations to new problem domains.