1{
2 "cells": [
3  {
4   "cell_type": "markdown",
5   "id": "7f100c00",
6   "metadata": {},
7   "source": [
8    "# Optimal Control of the Lotka-Volterra equations\n",
9    "\n",
10    "In this tutorial we study an optimal control problem for the [Lotka-Volterra equations](https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations).These famous equations are frequently\n",
11    "used to describe the dynamics of biological systems in which two species interact: a predator ($y$) and a prey ($x$). \n",
12    "\n",
13    "The dynamics considered, taken from the work of [Aitziber Ibanez](https://www.tandfonline.com/doi/full/10.1080/17513758.2016.1226435) is:\n",
14    "\n",
15    "$$\n",
16    "(1) \\quad \\left\\{\n",
17    "\\begin{array}{l}\n",
18    "\\dot x = p_0 x - p_1 xy - p_4 x u\\\\\n",
19    "\\dot y = p_2 xy - p_3 y - p_5 y u\\\\\n",
20    "\\end{array}\n",
21    "\\right.\n",
22    "$$\n",
23    "\n",
24    "where $p_i > 0$ and $0 <= \\vert u\\vert <= 1$ is a control variable describing our hunting capabilities assumed to be proportional to the number of individuals in each of the two species. \n",
25    "\n",
26    "**NOTE**: we used the symbols $p_i$ for all parameters as $p$ it is the default symbol used in `heyoka.py` streams, and thus it allows consistency in notation throughout the notebook.\n",
27    "\n",
28    "We are interested in finding $u(t)$ in the functional space of piecewice continuous functions such that the system is steered into a terminal state $x_f, y_f$ in minimal time when starting from some intial condition $x_0, y_0$.\n",
29    "\n",
30    "More formally, we consider the following optimal control problem (**OCP**):\n",
31    "\n",
32    "$$\n",
33    "\\begin{array}{rl}\n",
34    "\\mbox{find:}  & u(t), t_f \\\\\n",
35    "\\mbox{subject to (dynamics):}   &  \\dot x = p_0 x - p_1 xy - p_4 x u\\\\\n",
36    "                     &  \\dot y = p_2 xy - p_3 y - p_5 y u\\\\\n",
37    "\\mbox{subject to (boundary conditions):}   &  x(0) = x_0, \\quad  x(t_f) = x_f\\\\\n",
38    "    &  y(0) = y_0, \\quad  y(t_f) = y_f\\\\\n",
39    "    & 0 <= \\vert u\\vert <= 1 \\\\\n",
40    "\\mbox{to minimize:}   &  t_f \\\\  \n",
41    "\\end{array}\n",
42    "$$\n",
43    "\n",
44    "To solve the above **OCP** we will apply Pontryagin maximum principle ([**PMP**](https://en.wikipedia.org/wiki/Pontryagin%27s_maximum_principle)) maximizing $-t_f$. \n",
45    "We will thus eventually transform the problem above into a two-point boudary value problem (**TPBVP**) for an augmented set of ordinary differential equation (**ODE**)\n",
46    "which we will solve using `heyoka.py`. \n",
47    "\n",
48    "Let us start importing a few core tools:"
49   ]
50  },
51  {
52   "cell_type": "code",
53   "execution_count": 1,
54   "id": "91a733cf",
55   "metadata": {},
56   "outputs": [],
57   "source": [
58    "import heyoka as hy\n",
59    "import numpy as np\n",
60    "from matplotlib import pyplot as plt\n",
61    "\n",
62    "import sympy.simplify as pretty"
63   ]
64  },
65  {
66   "cell_type": "markdown",
67   "id": "806279e6",
68   "metadata": {},
69   "source": [
70    "## Deriving the augmented dynamics\n",
71    "\n",
72    "The straightforward application of the **PMP** requires the introduction of auxiliary functions $\\boldsymbol\\lambda(t)$ and of a Hamiltonian which, in the case of a minimum time problem is:\n",
73    "\n",
74    "$$\n",
75    "\\mathcal H(\\mathbf x, \\boldsymbol\\lambda, u) = \\mathbf f \\cdot \\boldsymbol\\lambda - 1\n",
76    "$$\n",
77    "where we indicated with the symbol $\\mathbf f$ the r.h.s. of the dynamics.\n",
78    "\n",
79    "We define the system state, the co-states (or augmented states, essentially some form of continuous version of Lagrange multipliers) $\\lambda_x, \\lambda_y$ and the dynamics:"
80   ]
81  },
82  {
83   "cell_type": "code",
84   "execution_count": 2,
85   "id": "d3a7658c",
86   "metadata": {},
87   "outputs": [],
88   "source": [
89    "# Define the symbolic variables x, y and th.\n",
90    "x, y = hy.make_vars(\"x\", \"y\")\n",
91    "\n",
92    "# the co-states lx, ly \n",
93    "lx, ly = hy.make_vars(\"lambda_x\", \"lambda_y\")\n",
94    "\n",
95    "# the control\n",
96    "u, = hy.make_vars(\"u\")\n",
97    "\n",
98    "# and the dynamics fx, fy\n",
99    "fx = hy.par[0]*x - hy.par[1]*x*y - x * hy.par[4] * u\n",
100    "fy = hy.par[2]*x*y - hy.par[3]*y - y * hy.par[5] * u"
