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Updated for MK27.1.
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lines changed

‎hands-on-exercises/implied-volatility-q.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-exercises/odes-q.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-exercises/opt-q.ipynb

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"\n",
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"One can see from the HTML documentation [here](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.html) that the relevant algorithmic submodule for (local) optimization is `opt`.\n",
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"\n",
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"Studying the `opt` Functionality Index confirms that a suitable optimization solver to call is `one_var_func`. The HTML documentation for this solver is [here](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.one_var_func.)\n",
19+
"Studying the `opt` Functionality Index confirms that a suitable optimization solver to call is `one_var_func`. The HTML documentation for this solver is [here](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.one_var_func.html.)\n",
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"\n",
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"Write a program to find the minimum of the function $x^2 + 3x - 5$ using `one_var_func`. Assume that the required minimum lies in the range $[-6.0,4.0]$.\n"
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]
@@ -122,7 +122,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-exercises/option-pricing-q.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-exercises/polyfit-q.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-exercises/reg-q.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-exercises/reg_fun.py

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@@ -24,16 +24,16 @@ def gen_multivar_x(m, n, statecomm, dist_id="MN", sigma=2.0, rho=0.5):
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# NAG arguments for multivar_XXX functions
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mode = 2
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r = np.zeros(m*(m+1) + 2)
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comm = {}
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# call random sampling
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if dist_id == "MN":
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sorder = 1
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r, x = rand.multivar_normal(sorder, mode, n, xmu, c, r, statecomm)
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x = rand.multivar_normal(sorder, mode, n, xmu, c, comm, statecomm)
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if dist_id == "T3":
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df = 3
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c_t = (float(df) - 2) / float(df) * c
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r, x = rand.multivar_students_t(mode, n, df, xmu, c_t, r, statecomm)
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x = rand.multivar_students_t(mode, n, df, xmu, c_t, comm, statecomm)
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return x
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‎hands-on-exercises/short-q.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-solutions/implied-volatility-a.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-solutions/odes-a.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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"nbformat": 4,

