sympy/solvers/solvers.py

   1
   2 """ This module contain solvers for all kinds of equations:
   3
   4     - algebraic, use solve()
   5
   6     - recurrence, use rsolve()
   7
   8     - differential, use dsolve()
   9
  10     -transcendental, use tsolve()
  11
  12     -nonlinear (numerically), use msolve() (you will need a good starting point)
  13
  14 """
  15
  16 from sympy.core.sympify import sympify
  17 from sympy.core.basic import Basic, S, C
  18 from sympy.core.symbol import Symbol, Wild
  19 from sympy.core.relational import Equality
  20 from sympy.core.function import Derivative, diff
  21
  22 from sympy.functions import sqrt, log, exp, LambertW
  23 from sympy.simplify import simplify, collect
  24 from sympy.matrices import Matrix, zeros
  25 from sympy.polys import roots
  26
  27 from sympy.utilities import any, all
  28 from sympy.utilities.lambdify import lambdify
  29 from sympy.solvers.numeric import newton
  30
  31 from sympy.solvers.polysys import solve_poly_system
  32
  33 def solve(f, *symbols, **flags):
  34     """Solves equations and systems of equations.
  35
  36        Currently supported are univariate polynomial and transcendental
  37        equations and systems of linear and polynomial equations.  Input
  38        is formed as a single expression or an equation,  or an iterable
  39        container in case of an equation system.  The type of output may
  40        vary and depends heavily on the input. For more details refer to
  41        more problem specific functions.
  42
  43        By default all solutions are simplified to make the output more
  44        readable. If this is not the expected behavior,  eg. because of
  45        speed issues, set simplified=False in function arguments.
  46
  47        To solve equations and systems of equations of other kind, eg.
  48        recurrence relations of differential equations use rsolve() or
  49        dsolve() functions respectively.
  50
  51        >>> from sympy import *
  52        >>> x,y = symbols('xy')
  53
  54        Solve a polynomial equation:
  55
  56        >>> solve(x**4-1, x)
  57        [1, -1, -I, I]
  58
  59        Solve a linear system:
  60
  61        >>> solve((x+5*y-2, -3*x+6*y-15), x, y)
  62        {x: -3, y: 1}
  63
  64     """
  65     if not symbols:
  66         raise ValueError('no symbols were given')
  67
  68     if len(symbols) == 1:
  69         if isinstance(symbols[0], (list, tuple, set)):
  70             symbols = symbols[0]
  71
  72     symbols = map(sympify, symbols)
  73
  74     if any(not s.is_Symbol for s in symbols):
  75         raise TypeError('not a Symbol')
  76
  77     if not isinstance(f, (tuple, list, set)):
  78         f = sympify(f)
  79
  80         if isinstance(f, Equality):
  81             f = f.lhs - f.rhs
  82
  83         if len(symbols) == 1:
  84             poly = f.as_poly(*symbols)
  85
  86             if poly is not None:
  87                 result = roots(poly, cubics=True, quartics=True).keys()
  88             else:
  89                 result = [tsolve(f, *symbols)]
  90         else:
  91             raise NotImplementedError('multivariate equation')
  92
  93         if flags.get('simplified', True):
  94             return map(simplify, result)
  95         else:
  96             return result
  97     else:
  98         if not f:
  99             return {}
 100         else:
 101             polys = []
 102
 103             for g in f:
 104                 g = sympify(g)
 105
 106                 if isinstance(g, Equality):
 107                     g = g.lhs - g.rhs
 108
 109                 poly = g.as_poly(*symbols)
 110
 111                 if poly is not None:
 112                     polys.append(poly)
 113                 else:
 114                     raise NotImplementedError
 115
 116             if all(p.is_linear for p in polys):
 117                 n, m = len(f), len(symbols)
 118                 matrix = zeros((n, m + 1))
 119
 120                 for i, poly in enumerate(polys):
 121                     for coeff, monom in poly.iter_terms():
 122                         try:
 123                             j = list(monom).index(1)
 124                             matrix[i, j] = coeff
 125                         except ValueError:
 126                             matrix[i, m] = -coeff
 127
 128                 return solve_linear_system(matrix, *symbols, **flags)
 129             else:
 130                 return solve_poly_system(polys)
 131
 132 def solve_linear_system(system, *symbols, **flags):
 133     """Solve system of N linear equations with M variables, which means
 134        both Cramer and over defined systems are supported. The possible
 135        number of solutions is zero, one or infinite. Respectively this
 136        procedure will return None or dictionary with solutions. In the
 137        case of over definend system all arbitrary parameters are skiped.