101   ]
102  },
103  {
104   "cell_type": "markdown",
105   "id": "142536b6",
106   "metadata": {},
107   "source": [
108    "and the expression for the Hamiltonian:"
109   ]
110  },
111  {
112   "cell_type": "code",
113   "execution_count": 3,
114   "id": "822df704",
115   "metadata": {},
116   "outputs": [
117    {
118     "name": "stdout",
119     "output_type": "stream",
120     "text": [
121      "The Hamiltonian:\n"
122     ]
123    },
124    {
125     "data": {
126      "text/latex": [
127       "$\\displaystyle - \\lambda_{x} x \\left(- p_{0} + p_{1} y + p_{4} u\\right) - \\lambda_{y} y \\left(- p_{2} x + p_{3} + p_{5} u\\right) - 1.0$"
128      ],
129      "text/plain": [
130       "-lambda_x*x*(-p0 + p1*y + p4*u) - lambda_y*y*(-p2*x + p3 + p5*u) - 1.0"
131      ]
132     },
133     "execution_count": 3,
134     "metadata": {},
135     "output_type": "execute_result"
136    }
137   ],
138   "source": [
139    "# The Hamiltonian for the minimal time problem\n",
140    "H = fx*lx + fy*ly - 1\n",
141    "print(\"The Hamiltonian:\")\n",
142    "pretty(H.__repr__())"
143   ]
144  },
145  {
146   "cell_type": "markdown",
147   "id": "3bd3285f",
148   "metadata": {},
149   "source": [
150    "Note how in *heyoka.py* expression system system parameters are indicated with the symbols $p_i$ in the screen output.\n",
151    "\n",
152    "According to the **PMP** the value of the control $u$, along an optimal trajectory, is such that:\n",
153    "\n",
154    "$$\n",
155    "u^* = \\max_{u\\in\\mathcal U} \\mathcal H(\\mathbf x, \\boldsymbol\\lambda, u)\n",
156    "$$\n",
157    "where we have introduced the space of admissible controls as the closed interval $\\mathcal U = [0,1]$. In our case we get, quite simply:\n",
158    "\n",
159    "$$\n",
160    "u^*(\\mathbf x, \\boldsymbol\\lambda) = \n",
161    "\\left\\{\\begin{array}{ll}\n",
162    "1 & -(p_4 x\\lambda_x+p_5 y \\lambda_y) > 0 \\\\\n",
163    "0 & -(p_4 x\\lambda_x+p_5 y \\lambda_y) < 0\n",
164    "\\end{array}\n",
165    "\\right.\n",
166    "$$\n",
167    "\n",
168    "The value of the control will then switch between the extreme values 0 and 1 whenever the switching function $S(\\mathbf x, \\boldsymbol \\lambda) = -(p_4 x\\lambda_x+p_5 y \\lambda_y)$ changes sign. \n",
169    "\n",
170    "We implement two python functions to compute the switching function and the hamiltonian $\\mathcal H(\\mathbf x, \\boldsymbol\\lambda, u^*)$:\n"
171   ]
172  },
173  {
174   "cell_type": "code",
175   "execution_count": 4,
176   "id": "3805429d",
177   "metadata": {},
178   "outputs": [],
179   "source": [
180    "def _switching_function(x,y,lx,ly,p):\n",
181    "    \"\"\"The non vectorized form for the switching function\n",
182    "    \n",
183    "    Args:\n",
184    "        x,y,lx,ly (float): augmented system state.\n",
185    "        p (1-D np.array): system parameters.\n",
186    "\n",
187    "    Returns:\n",
188    "        float: The value of the switching function.\n",
189    "    \"\"\"\n",
190    "    return -(p[4]*x*lx+p[5]*y*ly)\n",
191    "\n",
192    "# Vectorized version\n",
193    "def switching_function(x,y,lx,ly,p):\n",
194    "    vectorized = np.vectorize(lambda x,y,lx,ly: _switching_function(x,y,lx,ly,p))\n",
195    "    return vectorized(x,y,lx,ly)\n",
196    "\n",
197    "def _hamiltonian(x,y,lx,ly,p):\n",
198    "    sw_v = switching_function(x,y,lx,ly,p)\n",
199    "    u_opt = 0\n",
200    "    if sw_v > 0:\n",
201    "        u_opt = 1\n",
202    "    return hy.eval(H, {\"x\":x, \"y\":y, \"lambda_x\":lx, \"lambda_y\":ly, \"u\":u_opt}, p)\n",
203    "\n",
204    "# Vectorized version\n",
205    "def hamiltonian(x,y,lx,ly,p):\n",
206    "    vectorized = np.vectorize(lambda x,y,lx,ly: _hamiltonian(x,y,lx,ly,p))\n",
207    "    return vectorized(x,y,lx,ly)"
208   ]
209  },
210  {
211   "cell_type": "markdown",
212   "id": "12aff30b",
213   "metadata": {},
214   "source": [
215    "The equations of motion in the augmented state ($\\mathbf x, \\boldsymbol \\lambda$) are the Hamilton's equations:\n",
216    "\n",
217    "$$\n",
218    "\\begin{array}{l}\n",
219    "\\dot {\\mathbf x} = \\frac{\\partial \\mathcal H}{\\partial \\boldsymbol \\lambda} \\\\\n",
220    "\\dot {\\boldsymbol \\lambda} = - \\frac{\\partial \\mathcal H}{\\partial \\mathbf x} \n",
221    "\\end{array}\n",
222    "$$\n",
223    "\n",
224    "let us compute them:"
225   ]
226  },
227  {
