solver

问题 I am wondering, what are the differences between ODEINT and solve_ivp for solving a differential equation. What could be advantages and disadvantages between them? f1 = solve_ivp(f, [0,1], y0) #y0 is the initial point f2 = odeint(f, y0, [0, 1], args=(a, b)) # a and b are arguments of function f Thank you 回答1: Well the main difference is the following: odeint came first and is uses lsoda from the FORTRAN package odepack to solve ODEs. solve_ivp is a more general solution that lets use decide

问题 I am wondering, what are the differences between ODEINT and solve_ivp for solving a differential equation. What could be advantages and disadvantages between them? f1 = solve_ivp(f, [0,1], y0) #y0 is the initial point f2 = odeint(f, y0, [0, 1], args=(a, b)) # a and b are arguments of function f Thank you 回答1: Well the main difference is the following: odeint came first and is uses lsoda from the FORTRAN package odepack to solve ODEs. solve_ivp is a more general solution that lets use decide

问题 I am wondering, what are the differences between ODEINT and solve_ivp for solving a differential equation. What could be advantages and disadvantages between them? f1 = solve_ivp(f, [0,1], y0) #y0 is the initial point f2 = odeint(f, y0, [0, 1], args=(a, b)) # a and b are arguments of function f Thank you 回答1: Well the main difference is the following: odeint came first and is uses lsoda from the FORTRAN package odepack to solve ODEs. solve_ivp is a more general solution that lets use decide

问题 Using excel solver, it is easy to find a solution (optimum value for x and y )for this equation: (x*14.80461) + (y * -4.9233) + (10*0.4803) ≈ 0 However, I can't figure out how to do this in Python. The existing scipy optimize library function like fsolve() or leastsq() seems to work with only one variable.... (I might just not know how to use them)... Any suggestions? Thanks! 回答1: >>> def f(x): ... return x[0]*14.80461 + x[1]*(-4.9233) + x[2]*(10*0.4803) >>> def vf(x): ... return [f(x), 0, 0]

问题 I'm trying to write an algorithm that can solve sudoku. For now, my code works till supplyGrid is out of numbers. When it happens it should go back and try another number, right? To be honest I have no clue how to achive that. var grid = [ [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0,

问题 I'm trying to write an algorithm that can solve sudoku. For now, my code works till supplyGrid is out of numbers. When it happens it should go back and try another number, right? To be honest I have no clue how to achive that. var grid = [ [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0,

问题 I'm trying to write an algorithm that can solve sudoku. For now, my code works till supplyGrid is out of numbers. When it happens it should go back and try another number, right? To be honest I have no clue how to achive that. var grid = [ [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0,

问题 I'm trying to write an algorithm that can solve sudoku. For now, my code works till supplyGrid is out of numbers. When it happens it should go back and try another number, right? To be honest I have no clue how to achive that. var grid = [ [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0,

问题 I wish to speed up the sparse system solver part of my code using Numba. Here is what I have up till now: # Both numba and numba-scipy packages are installed. I am using PyCharm IDE import numba import numba_scipy # import other required stuff @numba.jit(nopython=True) def solve_using_numba(A, b): return sp.linalg.gmres(A, b) # total = the number of points in the system A = sp.lil_matrix((total, total), dtype=float) # populate A with appropriate data A = A.tocsc() b = np.zeros((total, 1),

来源： https://stackoverflow.com/questions/54388977/is-there-a-way-to-see-progress-of-pulp-cbc-solver-on-jupyter-notebook