1\documentstyle[11pt,reduce,fancyheadings]{article} 2\title{The Sparse Matrices Package. \\ 3Sparse Matrix Calculations and a Linear Algebra Package for Sparse 4Matrices in \REDUCE{}} 5\author{Stephen Scowcroft \\ 6 Konrad-Zuse-Zentrum f\"ur Informationstechnik Berlin} 7\date{June 1995} 8 9\def\foottitle{The Sparse Matrices Package} 10\pagestyle{fancy} 11\lhead[]{{\footnotesize\leftmark}{}} 12\rhead[]{\thepage} 13\setlength{\headrulewidth}{0.6pt} 14\setlength{\footrulewidth}{0.6pt} 15\cfoot{} 16\rfoot{\small\foottitle} 17 18\def\exprlist {expr$_{1}$,expr$_{2}$, \ldots ,expr$_{{\tt n}}$} 19\def\lineqlist {lin\_eqn$_{1}$,lin\_eqn$_{2}$, \ldots ,lin\_eqn$_{n}$} 20\def\matlist {mat$_{1}$,mat$_{2}$, \ldots ,mat$_{n}$} 21\def\veclist {vec$_{1}$,vec$_{2}$, \ldots ,vec$_{n}$} 22 23\def\lazyfootnote{\footnote{The \{\}'s can be omitted.}} 24 25\renewcommand{\thefootnote}{\fnsymbol{footnote}} 26 27\begin{document} 28\maketitle 29\index{Linear Algebra package} 30 31\section{Introduction} 32A very powerful feature of \REDUCE{} is the ease with which matrix 33calculations can be performed. 34This package extends the available matrix feature to enable calculations 35with sparse matrices. This package also provides a selection of 36functions that are useful in the world of linear algebra with respect to 37sparse matrices. 38 39\subsection*{Loading the Package} 40The package is loaded by: {\tt load\_package sparse;} 41 42\section{Sparse Matrix Calculations} 43To extend the the syntax to this class of calculations we need to add an 44expression type {\tt sparse}. 45 46\subsection{Sparse Variables} 47An identifier may be declared a sparse variable by the declaration 48{\tt SPARSE}. 49The size of the sparse matrix must be declared explicitly in the matrix 50declaration. For example, 51\begin{verbatim} 52sparse aa(10,1),bb(200,200); 53\end{verbatim} 54declares {\tt AA} to be a 10 x 1 (column) sparse matrix and {\tt Y} to 55be a 200 x 200 sparse matrix. 56The declaration {\tt SPARSE} is similar to the declaration {\tt MATRIX}. 57Once a symbol is declared to name a sparse matrix, it can not also be 58used to name an array, operator, procedure, or used as an ordinary 59variable. For more information see the Matrix Variables section in The 60\REDUCE {} User's Manual[2]. 61 62\subsection{Assigning Sparse Matrix Elements} 63Once a matix has been declared a sparse matrix all elements of the 64matrix are initialized to 0. Thus when a sparse matrix is initially 65referred to the message 66\begin{verbatim} 67"The matrix is dense, contains only zeros" 68\end{verbatim} 69is returned. When printing out a matrix only the non-zero elements are 70printed. This is due to the fact that only the non-zero elements of the 71matrix are stored. 72To assign the elements of the declared matrix we use the following 73syntax. Assuming {\tt AA} and {\tt BB} have been declared as spasre 74matrices, we simply write, 75\begin{verbatim} 76aa(1,1):=10; 77bb(100,150):=a; 78\end{verbatim} 79etc. This then sets the element in the first row and first column to 10, 80or the element in the 100th row and 150th column to {\tt a}. 81 82\subsection{Evaluating Sparse Matrix Elements} 83Once an element of a sparse matrix has been assingned, it may be referred 84to in standard array element notation. Thus {\tt aa(2,1)} refers to the 85element in the second row and first column of the sparse matrix {\tt AA}. 86 87\section{Sparse Matrix Expressions} 88These follow the normal rules of matrix algebra. Sums and products must 89be of compatible size; otherwise an error will result during evaluation. 