228   "cell_type": "code",
229   "execution_count": 5,
230   "id": "92233c80",
231   "metadata": {},
232   "outputs": [],
233   "source": [
234    "# We compute the co-state equations\n",
235    "flx = -hy.diff(H, \"x\")\n",
236    "fly = -hy.diff(H, \"y\")"
237   ]
238  },
239  {
240   "cell_type": "code",
241   "execution_count": 6,
242   "id": "fec3f1a4",
243   "metadata": {},
244   "outputs": [
245    {
246     "name": "stdout",
247     "output_type": "stream",
248     "text": [
249      "Co-state dynamics (1):\n"
250     ]
251    },
252    {
253     "data": {
254      "text/latex": [
255       "$\\displaystyle \\lambda_{x} \\left(- p_{0} + p_{1} y + p_{4} u\\right) - \\lambda_{y} p_{2} y$"
256      ],
257      "text/plain": [
258       "lambda_x*(-p0 + p1*y + p4*u) - lambda_y*p2*y"
259      ]
260     },
261     "execution_count": 6,
262     "metadata": {},
263     "output_type": "execute_result"
264    }
265   ],
266   "source": [
267    "print(\"Co-state dynamics (1):\")\n",
268    "pretty((flx).__repr__())"
269   ]
270  },
271  {
272   "cell_type": "code",
273   "execution_count": 7,
274   "id": "59a19e75",
275   "metadata": {},
276   "outputs": [
277    {
278     "name": "stdout",
279     "output_type": "stream",
280     "text": [
281      "Co-state dynamics (2):\n"
282     ]
283    },
284    {
285     "data": {
286      "text/latex": [
287       "$\\displaystyle \\lambda_{x} p_{1} x + \\lambda_{y} \\left(- p_{2} x + p_{3} + p_{5} u\\right)$"
288      ],
289      "text/plain": [
290       "lambda_x*p1*x + lambda_y*(-p2*x + p3 + p5*u)"
291      ]
292     },
293     "execution_count": 7,
294     "metadata": {},
295     "output_type": "execute_result"
296    }
297   ],
298   "source": [
299    "print(\"Co-state dynamics (2):\")\n",
300    "pretty((fly).__repr__())"
301   ]
302  },
303  {
304   "cell_type": "markdown",
305   "id": "b2a90a62",
306   "metadata": {},
307   "source": [
308    "Putting everything together (i.e. substituting the expression for the optimal $u^*$ found via the **PMP** into the augmented equations) we get:\n",
309    "\n",
310    "$$\n",
311    "(2) \\quad \\left\\{\n",
312    "\\begin{array}{l}\n",
313    "\\dot x = p_0 x - p_1 xy - p_4 x u^*\\\\\n",
314    "\\dot y = p_2 xy - p_3 y - p_5 y u^*\\\\\n",
315    "\\frac{d \\lambda_x}{dt} = (-p_0 + p_1  y + p_4u^*)  \\lambda_x - \\lambda_y p_2  y   \\\\\n",
316    "\\frac{d \\lambda_y}{dt}  = p_1  x  \\lambda_x +(-p_2  x + p_3 + p_5 u^*)  \\lambda_y \\\\\n",
317    "\\end{array}\n",
318    "\\right.\n",
319    "$$\n",
320    "\n",
321    "As dictated by Pontryagin's Theorem, a necessary condition for a trajectory to be optimal is that intial values $\\boldsymbol \\lambda_0$ exist such that the solution of the above ODE initial value problem (**IVP**) from $[\\mathbf x_0, \\boldsymbol \\lambda_0]$ leads the system to the desired target state. Furthermore, if the final time $t_f$ is left free $\\mathcal H_f = \\mathcal H(\\mathcal x_f, \\boldsymbol \\lambda_f) = 0$.\n",
322    "\n",
323    "Formally, we seek a root of the system of (three) nonlinear equations in the (three) unknowns $\\boldsymbol \\lambda_0, t_f$:\n",
324    "\n",
325    "$$\n",
326    "(3)\\qquad\n",
327    "\\varphi(\\boldsymbol \\lambda_0, t_f) = \n",
328    "\\left[\n",
329    "\\begin{array}{l}\n",
330    "x(t_f) - x_f \\\\\n",
331    "y(t_f) - y_f \\\\\n",
332    "\\mathcal H (t_f)\n",
333    "\\end{array}\n",
334    "\\right]\n",
335    "$$\n",
336    "also called *shooting function* as finding its root requires solving multiple times an **IVP** in a \"clever\" trial and error iterative scheme.\n",
337    "\n",
338    "In the following we will be using `heyoka.py` to first study the uncontrolled dynamics, then to study the augmented dynamics and, finally, to define a clever, reduced, shooting function that can be easily \n",
339    "solved by a root finder."
340   ]
341  },
342  {
343   "cell_type": "markdown",
344   "id": "6f454805",
345   "metadata": {},
346   "source": [
347    "## Studying the uncontrolled system\n",
348    "Let us see what happens in an uncontrolled case. We will consider an initial population of 10 preys with growth rates $p_0= 1.1$, $p_1 = 0.2$ and 10 predators with growth rates $p_2 = 0.1$, $p_3 = 0.4$. \n",
349    "\n",
350    "We write the r.h.s. of Eq.(1). The parameters in the equations are indicated symbolically by the syntax `par[i]`. Their numerical value will be set later."
