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now arnoldi works. last problem on algo 4. main to update

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Luca Lombardo 2 years ago
parent 633655bc27
commit 4b8c39ad84
6 changed files with 251 additions and 232 deletions
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6
data/results/algo1/different_tau.csv
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alpha,products m-v,tau,time
"[0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]",19,1e-05,14.4
"[0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]",56,1e-06,33.8
"[0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]",113,1e-07,84.19
"[0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]",275,1e-08,136.66
"[0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]",454,1e-09,193.94
1 alpha products m-v tau time
2 [0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99] 19 1e-05 14.4
3 [0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99] 56 1e-06 33.8
4 [0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99] 113 1e-07 84.19
5 [0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99] 275 1e-08 136.66
6 [0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99] 454 1e-09 193.94

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src/algo2_testing.ipynb vendored
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"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -29,37 +29,95 @@
},
{
"cell_type": "code",
"execution_count": 86,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def Arnoldi(A, v, m): \n",
"# def Arnoldi(A, v, m): \n",
"# beta = norm(v)\n",
"# v = v/beta # dimension of v is n x 1\n",
"# H = sp.sparse.lil_matrix((m,m)) # dimension of H is m x m \n",
"# V = sp.sparse.lil_matrix((A.shape[0],m))\n",
"# V[:,0] = v # each column of V is a vector v\n",
"\n",
"# for j in range(m):\n",
"# print(\"j = \", j)\n",
"# w = A @ v \n",
"# for i in range(j):\n",
"# tmp = v.T @ w \n",
"# H[i,j] = tmp[0,0]\n",
"# w = w - H[i,j]*v \n",
" \n",
"# H[j,j-1] = norm(w) \n",
"\n",
"# if H[j,j-1] == 0: \n",
"# print(\"Arnoldi breakdown\")\n",
"# m = j\n",
"# v = 0\n",
"# break\n",
"# else:\n",
"# if j < m-1:\n",
"# v = w/H[j,j-1]\n",
"# # for i in range(A.shape[0]):\n",
"# # V[i,j+1] = v[i,0]\n",
"# V[:,j+1] = v\n",
"\n",
"# print(j, \" iterations completed\")\n",
"# print(\"V = \", V.shape)\n",
"# print(\"H = \", H.shape)\n",
"# print(\"v = \", v.shape)\n",
"# print(\"beta = \", beta)\n",
"\n",
"# return V, H, v, beta, j"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# review the restarted GMRES method (hereafter referred to as GMRES in short), which is a nonsymmetric Krylov subspace solver based on the Arnoldi decomposition procedure\n",
"\n",
"# return the matrix V, the matrix Hp, the vector v, the scalar beta and the number of iterations j. Where V is a matrix of dimension n x m, Hp is an upper Hessenberg matrix of dimension (m+1) x m. Return also the m+1 vector of the basis V. Return h as the m+1,m element of Hp and the vector v as the m+1 element of the basis V.\n",
"\n",
"\n",
"def ArnoldiGMRES(A,v0,m):\n",
" v = v0\n",
" beta = norm(v)\n",
" v = v/beta\n",
" h = sp.sparse.lil_matrix((m,m)) \n",
" H = sp.sparse.lil_matrix((m+1,m)) \n",