90Similarly, only square matrices may be raised to a power. 91A negative power is computed as the inverse of the matrix raised to the 92corresponding positive power. For more information and the syntax for 93matrix algebra see the Matrix Expressions section in The \REDUCE{} 94User's Manual[2]. 95 96\section{Operators with Sparse Matrix Arguments} 97The operators in the Sparse Matix Package are the same as those in the 98Matrix Packge with the exception that the {\tt NULLSPACE} operator is 99not defined. See section Operators with Matrix Arguments in The 100\REDUCE{} User's Manual[2] for more details. 101\subsection{Examples} 102In the examples the matrix ${\cal AA}$ will be 103 104\begin{flushleft} 105\begin{math} 106{\cal AA} = \left( \begin{array}{cccc} 1 & 0 & 0 & 0 \\ 0 & 3 & 0 & 0 \\ 1070 & 0 & 5 & 0 \\ 0 & 0 & 0 & 9 108\end{array} \right) 109\end{math} 110\end{flushleft} 111\begin {verbatim} 112det ppp; 113 114135 115 116trace ppp; 117 11818 119 120rank ppp; 121 1224 123 124spmateigen(ppp,eta); 125 126{{eta - 1,1, 127 128 spm(1,1) := arbcomplex(4)$ 129 }, 130 131 {eta - 3,1, 132 133 spm(2,1) := arbcomplex(5)$ 134 }, 135 136 {eta - 5,1, 137 138 spm(3,1) := arbcomplex(6)$ 139 }, 140 141 {eta - 9,1, 142 143 spm(4,1) := arbcomplex(7)$ 144 }} 145\end{verbatim} 146 147\section{The Linear Algebra Package for Sparse Matrices} 148This package is an extension of the Linear Algebra Package for \REDUCE{}.[1] 149These functions are described 150alphabetically in section 6 of this document and are labelled 6.1 to 1516.47. They can be classified into four sections(n.b: the numbers after 152the dots signify the function label in section 6). 153\subsection{Basic matrix handling} 154\begin{center} 155\begin{tabular}{l l l l l l} 156spadd\_columns & \ldots & 6.1 & 157spadd\_rows & \ldots & 6.2 \\ 158spadd\_to\_columns & \ldots & 6.3 & 159spadd\_to\_rows & \ldots & 6.4 \\ 160spaugment\_columns & \ldots & 6.5 & 161spchar\_poly & \ldots & 6.9 \\ 162spcol\_dim & \ldots & 6.12 & 163spcopy\_into & \ldots & 6.14 \\ 164spdiagonal & \ldots & 6.15 & 165spextend & \ldots & 6.16 \\ 166spfind\_companion & \ldots & 6.17 & 167spget\_columns & \ldots & 6.18 \\ 168spget\_rows & \ldots & 6.19 & 169sphermitian\_tp & \ldots & 6.21 \\ 170spmatrix\_augment & \ldots & 6.27 & 171spmatrix\_stack & \ldots & 6.29 \\ 172spminor & \ldots & 6.30 & 173spmult\_columns & \ldots & 6.31 \\ 174spmult\_rows & \ldots & 6.32 & 175sppivot & \ldots & 6.33 \\ 176spremove\_columns & \ldots & 6.35 & 177spremove\_rows & \ldots & 6.36 \\ 178sprow\_dim & \ldots & 6.37 & 179sprows\_pivot & \ldots & 6.38 \\ 180spstack\_rows & \ldots & 6.41 & 181spsub\_matrix & \ldots & 6.42 \\ 182spswap\_columns & \ldots & 6.44 & 183spswap\_entries & \ldots & 6.45 \\ 184spswap\_rows & \ldots & 6.46 & 185\end{tabular} 186\end{center} 187 188\subsection{Constructors} 189 190Functions that create sparse matrices. 191 192\begin{center} 193\begin{tabular}{l l l l l l} 194spband\_matrix & \ldots & 6. 6 & 195spblock\_matrix & \ldots & 6. 7 \\ 196spchar\_matrix & \ldots & 6. 8 & 197spcoeff\_matrix & \ldots & 6. 11 \\ 198spcompanion & \ldots & 6. 13 & 199sphessian & \ldots & 6. 22 \\ 200spjacobian & \ldots & 6. 23 & 201spjordan\_block & \ldots & 6. 24 \\ 202spmake\_identity & \ldots & 6. 