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Parker–Sochacki method

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higher order permits a longer time step without loss of accuracy, which improves efficiency.) The order and step size can be easily changed from one step to the next. It is possible to calculate a guaranteed error bound on the solution. Arbitrary precision floating point libraries allow this method to compute arbitrarily accurate solutions.
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error bound for a single step with the Parker–Sochacki method. This allows a Parker–Sochacki program to calculate the step size that guarantees that the error is below any non-zero given tolerance. Using this calculated step size with an error tolerance of less than half of the machine epsilon yields
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With the Parker–Sochacki method, information between integration steps is developed at high order. As the Parker–Sochacki method integrates, the program can be designed to save the power series coefficients that provide a smooth solution between points in time. The coefficients can be saved and used
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The method requires only addition, subtraction, and multiplication, making it very convenient for high-speed computation. (The only divisions are inverses of small integers, which can be precomputed.) Use of a high order—calculating many coefficients of the power series—is convenient. (Typically a
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The end result is a high order piecewise solution to the original ODE problem. The order of the solution desired is an adjustable variable in the program that can change between steps. The order of the solution is only limited by the floating point representation on the machine running the program.
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Most methods for numerically solving ODEs require only the evaluation of derivatives for chosen values of the variables, so systems like MATLAB include implementations of several methods all sharing the same calling sequence. Users can try different methods by simply changing the name of the
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so that polynomial evaluation provides the high order solution between steps. With most other classical integration methods, one would have to resort to interpolation to get information between integration steps, leading to an increase of error.
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If the ODEs do not have the required form, it is nearly always possible to find an expanded set of equations that do have the required form, such that a subset of the solution is a solution of the original
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And in some cases can be either extended by using arbitrary precision floating point numbers, or for special cases by finding solution with only integer or rational coefficients.
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Several coefficients of the power series are calculated in turn, a time step is chosen, the series is evaluated at that time, and the process repeats.
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function called. The Parker–Sochacki method requires more work to put the equations into the proper form, and cannot use the same calling sequence.
36: 40: 132:"Explicit a-priori error bounds and adaptive error control for approximation of nonlinear initial value differential systems" 224:. A demonstration of the theory and usage of the Parker–Sochacki method, including a solution for the classical Newtonian 258: 51:
solutions to systems of differential equations, with the coefficients in either algebraic or numerical form.
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Joseph W. Rudmin (1998), "Application of the Parker–Sochacki Method to Celestial Mechanics",
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P.G. Warne; D.P. Warne; J.S. Sochacki; G.E. Parker; D.C Carothers (2006).
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The Parker–Sochacki method rests on two simple observations:
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Algorithm for solving ordinary differential equations
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can be used to find their solution in the form of a
228:-body problem with mutual gravitational attraction. 179:Polynomial ODEs – Examples, Solutions, Properties 63:If a set of ODEs has a particular form, then the 250: 196: 245:. A collection of papers and some Matlab code. 139:Computers & Mathematics with Applications 47:Mathematics Department. The method produces 210: 150: 251: 199:Journal of Computational Neuroscience 13: 14: 270: 170: 107: 31:for solving systems of ordinary 123: 1: 116: 86: 7: 234:The Modified Picard Method. 152:10.1016/j.camwa.2005.12.004 10: 275: 104:a symplectic integration. 54: 221:10.1007/s10827-008-0131-5 45:James Madison University 33:differential equations 25:Parker–Sochacki method 259:Mathematical analysis 35:(ODEs), developed by 145:(12): 1695–1710. 266: 244: 243: 241: 223: 214: 192: 191: 189: 184: 164: 163: 161: 159: 154: 136: 127: 49:Maclaurin series 274: 273: 269: 268: 267: 265: 264: 263: 249: 248: 239: 237: 231: 187: 185: 182: 176: 173: 168: 167: 157: 155: 134: 128: 124: 119: 110: 89: 57: 37:G. Edgar Parker 17: 12: 11: 5: 272: 262: 261: 247: 246: 229: 194: 172: 171:External links 169: 166: 165: 121: 120: 118: 115: 109: 106: 88: 85: 77: 76: 72: 56: 53: 41:James Sochacki 15: 9: 6: 4: 3: 2: 271: 260: 257: 256: 254: 236: 235: 230: 227: 222: 218: 213: 208: 204: 200: 195: 181: 180: 175: 174: 153: 148: 144: 140: 133: 126: 122: 114: 108:Disadvantages 105: 102: 97: 93: 84: 80: 73: 70: 66: 65:Picard method 62: 61: 60: 52: 50: 46: 42: 38: 34: 30: 26: 22: 240:November 11, 238:, retrieved 233: 225: 202: 198: 186:, retrieved 178: 156:. Retrieved 142: 138: 125: 111: 100: 99:There is an 98: 94: 90: 81: 78: 69:power series 58: 24: 18: 205:: 115–133, 21:mathematics 188:August 27, 158:August 27, 117:References 87:Advantages 212:1007.1677 43:, of the 29:algorithm 253:Category 101:a priori 55:Summary 27:is an 23:, the 207:arXiv 183:(PDF) 135:(PDF) 75:ODEs. 242:2013 190:2017 160:2017 39:and 217:doi 147:doi 19:In 255:: 215:, 203:27 201:, 143:52 141:. 137:. 226:N 219:: 209:: 162:. 149:: 71:.

Index

mathematics
algorithm
differential equations
G. Edgar Parker
James Sochacki
James Madison University
Maclaurin series
Picard method
power series
"Explicit a-priori error bounds and adaptive error control for approximation of nonlinear initial value differential systems"
doi
10.1016/j.camwa.2005.12.004
Polynomial ODEs – Examples, Solutions, Properties
arXiv
1007.1677
doi
10.1007/s10827-008-0131-5
The Modified Picard Method.
Category
Mathematical analysis

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