" V = sp.sparse.lil_matrix((A.shape[0],m+1))\n",
" V[:,0] = v # each column of V is a vector v\n",
"\n",
" for j in range(m):\n",
" # print(\"j = \", j)\n",
" w = A @ v \n",
" for i in range(j):\n",
" tmp = v.T @ w \n",
" h[i,j] = tmp[0,0]\n",
" w = w - h[i,j]*v \n",
" h[j,j-1] = norm(w) \n",
" tmp = v.T @ w # tmp is a 1x1 matrix, so it's O(1) in memory\n",
" H[i,j] = tmp[0,0] \n",
" w = w - H[i,j]*v \n",
" \n",
" H[j+1,j] = norm(w)\n",
"\n",
" if h[j,j-1] == 0: \n",
" if H[j+1,j] == 0:\n",
" print(\"Arnoldi breakdown\")\n",
" m = j\n",
" v = 0\n",
" break\n",
" else:\n",
" v = w/h[j,j-1] \n",
" if j < m-1:\n",
" v = w/H[j+1,j]\n",
" V[:,j+1] = v\n",
"\n",
" print(j, \" iterations completed\")\n",
" print(\"V = \", V.shape)\n",
" print(\"H = \", H.shape)\n",
" print(\"v = \", v.shape)\n",
" print(\"beta = \", beta)\n",
"\n",
" print(\"Arnoldi finished\\n\")\n",
" print(\"h is \", h.shape)\n",
" print(\"v is \", v.shape)\n",
" print(\"m is \", m)\n",
" print(\"beta is \", beta)\n",
" return v, h, m, beta, j # this is not the output required in the paper, I should return the matrix V and the matrix H\n"
" return V, H, v, beta, j \n",
" "
]
},
{
@ -71,35 +129,52 @@
},
{
"cell_type": "code",
"execution_count": 82,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"m = 100\n",
"A = sp.sparse.rand(m,m, density=0.5, format='lil')\n",
"v = sp.sparse.rand(m,1, density=0.5, format='lil')"
"n = 110\n",
"A = sp.sparse.rand(n,n, density=0.1, format='lil')\n",
"v = sp.sparse.rand(n,1, density=0.1, format='lil')"
]
},
{
"cell_type": "code",
"execution_count": 87,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Arnoldi finished\n",
"\n",
"h is (100, 100)\n",
"v is (100, 1)\n",
"m is 100\n",
"beta is 1.0\n"
"99 iterations completed\n",
"V = (110, 101)\n",
"H = (101, 100)\n",
"v = (110, 1)\n",
"beta = 2.1103247239370497\n"
]
},
{
"data": {
"text/plain": [
"(<110x101 sparse matrix of type '<class 'numpy.float64'>'\n",
" \twith 10863 stored elements in List of Lists format>,\n",
" <101x100 sparse matrix of type '<class 'numpy.float64'>'\n",
" \twith 4923 stored elements in List of Lists format>,\n",
" <110x1 sparse matrix of type '<class 'numpy.float64'>'\n",
" \twith 110 stored elements in Compressed Sparse Row format>,\n",
" 2.1103247239370497,\n",
" 99)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v, h, m, beta, j = Arnoldi(A, v, m)"
"ArnoldiGMRES(A,v,m)"
]
}
],

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src/algo4_testing.ipynb vendored
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@ -29,41 +29,45 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def Arnoldi(A, v, m): # defined ad algorithm 2 in the paper\n",
"def Arnoldi(A,v0,m):\n",
" v = v0\n",
" beta = norm(v)\n",
" print(\"A\")\n",
" v = v/beta\n",
" print(\"B\")\n",
" h = sp.sparse.lil_matrix((m,m))\n",
" print(\"C\")\n",
" H = sp.sparse.lil_matrix((m+1,m)) \n",
" V = sp.sparse.lil_matrix((A.shape[0],m+1))\n",
" V[:,0] = v # each column of V is a vector v\n",
"\n",
" for j in range(m):\n",
" w = A.dot(v)\n",
" print(\"D\")\n",
" # print(\"j = \", j)\n",
" w = A @ v \n",
" for i in range(j):\n",
" h[i,j] = v.T.dot(w)\n",
" print(\"E\")\n",
" w = w - h[i,j]*v[i]\n",
" print(\"F\")\n",
" tmp = v.T @ w # tmp is a 1x1 matrix, so it's O(1) in memory\n",