 138        This may cause situation in with empty dictionary is returned.
 139        In this case it means all symbols can be assigne arbitray values.
 140
 141        Input to this functions is a Nx(M+1) matrix, which means it has
 142        to be in augmented form. If you are unhappy with such setting
 143        use 'solve' method instead, where you can input equations
 144        explicitely. And don't worry aboute the matrix, this function
 145        is persistent and will make a local copy of it.
 146
 147        The algorithm used here is fraction free Gaussian elimination,
 148        which results, after elimination, in upper-triangular matrix.
 149        Then solutions are found using back-substitution. This approach
 150        is more efficient and compact than the Gauss-Jordan method.
 151
 152        >>> from sympy import *
 153        >>> x, y = symbols('xy')
 154
 155        Solve the following system:
 156
 157               x + 4 y ==  2
 158            -2 x +   y == 14
 159
 160        >>> system = Matrix(( (1, 4, 2), (-2, 1, 14)))
 161        >>> solve_linear_system(system, x, y)
 162        {x: -6, y: 2}
 163
 164     """
 165     matrix = system[:,:]
 166     syms = list(symbols)
 167
 168     i, m = 0, matrix.cols-1  # don't count augmentation
 169
 170     while i < matrix.lines:
 171         if i == m:
 172             # an overdetermined system
 173             if any(matrix[i:,m]):
 174                 return None   # no solutions
 175             else:
 176                 # remove trailing rows
 177                 matrix = matrix[:i,:]
 178                 break
 179
 180         if not matrix[i, i]:
 181             # there is no pivot in current column
 182             # so try to find one in other colums
 183             for k in xrange(i+1, m):
 184                 if matrix[i, k]:
 185                     break
 186             else:
 187                 if matrix[i, m]:
 188                     return None   # no solutions
 189                 else:
 190                     # zero row or was a linear combination of
 191                     # other rows so now we can safely skip it
 192                     matrix.row_del(i)
 193                     continue
 194
 195             # we want to change the order of colums so
 196             # the order of variables must also change
 197             syms[i], syms[k] = syms[k], syms[i]
 198             matrix.col_swap(i, k)
 199
 200         pivot_inv = S.One / matrix [i, i]
 201
 202         # divide all elements in the current row by the pivot
 203         matrix.row(i, lambda x, _: x * pivot_inv)
 204
 205         for k in xrange(i+1, matrix.lines):
 206             if matrix[k, i]:
 207                 coeff = matrix[k, i]
 208
 209                 # subtract from the current row the row containing
 210                 # pivot and multiplied by extracted coefficient
 211                 matrix.row(k, lambda x, j: simplify(x - matrix[i, j]*coeff))
 212
 213         i += 1
 214
 215     # if there weren't any problmes, augmented matrix is now
 216     # in row-echelon form so we can check how many solutions
 217     # there are and extract them using back substitution
 218
 219     simplified = flags.get('simplified', True)
 220
 221     if len(syms) == matrix.lines:
 222         # this system is Cramer equivalent so there is
 223         # exactly one solution to this system of equations
 224         k, solutions = i-1, {}
 225
 226         while k >= 0:
 227             content = matrix[k, m]
 228
 229             # run back-substitution for variables
 230             for j in xrange(k+1, m):
 231                 content -= matrix[k, j]*solutions[syms[j]]
 232
 233             if simplified:
 234                 solutions[syms[k]] = simplify(content)
 235             else:
 236                 solutions[syms[k]] = content
 237
 238             k -= 1
 239
 240         return solutions
 241     elif len(syms) > matrix.lines:
 242         # this system will have infinite number of solutions
 243         # dependent on exactly len(syms) - i parameters
 244         k, solutions = i-1, {}
 245
 246         while k >= 0:
 247             content = matrix[k, m]
 248
 249             # run back-substitution for variables
 250             for j in xrange(k+1, i):
 251                 content -= matrix[k, j]*solutions[syms[j]]
 252
 253             # run back-substitution for parameters
 254             for j in xrange(i, m):
 255                 content -= matrix[k, j]*syms[j]
 256
 257             if simplified:
 258                 solutions[syms[k]] = simplify(content)
 259             else:
 260                 solutions[syms[k]] = content
 261
 262             k -= 1
 263
 264         return solutions
 265     else:
 266         return None   # no solutions
 267
 268 def solve_undetermined_coeffs(equ, coeffs, sym, **flags):
 269     """Solve equation of a type p(x; a_1, ..., a_k) == q(x) where both
 270        p, q are univariate polynomials and f depends on k parameters.
