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jaxopt_gradient_descent_quadratic_function_minimization.py
pythonThis quickstart demonstrates how to solve a simple optimization problem by minimi
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jaxopt_gradient_descent_quadratic_function_minimization.py
1import jax
2import jax.numpy as jnp
3from jaxopt import GradientDescent
4
5def f(x, params):
6 return jnp.sum(0.5 * params * x ** 2)
7
8# Initial parameters
9x0 = jnp.array([1.0, 2.0])
10params = jnp.array([1.0, 10.0])
11
12# Initialize the solver
13gd = GradientDescent(fun=f, maxiter=100)
14
15# Run the solver
16res = gd.run(init_params=x0, params=params)
17
18# The result is a named tuple containing the solution
19print(f"Optimal solution: {res.params}")
20print(f"Objective value: {f(res.params, params)}")