" H[i,j] = tmp[0,0] \n",
" w = w - H[i,j]*v \n",
" \n",
" h[j+1,j] = norm(w)\n",
" print(\"G\")\n",
" H[j+1,j] = norm(w)\n",
"\n",
" if h[j+1,j] == 0:\n",
" print(\"The algorithm didn't converge\")\n",
" if H[j+1,j] == 0:\n",
" # print(\"Arnoldi breakdown\")\n",
" m = j\n",
" v[m+1] = 0\n",
" v = 0\n",
" break\n",
" else:\n",
" print(\"H\")\n",
" v[j+1] = w**h[j+1,j] # THIS IS WRONG, I DON'T KNOW HOW TO FIX IT. ERROR \" matrix is not square\"\n",
" print(\"I\")\n",
" if j < m-1:\n",
" v = w/H[j+1,j]\n",
" V[:,j+1] = v\n",
"\n",
" # print(j, \" iterations completed\")\n",
" # print(\"V = \", V.shape)\n",
" # print(\"H = \", H.shape)\n",
" # print(\"v = \", v.shape)\n",
" # print(\"beta = \", beta)\n",
"\n",
" return v, h, m, beta, j"
" return V, H, v, beta, j "
]
},
{
@ -77,7 +81,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@ -85,19 +89,21 @@
" \n",
" iter = 1\n",
" mv = 0\n",
" e1 = sp.sparse.lil_matrix((1,n))\n",
" e1[0,0] = 1\n",
" x = sp.sparse.lil_matrix((len(a),1))\n",
" I = sp.sparse.eye(n, n, format='lil')\n",
" res = sp.sparse.lil_matrix((len(a),1))\n",
" r = sp.sparse.lil_matrix((n,1))\n",
" y = sp.sparse.lil_matrix((n,1))\n",
" res = np.zeros(len(a)) \n",
"\n",
" for i in range(len(a)): # I don't think that this is what was intended in the pseudocode... \n",
" r = ((1-a[i])**a[i])*v - ((1**a[i])*I - Pt).dot(x)\n",
" res[i] = a[i]*norm(r)\n",
" H_e1 = np.zeros((m+1,1))\n",
" H_e1[0] = 1\n",
"\n",
" V_e1 = np.zeros((n,1))\n",
" V_e1[0] = 1\n",
"\n",
" def Find_k(res, maxit):\n",
" s_e1 = np.zeros((len(a),1))\n",
" s_e1[0] = 1\n",
"\n",
"\n",
" def find_k(res):\n",
" k = 0\n",
" for i in range(len(a)):\n",
" if res[i] == max(res):\n",
@ -105,7 +111,7 @@
" break\n",
" return k\n",
"\n",
" def Find_gamma(res, a, k):\n",
" def compute_gamma(res, a, k):\n",
" gamma = sp.sparse.lil_matrix((len(a),1))\n",
" for i in range(len(a)):\n",
" if i != k:\n",
@ -114,33 +120,118 @@
" gamma[i] = 0\n",
" return gamma\n",
"\n",
" # compute the residual vector\n",
" for i in range(len(a)):\n",
" r = ((1-a[i])/a[i])*v - ((1/a[i])*I - Pt) @ x\n",
" res[i] = a[i]*norm(r)\n",
"\n",
" while max(res) >= tau and iter <= maxit:\n",
" k = find_k(res)\n",
" gamma = compute_gamma(res, a, k)\n",
" # run Arnoldi, with a[k] as the shift\n",
" V, H, v, beta, j = Arnoldi((1/a[k])*I - Pt, r, m)\n",
"\n",
" mv = mv + j\n",
"\n",
" while max(res) > tau and iter < maxit:\n",
" k = Find_k(res, maxit)\n",
" gamma = Find_gamma(res, a, k)\n",
" v, h, m, beta, j = Arnoldi((1**a[k])*I - Pt, r, m)\n",
" Hbar = sp.sparse.lil_matrix((m+1,m))\n",
" Hbar[0:m,0:m] = h\n",
" Hbar[m+1,0:m] = e1\n",
" # compute y as the minimizer of || beta*e1 - Hy ||_2 using the least squares method\n",
" y = sp.sparse.linalg.lsqr(H, beta*H_e1)[0]\n",
" tmp = V[:,0:y.shape[0]]\n",
"\n",
" mv += j\n",
" # reshape y to be a column vector\n",
" y = y.reshape(y.shape[0],1)\n",
" x += tmp @ y\n",
"\n",
" y = sp.sparse.linalg.least_squares(Hbar, beta*e1)\n",
" res[k] = a[k]*norm(beta*e1 - Hbar*y)\n",
" x[k] = x[k] + v*y[k]\n",
" # compute the residual vector\n",
" res[k] = a[k]*np.linalg.norm(beta*V_e1 - tmp @ y)\n",
" \n",
" # for i in range(len(a)) but not k\n",
" for i in range(len(a)):\n",
" if i != k:\n",
" if res[i] >= tau:\n",
" Hbar[i] = Hbar[k] + ((1-a[i])/a[i] - (1-a[k])/a[k])*I\n",