‎hands-on-solutions/opt-a.ipynb

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"\n",
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"One can see from the HTML documentation [here](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.html) that the relevant algorithmic submodule for (local) optimization is `opt`.\n",
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"\n",
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"Studying the `opt` Functionality Index confirms that a suitable optimization solver to call is `one_var_func`. The HTML documentation for this solver is [here](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.one_var_func.)\n",
19+
"Studying the `opt` Functionality Index confirms that a suitable optimization solver to call is `one_var_func`. The HTML documentation for this solver is [here](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.one_var_func.html.)\n",
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"\n",
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"Write a program to find the minimum of the function $x^2 + 3x - 5$ using `one_var_func`. Assume that the required minimum lies in the range $[-6.0,4.0]$.\n"
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]
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"nag_return_data(e1=1e-08, e2=1e-08, a=-1.5, b=0.0, maxcal=6, x=-1.499999975, f=-7.25)\n"
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"OneVarFuncReturnData(e1=1e-08, e2=1e-08, a=-1.5, b=0.0, maxcal=6, x=-1.499999975, f=-7.25)\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"nag_return_data(bl=array([-2., -1.]), bu=array([1., 1.]), x=array([-1.56951417, -0.0358072 ]), f=-2.0, iw=array([1, 2, 2, 2]), w=array([ 0.00000000e+000, -4.70114194e-009, 6.25037429e+000,\n",
116+
"BoundsQuasiFuncEasyReturnData(bl=array([-2., -1.]), bu=array([1., 1.]), x=array([-1.56951417, -0.0358072 ]), f=-2.0, iw=array([1, 2, 2, 2]), w=array([ 0.00000000e+000, -4.70114194e-009, 6.25037429e+000,\n",
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" 7.78883445e-001, 4.17710202e-236, 4.49150482e+077,\n",
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" -1.56951417e+000, -3.58071805e-002, -1.56951414e+000,\n",
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" -3.58073978e-002, -1.08174744e-003, 6.82317440e-003,\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"nag_return_data(bl=array([-2., -1.]), bu=array([1., 1.]), x=array([-0.40998172, 1. ]), f=-1.5855074116537922, iw=array([ 1, -1, 1, -1]), w=array([ 4.38832894e-008, 0.00000000e+000, 1.00000000e+000,\n",
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"BoundsQuasiFuncEasyReturnData(bl=array([-2., -1.]), bu=array([1., 1.]), x=array([-0.40998172, 1. ]), f=-1.5855074116537922, iw=array([ 1, -1, 1, -1]), w=array([ 4.38832894e-008, 0.00000000e+000, 1.00000000e+000,\n",
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" 7.78883445e-001, 4.17710202e-236, 4.49150482e+077,\n",
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" 1.00000000e+000, 1.00000000e+000, -1.58550741e+000,\n",
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" -1.05114491e+000, -1.00000000e+000, 0.00000000e+000,\n",
@@ -343,9 +343,9 @@
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"naginterfaces.base.opt.nlp1_solve: 6 1 1.0E+00 -1.999915E+00 3.7E-02 2.8E+00\n",
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"naginterfaces.base.opt.nlp1_solve: 7 1 1.0E+00 -2.000000E+00 1.2E-03 2.6E+00\n",
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"naginterfaces.base.opt.nlp1_solve: 8 1 1.0E+00 -2.000000E+00 6.4E-05 2.7E+00\n",
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"naginterfaces.base.opt.nlp1_solve: 9 1 1.0E+00 -2.000000E+00 6.9E-07 2.7E+00\n",
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"naginterfaces.base.opt.nlp1_solve: 9 1 1.0E+00 -2.000000E+00 6.8E-07 2.7E+00\n",
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"naginterfaces.base.opt.nlp1_solve: Mnr itn 1 -- Re-solve QP subproblem.\n",
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"naginterfaces.base.opt.nlp1_solve: 10 2 1.0E+00 -2.000000E+00 7.7E-07 2.7E+00 C\n",
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"naginterfaces.base.opt.nlp1_solve: 10 2 1.0E+00 -2.000000E+00 7.7E-07 2.6E+00 C\n",
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"naginterfaces.base.opt.nlp1_solve:\n",
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"naginterfaces.base.opt.nlp1_solve: Exit from NP problem after 10 major iterations,\n",
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"naginterfaces.base.opt.nlp1_solve: 16 minor iterations.\n",
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"source": [
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"from naginterfaces.library import glopt\n",
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"def g_mcs(x,_nstate):\n",
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" return g(x);\n",
557+
" return g(x)\n",
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"\n",
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"comm = glopt.bnd_mcs_init() # initialize solver\n",
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"start_time = time.time()\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎hands-on-solutions/option-pricing-a.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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"version": "3.8.5"
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}
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"nbformat": 4,

‎hands-on-solutions/polyfit-a.ipynb

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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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"nbformat": 4,

‎hands-on-solutions/reg-a.ipynb

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"nbconvert_exporter": "python",
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"nbformat": 4,

‎hands-on-solutions/reg_fun.py

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@@ -24,16 +24,16 @@ def gen_multivar_x(m, n, statecomm, dist_id="MN", sigma=2.0, rho=0.5):
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# NAG arguments for multivar_XXX functions
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mode = 2
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r = np.zeros(m*(m+1) + 2)
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comm = {}
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# call random sampling
3030
if dist_id == "MN":
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sorder = 1
32-
r, x = rand.multivar_normal(sorder, mode, n, xmu, c, r, statecomm)
32+
x = rand.multivar_normal(sorder, mode, n, xmu, c, comm, statecomm)
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if dist_id == "T3":
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df = 3
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c_t = (float(df) - 2) / float(df) * c
36-
r, x = rand.multivar_students_t(mode, n, df, xmu, c_t, r, statecomm)
36+
x = rand.multivar_students_t(mode, n, df, xmu, c_t, comm, statecomm)
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return x
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‎hands-on-solutions/short-a.ipynb

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"3.141592653589793\n",
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"nag_return_data(din=1.9999999999999998, npts=15, relerr=6.106226635438361e-17)\n"
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"Dim1FinWellReturnData(din=1.9999999999999998, npts=15, relerr=6.106226635438361e-17)\n"
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"name": "python",
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"nbformat": 4,

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