 271        The result of this functions is a dictionary with symbolic
 272        values of those parameters with respect to coefficiens in q.
 273
 274        This functions accepts both Equations class instances and ordinary
 275        SymPy expressions. Specification of parameters and variable is
 276        obligatory for efficiency and simplicity reason.
 277
 278        >>> from sympy import *
 279        >>> a, b, c, x = symbols('a', 'b', 'c', 'x')
 280
 281        >>> solve_undetermined_coeffs(Eq(2*a*x + a+b, x), [a, b], x)
 282        {a: 1/2, b: -1/2}
 283
 284        >>> solve_undetermined_coeffs(Eq(a*c*x + a+b, x), [a, b], x)
 285        {a: 1/c, b: -1/c}
 286
 287     """
 288     if isinstance(equ, Equality):
 289         # got equation, so move all the
 290         # terms to the left hand side
 291         equ = equ.lhs - equ.rhs
 292
 293     system = collect(equ.expand(), sym, evaluate=False).values()
 294
 295     if not any([ equ.has(sym) for equ in system ]):
 296         # consecutive powers in the input expressions have
 297         # been successfully collected, so solve remaining
 298         # system using Gaussian ellimination algorithm
 299         return solve(system, *coeffs, **flags)
 300     else:
 301         return None # no solutions
 302
 303 def solve_linear_system_LU(matrix, syms):
 304     """ LU function works for invertible only """
 305     assert matrix.lines == matrix.cols-1
 306     A = matrix[:matrix.lines,:matrix.lines]
 307     b = matrix[:,matrix.cols-1:]
 308     soln = A.LUsolve(b)
 309     solutions = {}
 310     for i in range(soln.lines):
 311         solutions[syms[i]] = soln[i,0]
 312     return solutions
 313
 314 def dsolve(eq, funcs):
 315     """
 316     Solves any (supported) kind of differential equation.
 317
 318     Usage
 319     =====
 320         dsolve(f, y(x)) -> Solve a differential equation f for the function y
 321
 322
 323     Details
 324     =======
 325         @param f: ordinary differential equation (either just the left hand
 326             side, or the Equality class)
 327
 328         @param y: indeterminate function of one variable
 329
 330         - you can declare the derivative of an unknown function this way:
 331         >>> from sympy import *
 332         >>> x = Symbol('x') # x is the independent variable
 333
 334         >>> f = Function("f")(x) # f is a function of x
 335         >>> f_ = Derivative(f, x) # f_ will be the derivative of f with respect to x
 336
 337         - This function just parses the equation "eq" and determines the type of
 338         differential equation by its order, then it determines all the coefficients and then
 339         calls the particular solver, which just accepts the coefficients.
 340         - "eq" can be either an Equality, or just the left hand side (in which
 341           case the right hand side is assumed to be 0)
 342         - see test_ode.py for many tests, that serve also as a set of examples
 343           how to use dsolve
 344
 345     Examples
 346     ========
 347         >>> from sympy import *
 348         >>> x = Symbol('x')
 349
 350         >>> f = Function('f')
 351         >>> dsolve(Derivative(f(x),x,x)+9*f(x), f(x))
 352         C1*sin(3*x) + C2*cos(3*x)
 353         >>> dsolve(Eq(Derivative(f(x),x,x)+9*f(x)+1, 1), f(x))
 354         C1*sin(3*x) + C2*cos(3*x)
 355
 356     """
 357
 358     if isinstance(eq, Equality):
 359         if eq.rhs != 0:
 360             return dsolve(eq.lhs-eq.rhs, funcs)
 361         eq = eq.lhs
 362
 363     #currently only solve for one function
 364     if isinstance(funcs, Basic) or len(funcs) == 1:
 365         if isinstance(funcs, (list, tuple)): # normalize args
 366             f = funcs[0]
 367         else:
 368             f = funcs
 369
 370         x = f.args[0]
 371         f = f.func
 372
 373         #We first get the order of the equation, so that we can choose the
 374         #corresponding methods. Currently, only first and second
 375         #order odes can be handled.