351   ]
352  },
353  {
354   "cell_type": "code",
355   "execution_count": 8,
356   "id": "7b286a64",
357   "metadata": {},
358   "outputs": [],
359   "source": [
360    "# Dynamics\n",
361    "fx_unc = hy.par[0]*x - hy.par[1]*x*y \n",
362    "fy_unc = hy.par[2]*x*y - hy.par[3]*y\n",
363    "\n",
364    "# System parameters\n",
365    "ps = [1.1, 0.2, 0.1, 0.4]\n",
366    "\n",
367    "# Initial conditions\n",
368    "x_0, y_0 = 10, 10"
369   ]
370  },
371  {
372   "cell_type": "markdown",
373   "id": "40bfe804",
374   "metadata": {},
375   "source": [
376    "The actual EOM, in the `heyoka.py` syntax, can be specified as a list of tuples (variable, equation):"
377   ]
378  },
379  {
380   "cell_type": "code",
381   "execution_count": 9,
382   "id": "685fde0d",
383   "metadata": {},
384   "outputs": [],
385   "source": [
386    "ode_sys_unc = [(x,fx_unc), (y, fy_unc)]"
387   ]
388  },
389  {
390   "cell_type": "markdown",
391   "id": "0ecc632e",
392   "metadata": {},
393   "source": [
394    "We now create the integrator object, using as initial conditions 10, 10."
395   ]
396  },
397  {
398   "cell_type": "code",
399   "execution_count": 10,
400   "id": "3b70a4b4",
401   "metadata": {},
402   "outputs": [
403    {
404     "name": "stdout",
405     "output_type": "stream",
406     "text": [
407      "Taylor order            : 20\n",
408      "Dimension               : 2\n",
409      "Time                    : 0.0000000000000000\n",
410      "State                   : [10.000000000000000, 10.000000000000000]\n",
411      "Parameters              : [1.1000000000000001, 0.20000000000000001, 0.10000000000000001, 0.40000000000000002]\n",
412      "\n"
413     ]
414    }
415   ],
416   "source": [
417    "ta = hy.taylor_adaptive(ode_sys_unc, state = [x_0,y_0], pars = ps)\n",
418    "print(ta)"
419   ]
420  },
421  {
422   "cell_type": "markdown",
423   "id": "a8cb0867",
424   "metadata": {},
425   "source": [
426    "We may now solve the **IVP**, since we want to record the state and show its trend we use the `propagate_grid` method which efficiently returns the system state along a predefined time grid."
427   ]
428  },
429  {
430   "cell_type": "code",
431   "execution_count": 11,
432   "id": "78206755",
433   "metadata": {},
434   "outputs": [],
435   "source": [
436    "t_grid = np.linspace(0,20,1000)\n",
437    "outcome, min_h, max_h, steps, sol = ta.propagate_grid(t_grid)"
438   ]
439  },
440  {
441   "cell_type": "markdown",
442   "id": "851bd247",
443   "metadata": {},
444   "source": [
445    "... and plot the solution ... (note that we recorded also other quantities ... but we are not really using them here)"
446   ]
447  },
448  {
449   "cell_type": "code",
450   "execution_count": 12,
451   "id": "ace2250c",
452   "metadata": {},
453   "outputs": [
454    {
455     "data": {
456      "image/png": 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\n",
457      "text/plain": [
458       "<Figure size 936x288 with 3 Axes>"
459      ]
460     },
461     "metadata": {
462      "needs_background": "light"
463     },
464     "output_type": "display_data"
465    }
466   ],
467   "source": [
468    "fig, axs = plt.subplots(1,3, figsize=(13,4))\n",
469    "preys_unc = sol[:,0]\n",
470    "predators_unc = sol[:,1]\n",
471    "\n",
472    "axs[0].plot(t_grid, preys_unc, label=\"n. preys\")\n",
473    "axs[0].plot(t_grid, predators_unc, label=\"n. predators\")\n",
474    "axs[0].legend(loc=2)\n",
475    "axs[0].set_xlabel(\"system time\")\n",
476    "axs[0].set_ylabel(\"number in each species\")\n",
477    "\n",
478    "axs[1].plot(preys_unc, predators_unc)\n",
479    "axs[1].set_xlabel(\"n. preys\")\n",
480    "axs[1].set_ylabel(\"n. predators\")\n",
481    "axs[1].set_xlim([0,15])\n",
482    "axs[1].set_ylim([0,14])\n",
483    "\n",
484    "plt.tight_layout() "
485   ]
486  },
487  {
488   "cell_type": "markdown",
489   "id": "e3a042b3",
490   "metadata": {},
491   "source": [
492    "## Controlling the prey-predator dynamics\n",
493    "We now study the augmented system of equations. This will be used later in the shooting method.\n",
494    "\n",
495    "We define our ODE first:"
496   ]
497  },
498  {
499   "cell_type": "code",
500   "execution_count": 13,
501   "id": "6a779347",
502   "metadata": {},
503   "outputs": [],
504   "source": [
505    "# Dynamics\n",
506    "fx = hy.par[0]*x - hy.par[1]*x*y - x * hy.par[4] *  hy.par[6]\n",
507    "fy = hy.par[2]*x*y - hy.par[3]*y - y * hy.par[5] *  hy.par[6]\n",
508    "flx = (-hy.par[0] + hy.par[1]*y + hy.par[6] * hy.par[4]) * lx - hy.par[2]*ly*y\n",
509    "fly = hy.par[1]*x*lx + (-hy.par[2]*x + hy.par[3] + hy.par[6] * hy.par[5]) * ly\n",
510    "ode_sys = [(x,fx), (y, fy), (lx, flx), (ly, fly)]\n",
511    "\n",
512    "# System parameters\n",
513    "p0,p1,p2,p3,p4,p5,p6 = ps = [1.1,0.2, 0.1,0.4,0.03,0.03, 0]\n",
514    "\n",
515    "# Target conditions\n",
516    "x_t, y_t = 10, 4\n",
517    "\n",
518    "# A random choice for the initial costates\n",
519    "lx_0, ly_0  =-1.105, -1.6274561403508774"
520   ]
521  },
522  {
523   "cell_type": "markdown",
524   "id": "e22fff95",
525   "metadata": {},
526   "source": [
527    "We have added three parameters to the system. The first ones are `par[4]` and `par[5]` representing the values of $p_4, p_5$. Then, `par[6]` is the value of the optimal control $u^*$ effectively determining which one of two different systems of ODEs gets propagated. Its value is decided, as seen  above, by the value of the switching function.\n",
528    "\n",
529    "We now build the system of equations taking care to define in `heyoka.py` a terminal event detecting the change in sign of the switching function and thus changing the optimal control accordingly."