" z = beta*e1 - Hbar*y\n",
" y = sp.sparse.linalg.solve(Hbar, gamma*beta*e1)\n",
" x = x + v*y\n",
" res[i] = a[i]**a[k]*gamma[i]*res[k]\n",
" if i != k and res[i] >= tau:\n",
" H = H + ((1-a[i])/a[i] - (1-a[k])/a[k])*sp.sparse.eye(H.shape[0], H.shape[1], format='lil')\n",
"\n",
" # solve y and gamma[i] from [H z] = [y gamma[i]]^T = gamma[i]*e1*beta\n",
" z = beta*H_e1 - H.dot(y)\n",
" A_tmp = sp.sparse.hstack([H, z]) \n",
" b_tmp = gamma * beta \n",
"\n",
" # make b_tmp a column vector\n",
" b_tmp = b_tmp.reshape(b_tmp.shape[0],1)\n",
" # add zeros to the end of b_tmp until it has the same number of rows as A_tmp\n",
" b_tmp = sp.sparse.vstack([b_tmp, sp.sparse.lil_matrix((A_tmp.shape[0]-b_tmp.shape[0],1))])\n",
"\n",
"\n",
" # solve the system, without using the least squares method\n",
" # CONVERT TO CSC FORMAT TO USE SPARSE SOLVERS\n",
" A_tmp = A_tmp.tocsc()\n",
"\n",
" result = sp.sparse.linalg.spsolve(A_tmp, b_tmp)[0]\n",
" print(\"result:\", result.shape)\n",
" \n",
" \n",
" iter += 1\n",
" # split the result into y and gamma\n",
" y = result[0:y.shape[0]]\n",
" gamma = result[y.shape[0]:]\n",
"\n",
" return x, res, mv\n"
" # update x\n",
" x = x + tmp @ y\n",
" # update residual\n",
" res = (a[i]/a[k])*res*gamma[k]\n",
"\n",
" iter = iter + 1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_264197/102804730.py:12: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.\n",
" A = nx.adjacency_matrix(G)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"result: ()\n"
]
},
{
"ename": "IndexError",
"evalue": "invalid index to scalar variable.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_264197/102804730.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mPt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# run the algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mAlgo4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtau\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/tmp/ipykernel_264197/2668036735.py\u001b[0m in \u001b[0;36mAlgo4\u001b[0;34m(Pt, v, m, a, tau, maxit, x)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;31m# split the result into y and gamma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 86\u001b[0m \u001b[0mgamma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable."
]
}
],
"source": [
"# test the algorithm\n",
"n = 100\n",
"m = 110\n",
"maxit = 100\n",
"tau = 1e-6\n",
"a = [0.85, 0.9, 0.95, 0.99]\n",
"x = sp.sparse.lil_matrix((n,1))\n",
"x[0,0] = 1\n",
"# generate a random graph\n",
"G = nx.gnp_random_graph(n, 0.1, seed=1, directed=True)\n",
"# generate a random matrix\n",
"A = nx.adjacency_matrix(G)\n",
"# generate a random vector\n",
"v = sp.sparse.rand(n, 1, density=0.1, format='lil')\n",
"# compute the power iteration matrix\n",
"Pt = A.T\n",
"# run the algorithm\n",
"Algo4(Pt, v, m, a, tau, maxit, x)"
]
}
],

1
src/main.py
Unescape Escape View File

@ -193,6 +193,7 @@ class Algorithms:
return mv, x, r, total_time
# Refers to Algorithm 2 in the paper, it's needed to implement the algorithm 4. It doesn't work yet. Refer to the file testing.ipynb for more details. This function down here is just a place holder for now
def Arnoldi(A, v, m):
beta = norm(v)
v = v/beta

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