 376         order = deriv_degree(eq, f(x))
 377
 378         if  order > 2 :
 379            raise NotImplementedError("dsolve: Cannot solve " + str(eq))
 380         elif order == 2:
 381             return solve_ODE_second_order(eq, f(x))
 382         elif order == 1:
 383             return solve_ODE_first_order(eq, f(x))
 384         else:
 385             raise NotImplementedError("Not a differential equation!")
 386
 387 def deriv_degree(expr, func):
 388     """ get the order of a given ode, the function is implemented
 389     recursively """
 390     a = Wild('a', exclude=[func])
 391
 392     order = 0
 393     if isinstance(expr, Derivative):
 394         order = len(expr.symbols)
 395     else:
 396         for arg in expr.args:
 397             if isinstance(arg, Derivative):
 398                 order = max(order, len(arg.symbols))
 399             elif expr.match(a):
 400                 order = 0
 401             else :
 402                 for arg1 in arg.args:
 403                     order = max(order, deriv_degree(arg1, func))
 404
 405     return order
 406
 407 def solve_ODE_first_order(eq, f):
 408     """
 409     solves many kinds of first order odes, different methods are used
 410     depending on the form of the given equation. Now the linear
 411     case is implemented.
 412     """
 413     from sympy.integrals.integrals import integrate
 414     x = f.args[0]
 415     f = f.func
 416
 417     #linear case: a(x)*f'(x)+b(x)*f(x)+c(x) = 0
 418     a = Wild('a', exclude=[f(x)])
 419     b = Wild('b', exclude=[f(x)])
 420     c = Wild('c', exclude=[f(x)])
 421
 422     r = eq.match(a*diff(f(x),x) + b*f(x) + c)
 423     if r:
 424         t = C.exp(integrate(r[b]/r[a], x))
 425         tt = integrate(t*(-r[c]/r[a]), x)
 426         return (tt + Symbol("C1"))/t
 427
 428     #other cases of first order odes will be implemented here
 429
 430     raise NotImplementedError("solve_ODE_first_order: Cannot solve " + str(eq))
 431
 432 def solve_ODE_second_order(eq, f):
 433     """
 434     solves many kinds of second order odes, different methods are used
 435     depending on the form of the given equation. Now the constanst
 436     coefficients case and a special case are implemented.
 437     """
 438     x = f.args[0]
 439     f = f.func
 440
 441     #constant coefficients case: af''(x)+bf'(x)+cf(x)=0
 442     a = Wild('a', exclude=[x])
 443     b = Wild('b', exclude=[x])
 444     c = Wild('c', exclude=[x])
 445
 446     r = eq.match(a*f(x).diff(x,x) + c*f(x))
 447     if r:
 448         return Symbol("C1")*C.sin(sqrt(r[c]/r[a])*x)+Symbol("C2")*C.cos(sqrt(r[c]/r[a])*x)
 449
 450     r = eq.match(a*f(x).diff(x,x) + b*diff(f(x),x) + c*f(x))
 451     if r:
 452         r1 = solve(r[a]*x**2 + r[b]*x + r[c], x)
 453         if r1[0].is_real:
 454             if len(r1) == 1:
 455                 return (Symbol("C1") + Symbol("C2")*x)*exp(r1[0]*x)
 456             else:
 457                 return Symbol("C1")*exp(r1[0]*x) + Symbol("C2")*exp(r1[1]*x)
 458         else:
 459             r2 = abs((r1[0] - r1[1])/(2*S.ImaginaryUnit))
 460             return (Symbol("C2")*C.cos(r2*x) + Symbol("C1")*C.sin(r2*x))*exp((r1[0] + r1[1])*x/2)
 461
 462     #other cases of the second order odes will be implemented here
 463
 464     #special equations, that we know how to solve
 465     t = x*C.exp(f(x))
 466     tt = a*t.diff(x, x)/t
 467     r = eq.match(tt.expand())
 468     if r:
 469         return -solve_ODE_1(f(x), x)
 470
 471     t = x*C.exp(-f(x))
 472     tt = a*t.diff(x, x)/t
 473     r = eq.match(tt.expand())
 474     if r:
 475         #check, that we've rewritten the equation correctly:
 476         #assert ( r[a]*t.diff(x,2)/t ) == eq.subs(f, t)
 477         return solve_ODE_1(f(x), x)
 478
 479     neq = eq*C.exp(f(x))/C.exp(-f(x))
 480     r = neq.match(tt.expand())
 481     if r:
 482         #check, that we've rewritten the equation correctly:
 483         #assert ( t.diff(x,2)*r[a]/t ).expand() == eq
 484         return solve_ODE_1(f(x), x)
 485
 486     raise NotImplementedError("solve_ODE_second_order: cannot solve " + str(eq))
 487
 488 def solve_ODE_1(f, x):
 489     """ (x*exp(-f(x)))'' = 0 """
 490     C1 = Symbol("C1")
 491     C2 = Symbol("C2")
 492     return -C.log(C1+C2/x)
 493
 494 x = Symbol('x', dummy=True)
 495 a,b,c,d,e,f,g,h = [Wild(t, exclude=[x]) for t in 'abcdefgh']
 496 patterns = None
 497
 498 def _generate_patterns():
 499     """Generates patterns for transcendental equations.
 500
 501     This is lazily calculated (called) in the tsolve() function and stored in
 502     the patterns global variable.
 503     """
 504
 505     tmp1 = f ** (h-(c*g/b))
 506     tmp2 = (-e*tmp1/a)**(1/d)
 507     global patterns
 508     patterns = [
 509         (a*(b*x+c)**d + e   , ((-(e/a))**(1/d)-c)/b),
 510         (    b+c*exp(d*x+e) , (log(-b/c)-e)/d),
 511         (a*x+b+c*exp(d*x+e) , -b/a-LambertW(c*d*exp(e-b*d/a)/a)/d),
 512         (    b+c*f**(d*x+e) , (log(-b/c)-e*log(f))/d/log(f)),
 513         (a*x+b+c*f**(d*x+e) , -b/a-LambertW(c*d*f**(e-b*d/a)*log(f)/a)/d/log(f)),
 514         (    b+c*log(d*x+e) , (exp(-b/c)-e)/d),
 515         (a*x+b+c*log(d*x+e) , -e/d+c/a*LambertW(a/c/d*exp(-b/c+a*e/c/d))),
 516         (a*(b*x+c)**d + e*f**(g*x+h) , -c/b-d*LambertW(-tmp2*g*log(f)/b/d)/g/log(f))
 517     ]
 518
 519 def tsolve(eq, sym):
 520     """
 521     Solves a transcendental equation with respect to the given
 522     symbol. Various equations containing mixed linear terms, powers,
 523     and logarithms, can be solved.
 524
 525     Only a single solution is returned. This solution is generally
 526     not unique. In some cases, a complex solution may be returned
 527     even though a real solution exists.
 528
 529         >>> from sympy import *
 530         >>> x = Symbol('x')
 531
 532         >>> tsolve(3**(2*x+5)-4, x)
 533         (-5*log(3) + log(4))/(2*log(3))
 534
 535         >>> tsolve(log(x) + 2*x, x)
 536         1/2*LambertW(2)
 537
 538     """
 539     if patterns is None:
 540         _generate_patterns()
 541     eq = sympify(eq)
 542     if isinstance(eq, Equality):
 543         eq = eq.lhs - eq.rhs
 544     sym = sympify(sym)
 545     eq2 = eq.subs(sym, x)
 546     # First see if the equation has a linear factor
 547     # In that case, the other factor can contain x in any way (as long as it
 548     # is finite), and we have a direct solution
 549     r = Wild('r')
 550     m = eq2.match((a*x+b)*r)
 551     if m and m[a]:
 552         return (-b/a).subs(m).subs(x, sym)
 553     for p, sol in patterns:
 554         m = eq2.match(p)
 555         if m:
 556             return sol.subs(m).subs(x, sym)
 557
 558     # let's also try to inverse the equation
 559     lhs = eq
 560     rhs = S.Zero
 561
 562     while True:
 563         indep, dep = lhs.as_independent(sym)
 564
 565         # dep + indep == rhs
 566         if lhs.is_Add:
 567             # this indicates we have done it all
 568             if indep is S.Zero:
 569                 break
 570
 571             lhs = dep
 572             rhs-= indep
 573
 574         # dep * indep == rhs
 575         else:
 576             # this indicates we have done it all
 577             if indep is S.One:
 578                 break
 579
 580             lhs = dep
 581             rhs/= indep
 582
 583     #                    -1
 584     # f(x) = g  ->  x = f  (g)
 585     if lhs.is_Function and lhs.nargs==1 and hasattr(lhs, 'inverse'):
 586         rhs = lhs.inverse() (rhs)
 587         lhs = lhs.args[0]
 588
 589         sol = solve(lhs-rhs, sym)
 590         return sol[0]
 591
 592     elif lhs.is_Add:
 593         # just a simple case - we do variable substitution for first function,
 594         # and if it removes all functions - let's call solve.