530   ]
531  },
532  {
533   "cell_type": "code",
534   "execution_count": 14,
535   "id": "33f8ffce",
536   "metadata": {},
537   "outputs": [
538    {
539     "name": "stdout",
540     "output_type": "stream",
541     "text": [
542      "Taylor order            : 20\n",
543      "Dimension               : 4\n",
544      "Time                    : 0.0000000000000000\n",
545      "State                   : [10.000000000000000, 10.000000000000000, -1.1050000000000000, -1.6274561403508774]\n",
546      "Parameters              : [1.1000000000000001, 0.20000000000000001, 0.10000000000000001, 0.40000000000000002, 0.029999999999999999, 0.029999999999999999, 1.0000000000000000]\n",
547      "N of terminal events    : 1\n",
548      "\n"
549     ]
550    }
551   ],
552   "source": [
553    "# Here we record the switching times\n",
554    "switch_times = []\n",
555    "\n",
556    "# This callback will be called by the Taylor integrator when the switching event is detected \n",
557    "def switch_callback(ta, mr, log_times):\n",
558    "    if not mr:\n",
559    "        if ta.pars[6]==0.:\n",
560    "            ta.pars[6] = 1.\n",
561    "        else:\n",
562    "            ta.pars[6]=0.\n",
563    "        if log_times:\n",
564    "            switch_times.append(ta.time)\n",
565    "    # Do not stop the integration\n",
566    "    return True\n",
567    "        \n",
568    "# The switching event\n",
569    "switching_event = hy.t_event(x * lx * hy.par[4] + y * ly * hy.par[5],\n",
570    "                             callback = lambda ta, mr, d_sgn: switch_callback(ta, mr, True))\n",
571    "\n",
572    "# The Taylor integrator\n",
573    "ta = hy.taylor_adaptive(ode_sys, [x_0, y_0, lx_0, ly_0], pars = ps, t_events = [switching_event])\n",
574    "\n",
575    "# We must set the initial value of the parameter par[6] representing u^* to its optimal value at t=0\n",
576    "ta.pars[6] = np.heaviside(switching_function(x_0,y_0,lx_0,ly_0, ps), 1.)\n",
577    "print(ta)"
578   ]
579  },
580  {
581   "cell_type": "markdown",
582   "id": "2441e940",
583   "metadata": {},
584   "source": [
585    "We now integrate the system. The resulting trajectory will be optimal, but not respecting the requested boundary conditions, since the final time $t_f$ and the initial values of the costates were randomly chosen."
586   ]
587  },
588  {
589   "cell_type": "code",
590   "execution_count": 15,
591   "id": "4745b872",
592   "metadata": {},
593   "outputs": [],
594   "source": [
595    "t_grid = np.linspace(0,30,1000)\n",
596    "outcome, min_h, max_h, steps, sol = ta.propagate_grid(t_grid)"
597   ]
598  },
599  {
600   "cell_type": "markdown",
601   "id": "fb70245d",
602   "metadata": {},
603   "source": [
604    "and replot the result, comparing it to the previous uncontrolled system."
605   ]
606  },
607  {
608   "cell_type": "code",
609   "execution_count": 16,
610   "id": "4b33a582",
611   "metadata": {},
612   "outputs": [
613    {
614     "data": {
615      "image/png": 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\n",
616      "text/plain": [
617       "<Figure size 936x288 with 3 Axes>"
618      ]
619     },
620     "metadata": {
621      "needs_background": "light"
622     },
623     "output_type": "display_data"
624    }
625   ],
626   "source": [
627    "fig, axs = plt.subplots(1,3, figsize=(13,4))\n",
628    "preys = sol[:,0]\n",
629    "predators = sol[:,1]\n",
630    "lpreys = sol[:,2]\n",
631    "lpredators = sol[:,3]\n",
632    "\n",
633    "sf_num = switching_function(preys, predators, lpreys, lpredators, ps)\n",
634    "H_num = hamiltonian(preys, predators, lpreys, lpredators, ps)\n",
635    "mask_on = sf_num > 0\n",
636    "mask_off = sf_num < 0\n",
637    "\n",
638    "axs[0].plot(t_grid, preys, label=\"n. preys\")\n",
639    "axs[0].plot(t_grid, predators, label=\"n. predators\")\n",
640    "axs[0].legend(loc=2)\n",
641    "axs[0].set_xlabel(\"system time\")\n",
642    "axs[0].set_ylabel(\"number in each species\")\n",
643    "\n",
644    "axs[1].plot(preys_unc, predators_unc, label=\"uncontrolled\")\n",
645    "axs[1].plot(preys, predators, 'r--', label = \"on phase\")\n",
