1.218 problem 219

1.218.1 Maple step by step solution

Internal problem ID [8555]
Internal file name [OUTPUT/7488_Sunday_June_05_2022_11_01_21_PM_40300235/index.tex]

Book: Differential Gleichungen, E. Kamke, 3rd ed. Chelsea Pub. NY, 1948
Section: Chapter 1, linear first order
Problem number: 219.
ODE order: 1.
ODE degree: 1.

The type(s) of ODE detected by this program : "unknown"

Maple gives the following as the ode type

[[_Abel, `2nd type`, `class A`]]

Unable to solve or complete the solution.

\[ \boxed {\left (y+g \left (x \right )\right ) y^{\prime }-f_{2} \left (x \right ) y^{2}-f_{1} \left (x \right ) y=f_{0} \left (x \right )} \] Unable to determine ODE type.

1.218.1 Maple step by step solution

\[ \begin {array}{lll} & {} & \textrm {Let's solve}\hspace {3pt} \\ {} & {} & \left (y+g \left (x \right )\right ) y^{\prime }-f_{2} \left (x \right ) y^{2}-f_{1} \left (x \right ) y=f_{0} \left (x \right ) \\ \bullet & {} & \textrm {Highest derivative means the order of the ODE is}\hspace {3pt} 1 \\ {} & {} & y^{\prime } \\ \bullet & {} & \textrm {Solve for the highest derivative}\hspace {3pt} \\ {} & {} & y^{\prime }=\frac {f_{2} \left (x \right ) y^{2}+f_{1} \left (x \right ) y+f_{0} \left (x \right )}{y+g \left (x \right )} \end {array} \]

Maple trace

`Methods for first order ODEs: 
--- Trying classification methods --- 
trying a quadrature 
trying 1st order linear 
trying Bernoulli 
trying separable 
trying inverse linear 
trying homogeneous types: 
trying Chini 
differential order: 1; looking for linear symmetries 
trying exact 
trying Abel 
Looking for potential symmetries 
Looking for potential symmetries 
Looking for potential symmetries 
trying inverse_Riccati 
trying an equivalence to an Abel ODE 
differential order: 1; trying a linearization to 2nd order 
--- trying a change of variables {x -> y(x), y(x) -> x} 
differential order: 1; trying a linearization to 2nd order 
trying 1st order ODE linearizable_by_differentiation 
--- Trying Lie symmetry methods, 1st order --- 
`, `-> Computing symmetries using: way = 3 
`, `-> Computing symmetries using: way = 4 
`, `-> Computing symmetries using: way = 2 
trying symmetry patterns for 1st order ODEs 
-> trying a symmetry pattern of the form [F(x)*G(y), 0] 
-> trying a symmetry pattern of the form [0, F(x)*G(y)] 
-> trying symmetry patterns of the forms [F(x),G(y)] and [G(y),F(x)] 
`, `-> Computing symmetries using: way = HINT 
   -> Calling odsolve with the ODE`, diff(y(x), x) = -y(x)*(diff(f__2(x), x))/f__2(x), y(x)`      *** Sublevel 2 *** 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+y(x)*(diff(f__2(x), x))/f__2(x), y(x)`      *** Sublevel 2 *** 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)-(y(x)*f__0(x)*(diff(g(x), x))-K[1]*f__1(x)*g(x)-y(x)*(diff(f__0(x), x))*g(x)+K[1] 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+(y(x)*(diff(f__2(x), x))*g(x)-y(x)*f__2(x)*(diff(g(x), x))+f__2(x)*K[1]+y(x)*(dif 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+(2*f__2(x)*g(x)*K[1]+y(x)*(diff(f__1(x), x))*g(x)-y(x)*f__1(x)*(diff(g(x), x))+y( 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)-(K[1]*f__0(x)*g(x)+y(x)*f__0(x)*(diff(g(x), x))-y(x)*(diff(f__0(x), x))*g(x))/(f_ 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)-(-y(x)*(diff(f__1(x), x))*g(x)+y(x)*f__1(x)*(diff(g(x), x))+2*K[1]*f__0(x)-y(x)*( 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+(y(x)*(diff(f__2(x), x))*g(x)+f__2(x)*g(x)*K[1]-y(x)*f__2(x)*(diff(g(x), x))+y(x) 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
`, `-> Computing symmetries using: way = HINT 
   -> Calling odsolve with the ODE`, diff(y(x), x)-(y(x)*f__0(x)*(diff(g(x), x))-y(x)*(diff(f__0(x), x))*g(x)-K[1]*f__1(x)*g(x)+K[1] 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+(y(x)*(diff(f__2(x), x))*g(x)-y(x)*f__2(x)*(diff(g(x), x))+y(x)*(diff(f__1(x), x) 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+(y(x)*(diff(f__1(x), x))*g(x)-y(x)*f__1(x)*(diff(g(x), x))+2*f__2(x)*g(x)*K[1]+y( 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+y(x)*(g(x)*(diff(f__2(x), x))-(diff(g(x), x))*f__2(x)+diff(f__1(x), x))/(f__2(x)* 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)-(y(x)*f__0(x)*(diff(g(x), x))-y(x)*(diff(f__0(x), x))*g(x)-3*f__2(x)*g(x)*K[1]-f_ 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
   -> Calling odsolve with the ODE`, diff(y(x), x)+(y(x)*(diff(f__1(x), x))*g(x)-y(x)*f__1(x)*(diff(g(x), x))+y(x)*(diff(f__0(x), x) 
      Methods for first order ODEs: 
      --- Trying classification methods --- 
      trying a quadrature 
      trying 1st order linear 
      <- 1st order linear successful 
-> trying a symmetry pattern of the form [F(x),G(x)] 
-> trying a symmetry pattern of the form [F(y),G(y)] 
-> trying a symmetry pattern of the form [F(x)+G(y), 0] 
-> trying a symmetry pattern of the form [0, F(x)+G(y)] 
-> trying a symmetry pattern of the form [F(x),G(x)*y+H(x)] 
-> trying a symmetry pattern of conformal type`
 

Solution by Maple

dsolve((y(x)+g(x))*diff(y(x),x)-f__2(x)*y(x)^2-f__1(x)*y(x)-f__0(x)=0,y(x), singsol=all)
 

\[ \text {No solution found} \]

Solution by Mathematica

Time used: 0.0 (sec). Leaf size: 0

DSolve[(y[x]+g[x])*y'[x]-f2[x]*y[x]^2-f1[x]*y[x]-f0[x]==0,y[x],x,IncludeSingularSolutions -> True]
 

Timed out