 595         #      x    -x                   -1
 596         # UC: e  + e   = y      ->  t + t   = y
 597         t = Symbol('t', dummy=True)
 598         terms = lhs.args
 599
 600         # find first term which is Function
 601         for f1 in lhs.args:
 602             if f1.is_Function:
 603                 break
 604         else:
 605             assert False, 'tsolve: at least one Function expected at this point'
 606
 607         # perform the substitution
 608         lhs_ = lhs.subs(f1, t)
 609
 610         # if no Functions left, we can proceed with usual solve
 611         if not (lhs_.is_Function or
 612                 any(term.is_Function for term in lhs_.args)):
 613
 614             # FIXME at present solve cannot solve x + 1/x = y
 615             # FIXME so we do this:
 616             numer, denom = lhs_.as_numer_denom()
 617             sol = solve(numer-rhs*denom, t)
 618            #sol = solve(lhs_-rhs, t)
 619             sol = sol[0]
 620
 621             sol = tsolve(sol-f1, sym)
 622             return sol
 623
 624
 625
 626
 627     raise ValueError("unable to solve the equation")
 628
 629
 630 def msolve(args, f, x0, tol=None, maxsteps=None, verbose=False, norm=None,
 631            modules=['mpmath', 'sympy']):
 632     """
 633     Solves a nonlinear equation system numerically.
 634
 635     f is a vector function of symbolic expressions representing the system.
 636     args are the variables.
 637     x0 is a starting vector close to a solution.
 638
 639     Be careful with x0, not using floats might give unexpected results.
 640
 641     Use modules to specify which modules should be used to evaluate the
 642     function and the Jacobian matrix. Make sure to use a module that supports
 643     matrices. For more information on the syntax, please see the docstring
 644     of lambdify.
 645
 646     Currently only fully determined systems are supported.
 647
 648     >>> from sympy import Symbol, Matrix
 649     >>> x1 = Symbol('x1')
 650     >>> x2 = Symbol('x2')
 651     >>> f1 = 3 * x1**2 - 2 * x2**2 - 1
 652     >>> f2 = x1**2 - 2 * x1 + x2**2 + 2 * x2 - 8
 653     >>> msolve((x1, x2), (f1, f2), (-1., 1.))
 654     [-1.19287309935246]
 655     [ 1.27844411169911]
 656     """
 657     if isinstance(f,  (list,  tuple)):
 658         f = Matrix(f).T
 659     if len(args) != f.cols:
 660         raise NotImplementedError('need exactly as many variables as equations')
 661     if verbose:
 662         print 'f(x):'
 663         print f
 664     # derive Jacobian
 665     J = f.jacobian(args)
 666     if verbose:
 667         print 'J(x):'
 668         print J
 669     # create functions
 670     f = lambdify(args, f.T, modules)
 671     J = lambdify(args, J, modules)
 672     # solve system using Newton's method
 673     kwargs = {}
 674     if tol:
 675         kwargs['tol'] = tol
 676     if maxsteps:
 677         kwargs['maxsteps'] = maxsteps
 678     kwargs['verbose'] = verbose
 679     if norm:
 680         kwargs['norm'] = norm
 681     x = newton(f, x0, J, **kwargs)
 682     return x