646    "axs[1].plot(preys[mask_off], predators[mask_off], 'k.', label = \"off phase\")\n",
647    "axs[1].set_xlabel(\"n. preys\")\n",
648    "axs[1].set_ylabel(\"n. predators\")\n",
649    "axs[1].set_xlim([0,15])\n",
650    "axs[1].set_ylim([0,14])\n",
651    "axs[1].scatter([x_t], [y_t], label='target')\n",
652    "axs[1].legend(loc=1)\n",
653    "\n",
654    "axs[2].plot(t_grid, sf_num, label=\"switching function\")\n",
655    "axs[2].plot(t_grid, H_num, label=\"hamiltonian\")\n",
656    "for time in switch_times:\n",
657    "    axs[2].axvline(x=time, color='k', linestyle='--')\n",
658    "axs[2].set_xlabel(\"system time\")\n",
659    "axs[2].legend(loc=2)\n",
660    "\n",
661    "plt.tight_layout() "
662   ]
663  },
664  {
665   "cell_type": "markdown",
666   "id": "77cd54d2",
667   "metadata": {},
668   "source": [
669    "## Implementing a single shooting method\n",
670    "Above we have produced an optimal trajectory, only not the one we looked for since we want to achieve the terminal conditions $x_f = 10, y_f=4$ and (free time) $H_f=0$. \n",
671    "The choice we made for $\\lambda_{x0}$, $\\lambda_{y0}$, $t_f$ was not a good one, we need to change it up to when Eq.(3) is actually satisfied. \n",
672    "\n",
673    "In this case, to showcase the possibilities offered by *heyoka.py*, we will vary $\\lambda_x$, find $\\lambda_y$ from the condition $\\mathcal H_f = \\mathcal H_0 = 0$ and find $t_f$ as the time along the generated trajectory when the difference between the state and the target is minimal (a terminal event).\n",
674    "\n",
675    "This reduces the free *shooting parameters* in Eq.(3) from three to one only: $\\lambda_x$. \n",
676    "\n",
677    "The following holds (from $\\mathcal H_f = \\mathcal H_0=0$):\n",
678    "$$\n",
679    "\\lambda_{y0} = \\frac{x\\lambda_{x0}(p_0-p_1 y_0-p_4 u^*_0-1)}{y_0(-p_2x_0+p_3+p_5 u^*_0)}\n",
680    "$$\n",
681    "and it is implemented in the function below:"
682   ]
683  },
684  {
685   "cell_type": "code",
686   "execution_count": 17,
687   "id": "0aba9a7e",
688   "metadata": {},
689   "outputs": [],
690   "source": [
691    "def find_lambda_y0(x_0,y_0,lx_0,p):\n",
692    "    #we assume u*=0\n",
693    "    ly_0 = (x_0*lx_0*(p[0]-p[1]*y_0)-1)/y_0/(-p[2]*x_0+p[3])\n",
694    "    if _switching_function(x_0,y_0,lx_0,ly_0,p) < 0:\n",
695    "        #the assumption holds\n",
696    "        return ly_0\n",
697    "    ly_0 = (x_0*lx_0*(p[0]-p[1]*y_0-p[4])-1)/y_0/(-p[2]*x_0+p[3]+p[5])\n",
698    "    if _switching_function(x_0,y_0,lx_0,ly_0,p) > 0:\n",
699    "        #the assumption holds\n",
700    "        return ly_0\n",
701    "    raise ValueError"
702   ]
703  },
704  {
705   "cell_type": "markdown",
706   "id": "1472ff4d",
707   "metadata": {},
708   "source": [
709    "We also need to introduce a distance event triggering when the derivative of the distance is zero, hence at the maximum and minimum distance points."
710   ]
711  },
712  {
713   "cell_type": "code",
714   "execution_count": 18,
715   "id": "fc7f28df",
716   "metadata": {},
717   "outputs": [],
718   "source": [
719    "def distance_callback(ta, mr, d_sgn):\n",
720    "    candidates_t.append(ta.time)\n",
721    "    candidates_d.append((ta.state[0]-x_t)**2 + (ta.state[1]-y_t)**2)\n",
722    "    # Do not stop the integration\n",
723    "    return True\n",
724    "\n",
725    "# This is the new \"distance\" event\n",
726    "distance_event = hy.t_event((x- x_t)*fx + (y-y_t)*fy, callback = distance_callback)\n",
727    "\n",
728    "# We also redefine the switching event as we have no need to log the switching times (this is optional and has not real impact in this case)\n",
729    "switching_event_no_log = hy.t_event(x * lx * hy.par[4] + y * ly * hy.par[5],\n",
730    "                                    callback = lambda ta, mr, d_sgn: switch_callback(ta, mr, False))"
731   ]
732  },
733  {
734   "cell_type": "markdown",
735   "id": "ba36bcdb",
736   "metadata": {},
737   "source": [
738    "Let us check that all is in order performing again the same integration and visualizing the new event triggers ...."
