# CVode

*CVode* (short for C-language Variable-coefficients ODE solver) is a numerical solver providing an efficient and stable method to solve Ordinary Differential Equations (ODEs) Initial Value Problems. It uses either *BDF* or *Adams* as implicit integration method, and *Newton* or *Functional* iterations.

### Description

Called by xcos, *CVode* (short for C-language Variable-coefficients ODE solver) is a numerical solver providing an efficient and stable method to solve Initial Value Problems of the form:

Starting with
*y _{0}*
,

*CVode*approximates

*y*with the formula:

_{n+1}

with
*y _{n}*
the approximation of

*y(t*, and

_{n})*h*=

_{n}*t*the step size.

_{n}- t_{n-1}These implicit methods are characterized by their respective order *q*, which indicates the number of intermediate points required to compute
*y _{n+1}*
.

This is where the difference between *BDF* and *Adams* intervenes (*Backward Differentiation Formula* and *Adams-Moulton formula*):

If the problem is stiff, the user should select BDF: |

*q*, the order of the method, is set between 1 and 5 (automated),*K1 = q*and*K2 = 0*.

In the case of nonstiffness, *Adams* is preferred:

*q*is set between 1 and 12 (automated),*K1 = 1*and*K2 = q*.

The coefficients are fixed, uniquely determined by the method type, its order, the history of the step sizes, and the normalization
*α _{n, 0} = -1*
.

For either choice and at each step, injecting this integration in *(1)* yields the nonlinear system:

This system can be solved by either *Functional* or *Newton* iterations, described hereafter.

In both following cases, the initial "predicted"
*y _{n(0)}*
is explicitly computed from the history data, by adding derivatives.

**Functional:**this method only involves evaluations of*f*, it simply computes*y*by iterating the formula:_{n(0)}**Newton:**here, we use an implemented direct dense solver on the linear system:

In both situations, *CVode* uses the history array to control the local error
*y _{n(m)} - y_{n(0)}*
and recomputes

*h*if that error is not satisfying.

_{n}**The recommended choices are BDF / Newton for stiff problems and Adams / Functional for the nonstiff ones.**

The function is called in between activations, because a discrete activation may change the system.

Following the criticality of the event (its effect on the continuous problem), we either relaunch the solver with different start and final times as if nothing happened, or, if the system has been modified, we need to "cold-restart" the problem by reinitializing it anew and relaunching the solver.

Averagely, *CVode* accepts tolerances up to 10^{-16}. Beyond that, it returns a *Too much accuracy* requested error.

### Examples

The integral block returns its continuous state, we can evaluate it with *BDF / Newton* by running the example:

// Import the diagram and set the ending time loadScicos(); loadXcosLibs(); importXcosDiagram("SCI/modules/xcos/examples/solvers/ODE_Example.zcos"); scs_m.props.tf = 5000; // Select the solver BDF / Newton scs_m.props.tol(6) = 1; // Start the timer, launch the simulation and display time tic(); try xcos_simulate(scs_m, 4); catch disp(lasterror()); end t = toc(); disp(t, "Time for BDF / Newton:");

The Scilab console displays:

Time for BDF / Newton: 13.438

Now, in the following script, we compare the time difference between the methods by running the example with the four solvers in turn: Open the script

Results:

Time for BDF / Newton: 18.894 Time for BDF / Functional: 18.382 Time for Adams / Newton: 10.368 Time for Adams / Functional: 9.815

The results show that for a simple nonstiff continuous problem, *Adams / Functional* is fastest.

### See also

- LSodar — LSodar (short for Livermore Solver for Ordinary Differential equations, with Automatic method switching for stiff and nonstiff problems, and with Root-finding) is a numerical solver providing an efficient and stable method to solve Ordinary Differential Equations (ODEs) Initial Value Problems.
- Runge-Kutta 4(5) — Runge-Kutta is a numerical solver providing an efficient explicit method to solve Ordinary Differential Equations (ODEs) Initial Value Problems.
- Dormand-Prince 4(5) — Dormand-Prince is a numerical solver providing an efficient explicit method to solve Ordinary Differential Equations (ODEs) Initial Value Problems.
- Implicit Runge-Kutta 4(5) — Numerical solver providing an efficient and stable implicit method to solve Ordinary Differential Equations (ODEs) Initial Value Problems.
- Crank-Nicolson 2(3) — Crank-Nicolson is a numerical solver based on the Runge-Kutta scheme providing an efficient and stable implicit method to solve Ordinary Differential Equations (ODEs) Initial Value Problems. Called by xcos.
- IDA — Implicit Differential Algebraic equations system solver, providing an efficient and stable method to solve Differential Algebraic Equations system (DAEs) Initial Value Problems.
- DDaskr — Double-precision Differential Algebraic equations system Solver with Krylov method and Rootfinding: numerical solver providing an efficient and stable method to solve Differential Algebraic Equations systems (DAEs) Initial Value Problems
- Comparisons — This page compares solvers to determine which one is best fitted for the studied problem.
- ode — 常微分方程式ソルバ
- ode_discrete — 常微分方程式ソルバ, 離散時間シミュレーション
- ode_root — 求解付きの常微分方程式ソルバ
- odedc — 離散/連続 ODE ソルバ
- impl — 微分代数方程式

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