Update ode.md

Method descriptions for the solvers added.

Author: ParasPuneetSingh

docs/src/optimization_packages/ode.md

@@ -37,14 +37,21 @@ sol = solve(prob_manual, opt; maxiters=50_000)
 
 ## Available Optimizers
 
-* `ODEGradientDescent(dt=...)` — uses the explicit Euler method.
-* `RKChebyshevDescent()` — uses the ROCK2 method.
-* `RKAccelerated()` — uses the Tsit5 Runge-Kutta method.
-* `HighOrderDescent()` — uses the Vern7 high-order Runge-Kutta method.
+All provided optimizers are **gradient-based local optimizers** that solve optimization problems by integrating gradient-based ODEs to convergence:
+
+* `ODEGradientDescent(dt=...)` — performs basic gradient descent using the explicit Euler method. This is a simple and efficient method suitable for small-scale or well-conditioned problems.
+
+* `RKChebyshevDescent()` — uses the ROCK2 solver, a stabilized explicit Runge-Kutta method suitable for stiff problems. It allows larger step sizes while maintaining stability.
+
+* `RKAccelerated()` — leverages the Tsit5 method, a 5th-order Runge-Kutta solver that achieves faster convergence for smooth problems by improving integration accuracy.
+
+* `HighOrderDescent()` — applies Vern7, a high-order (7th-order) explicit Runge-Kutta method for even more accurate integration. This can be beneficial for problems requiring high precision.
+
+You can also define a custom optimizer using the generic `ODEOptimizer(solver; dt=nothing)` constructor by supplying any ODE solver supported by [OrdinaryDiffEq.jl](https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/).
 
 ## Interface Details
 
-All optimizers require gradient information (either via automatic differentiation or manually provided `grad!`).
+All optimizers require gradient information (either via automatic differentiation or manually provided `grad!`). The optimization is performed by integrating the ODE defined by the negative gradient until a steady state is reached.
 
 ### Keyword Arguments