739   ]
740  },
741  {
742   "cell_type": "code",
743   "execution_count": 19,
744   "id": "ee9747c6",
745   "metadata": {},
746   "outputs": [
747    {
748     "name": "stdout",
749     "output_type": "stream",
750     "text": [
751      "Taylor order            : 20\n",
752      "Dimension               : 4\n",
753      "Time                    : 0.0000000000000000\n",
754      "State                   : [10.000000000000000, 10.000000000000000, -1.1050000000000000, -1.6274561403508774]\n",
755      "Parameters              : [1.1000000000000001, 0.20000000000000001, 0.10000000000000001, 0.40000000000000002, 0.029999999999999999, 0.029999999999999999, 1.0000000000000000]\n",
756      "N of terminal events    : 2\n",
757      "\n"
758     ]
759    }
760   ],
761   "source": [
762    "ta = hy.taylor_adaptive(ode_sys, [x_0, y_0, lx_0, ly_0], pars=ps, t_events = [switching_event_no_log, distance_event])\n",
763    "# we must set the initial value of the parameter $u^*$ to its optimal value at t=0\n",
764    "ta.pars[6] = np.heaviside(switching_function(x_0,y_0,lx_0,ly_0, ps), 1.)\n",
765    "print(ta)"
766   ]
767  },
768  {
769   "cell_type": "code",
770   "execution_count": 20,
771   "id": "f8eea372",
772   "metadata": {},
773   "outputs": [],
774   "source": [
775    "t_grid = np.linspace(0,35,1000)\n",
776    "# Here we record the detected max/min distances and times\n",
777    "candidates_d = []\n",
778    "candidates_t = []\n",
779    "# We propagate\n",
780    "outcome, min_h, max_h, steps, sol = ta.propagate_grid(t_grid)"
781   ]
782  },
783  {
784   "cell_type": "markdown",
785   "id": "c3999a23",
786   "metadata": {},
787   "source": [
788    "and we plot the results highlighting with different colors parts of the trajectory that are within successive distance events: "
789   ]
790  },
791  {
792   "cell_type": "code",
793   "execution_count": 21,
794   "id": "a852183f",
795   "metadata": {},
796   "outputs": [
797    {
798     "data": {
799      "image/png": 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\n",
800      "text/plain": [
801       "<Figure size 432x432 with 1 Axes>"
802      ]
803     },
804     "metadata": {
805      "needs_background": "light"
806     },
807     "output_type": "display_data"
808    }
809   ],
810   "source": [
811    "fig, axs = plt.subplots(1,1, figsize=(6,6))\n",
812    "preys = sol[:,0]\n",
813    "predators = sol[:,1]\n",
814    "lpreys = sol[:,2]\n",
815    "lpredators = sol[:,3]\n",
816    "\n",
817    "sf_num = switching_function(preys, predators, lpreys, lpredators, ps)\n",
818    "H_num = hamiltonian(preys, predators, lpreys, lpredators, ps)\n",
819    "axs.plot(preys_unc, predators_unc)\n",
820    "\n",
821    "candidates_t.insert(0,0)\n",
822    "candidates_d.insert(0,np.inf)\n",
823    "\n",
824    "for i in range(len(candidates_t)-1):\n",
825    "    mask_on = (t_grid >= candidates_t[i])&(t_grid <= candidates_t[i+1])\n",
826    "    axs.plot(preys[mask_on], predators[mask_on], '-')\n",
827    "axs.scatter([x_t], [y_t])\n",
828    "axs.set_xlabel(\"n. preys\")\n",
829    "axs.set_ylabel(\"n. predators\")\n",
830    "axs.set_xlim([0,14])\n",
831    "axs.set_ylim([0,14])\n",
832    "plt.title(\"Distance events trigger at minimal and maximal distance from the target point\")\n",
833    "\n",
834    "\n",
835    "plt.tight_layout() "
836   ]
837  },
838  {
839   "cell_type": "markdown",
840   "id": "e4c23ead",
841   "metadata": {},
842   "source": [
843    "We are now ready to put everything together into a reduced shooting function that will be zero corresponding to the time optimal trajectory."
844   ]
845  },
846  {
847   "cell_type": "code",
848   "execution_count": 22,
849   "id": "a618834a",
850   "metadata": {},
851   "outputs": [],
852   "source": [
853    "def reduced_shooting_function(x_0, y_0, lx_0, ps):\n",
854    "    global candidates_t\n",
855    "    global candidates_d\n",
856    "    global ta\n",
857    "\n",
858    "    # We reset the time\n",
859    "    ta.time = 0\n",
860    "    # We compute the initiqal value for the y co-state (form H=0)\n",
861    "    ly_0 = find_lambda_y0(x_0, y_0, lx_0, ps)\n",
862    "    # We compute the initial value of the optimal control\n",
863    "    ta.pars[6] = np.heaviside(switching_function(x_0,y_0,lx_0,ly_0, ps), 1.)\n",
864    "\n",
865    "    # We set the initial conditions\n",
866    "    ta.state[0] = x_0; ta.state[1] = y_0; ta.state[2] = lx_0; ta.state[3] = ly_0\n",
867    "    # We reset the candidates final times and distances\n",
868    "    candidates_t = []\n",
869    "    candidates_d = []\n",
870    "    # define the time grid\n",
871    "    t_grid = np.linspace(0,35,1000)\n",
872    "    # Perform the integration\n",
873    "    outcome, min_h, max_h, steps, sol = ta.propagate_grid(t_grid)\n",
874    "    # We return the minimum distance\n",
875    "    return np.min(candidates_d)\n",
876    "    "
877   ]
878  },
879  {
880   "cell_type": "markdown",
881   "id": "1cfdb457",
882   "metadata": {},
883   "source": [
884    "we solve the reduced shooting function. Note that the initial guess is fundamental. In this case, since we reduced the problem to a one only dimension, its trivial to find a good one."
885   ]
886  },
887  {
888   "cell_type": "code",
889   "execution_count": 23,
890   "id": "2891ef0c",
891   "metadata": {},
892   "outputs": [
893    {
894     "data": {
895      "text/plain": [
896       "      fun: 1.9031415041423592e-15\n",
897       " hess_inv: array([[0.01440261]])\n",
898       "      jac: array([3.25029357e-09])\n",
899       "  message: 'Optimization terminated successfully.'\n",
900       "     nfev: 14\n",
901       "      nit: 3\n",
902       "     njev: 7\n",
903       "   status: 0\n",
904       "  success: True\n",
905       "        x: array([-0.47906457])"
906      ]
907     },
908     "execution_count": 23,
909     "metadata": {},
910     "output_type": "execute_result"
911    }
912   ],
913   "source": [
914    "from scipy.optimize import minimize\n",
915    "res = minimize(lambda lx_0: reduced_shooting_function(x_0, y_0, lx_0, ps), -0.47, tol=1e-8)\n",
916    "res"
917   ]
918  },
919  {
920   "cell_type": "markdown",
921   "id": "8bfb6723",
922   "metadata": {},
923   "source": [
924    "And compute/plot the resulting optimal trajectory"
925   ]
926  },
927  {
928   "cell_type": "code",
929   "execution_count": 24,
930   "id": "9da44521",
931   "metadata": {},
932   "outputs": [],
933   "source": [
934    "idx = np.argmin(candidates_d)\n",
935    "tf = candidates_t[idx]\n",
936    "lx_0 = res.x\n",
937    "# We reset the time\n",
938    "ta.time = 0\n",
939    "# We compute the inititial value for the y co-state (form H=0)\n",
940    "ly_0 = find_lambda_y0(x_0, y_0, lx_0, ps)\n",
941    "\n",
942    "switch_times = []\n",
943    "ta = hy.taylor_adaptive(ode_sys, [x_0, y_0,lx_0, ly_0], pars=ps, t_events = [switching_event])\n",
944    "# We compute the initial value of the optimal control\n",
945    "ta.pars[6] = np.heaviside(switching_function(x_0,y_0,lx_0,ly_0, ps), 1.)\n",
946    "\n",
947    "# define the time grid\n",
948    "t_grid = np.linspace(0,tf,1000)\n",
949    "# Perform the integration\n",
950    "outcome, min_h, max_h, steps, sol = ta.propagate_grid(t_grid)"
951   ]
952  },
953  {
954   "cell_type": "code",
955   "execution_count": 25,
956   "id": "22552dd9",
957   "metadata": {},
958   "outputs": [
959    {
960     "data": {
961      "image/png": 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\n",
962      "text/plain": [
963       "<Figure size 936x288 with 3 Axes>"
964      ]
965     },
966     "metadata": {
967      "needs_background": "light"
968     },
969     "output_type": "display_data"
970    }
971   ],
972   "source": [
973    "fig, axs = plt.subplots(1,3, figsize=(13,4))\n",
974    "preys = sol[:,0]\n",
975    "predators = sol[:,1]\n",
976    "lpreys = sol[:,2]\n",
977    "lpredators = sol[:,3]\n",
978    "\n",
979    "sf_num = switching_function(preys, predators, lpreys, lpredators, ps)\n",
980    "H_num = hamiltonian(preys, predators, lpreys, lpredators, ps)\n",
981    "mask_on = sf_num > 0\n",
982    "mask_off = sf_num < 0\n",
983    "\n",
984    "axs[0].plot(t_grid, preys, label=\"n. preys\")\n",
985    "axs[0].plot(t_grid, predators, label=\"n. predators\")\n",
986    "axs[0].legend(loc=2)\n",
987    "axs[0].set_xlabel(\"system time\")\n",
988    "axs[0].set_ylabel(\"number in each species\")\n",
989    "\n",
990    "axs[1].plot(preys_unc, predators_unc, label=\"uncontrolled\")\n",
991    "axs[1].plot(preys, predators, 'r--', label = \"on phase\")\n",
992    "axs[1].plot(preys[mask_off], predators[mask_off], 'k.', label = \"off phase\")\n",
993    "axs[1].scatter([x_t], [y_t], label='target')\n",
994    "axs[1].set_xlabel(\"n. preys\")\n",
995    "axs[1].set_ylabel(\"n. predators\")\n",
996    "axs[1].set_xlim([0,15])\n",
997    "axs[1].set_ylim([0,14])\n",
998    "axs[1].legend(loc=1)\n",
999    "\n",
1000    "\n",
1001    "\n",
1002    "axs[2].plot(t_grid, sf_num, label=\"switching function\")\n",
1003    "axs[2].plot(t_grid, H_num, label=\"hamiltonian\")\n",
1004    "for time in switch_times:\n",
1005    "    axs[2].axvline(x=time, color='k', linestyle='--')\n",
1006    "axs[2].set_xlabel(\"system time\")\n",
1007    "axs[2].legend(loc=2)\n",
1008    "\n",
1009    "plt.tight_layout()\n"
1010   ]
1011  },
1012  {
1013   "cell_type": "markdown",
1014   "id": "e0816456",
1015   "metadata": {},
1016   "source": [
1017    "The time optimal trajectory is thus computed in the blink of an eye, showing us how to control the species dynamics in this example."
1018   ]
1019  }
1020 ],
1021 "metadata": {
1022  "kernelspec": {
1023   "display_name": "Python 3",
1024   "language": "python",
1025   "name": "python3"
1026  },
1027  "language_info": {
1028   "codemirror_mode": {
1029    "name": "ipython",
1030    "version": 3
1031   },
1032   "file_extension": ".py",
1033   "mimetype": "text/x-python",
1034   "name": "python",
1035   "nbconvert_exporter": "python",
1036   "pygments_lexer": "ipython3",
1037   "version": "3.8.6"
1038  }
1039 },
1040 "nbformat": 4,
1041 "nbformat_minor": 5
1042}
1043