26 & 203\end{tabular} 204\end{center} 205 206\subsection{High level algorithms} 207 208\begin{center} 209\begin{tabular}{l l l l l l} 210spchar\_poly & \ldots & 6.9 & 211spcholesky & \ldots & 6.10 \\ 212spgram\_schmidt & \ldots & 6.20 & 213splu\_decom & \ldots & 6.25 \\ 214sppseudo\_inverse & \ldots & 6.34 & 215svd & \ldots & 6.43 216\end{tabular} 217\end{center} 218 219\subsection{Predicates} 220 221\begin{center} 222\begin{tabular}{l l l l l l} 223matrixp & \ldots & 6.28 & 224sparsematp & \ldots & 6.39 \\ 225squarep & \ldots & 6.40 & 226symmetricp & \ldots & 6.47 227\end{tabular} 228\end{center} 229 230\subsection*{Note on examples:} 231 232In the examples the matrix ${\cal A}$ will be 233 234\begin{flushleft} 235\begin{math} 236{\cal A} = \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 237\end{array} \right) 238\end{math} 239\end{flushleft} 240Unfortunately, due to restrictions of size, it is not practical to use 241``large'' sparse matrices in the examples. As a result the examples 242shown may appear trivial, but they give an idea of how the functions 243work. 244 245\subsection*{Notation} 246 247Throughout ${\cal I}$ is used to indicate the identity matrix and 248${\cal A}^T$ to indicate the transpose of the matrix ${\cal A}$. 249 250\section{Available Functions} 251 252\subsection{spadd\_columns, spadd\_rows} 253 254\hspace*{0.175in} {\tt spadd\_columns(${\cal A}$,c1,c2,expr);} 255 256\hspace*{0.1in} 257\begin{tabular}{l l l} 258${\cal A}$ & :- & a sparse matrix. \\ 259c1,c2 & :- & positive integers. \\ 260expr & :- & a scalar expression. 261\end{tabular} 262 263{\bf Synopsis:} 264 265\begin{addtolength}{\leftskip}{0.22in} 266\parbox[t]{0.95\linewidth}{{\tt spadd\_columns} replaces column c2 of 267${\cal A}$ by expr $*$ column(${\cal A}$,c1) $+$ column(${\cal A}$,c2).} 268 269{\tt spadd\_rows} performs the equivalent task on the rows of ${\cal A}$. 270 271\end{addtolength} 272 273{\bf Examples:} 274 275\begin{flushleft} 276\begin{math} 277\hspace*{0.16in} 278\begin{array}{ccc} 279{\tt spadd\_columns}({\cal A},1,2,x) & = & 280\left( \begin{array}{ccc} 1 & x & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 281\end{array} \right) 282\end{array} 283\end{math} 284\end{flushleft} 285 286\vspace*{0.1in} 287 288\begin{flushleft} 289\hspace*{0.1in} 290\begin{math} 291\begin{array}{ccc} 292{\tt spadd\_rows}({\cal A},2,3,5) & = & 293\left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 25 & 9 294\end{array} \right) 295\end{array} 296\end{math} 297\end{flushleft} 298 299{\bf Related functions:} 300 301\hspace*{0.175in} {\tt spadd\_to\_columns}, {\tt spadd\_to\_rows}, 302{\tt spmult\_columns}, {\tt spmult\_rows}. 303 304 305\subsection{spadd\_rows} 306 307\hspace*{0.175in} see: {\tt spadd\_columns}. 308 309 310\subsection{spadd\_to\_columns, spadd\_to\_rows} 311 312\hspace*{0.175in} {\tt spadd\_to\_columns(${\cal A}$,column\_list,expr);} 313 314\hspace*{0.1in} 315\begin{tabular}{l l l} 316${\cal A}$ &:-& a sparse matrix. \\ 317column\_list &:-& a positive integer or a list of positive integers. \\ 318expr &:-& a scalar expression. 319\end{tabular} 320 321{\bf Synopsis:} 322 323\begin{addtolength}{\leftskip}{0.22in} 324{\tt spadd\_to\_columns} adds expr to each column specified in 325column\_list of ${\cal A}$. 326 327{\tt spadd\_to\_rows} performs the equivalent task on the rows of 328${\cal A}$. 329 330\end{addtolength} 331 332{\bf Examples:} 333 334\begin{flushleft} 335\hspace*{0.175in} 336\begin{math} 337\begin{array}{ccc} 338{\tt spadd\_to\_columns}({\cal A},\{1,2\},10) & = & 339\left( \begin{array}{ccc} 11 & 10 & 0 \\ 10 & 15 & 0 \\ 10 & 10 & 9 340\end{array} \right) 341\end{array} 342\end{math} 343\end{flushleft} 344 345\vspace*{0.1in} 346 347\begin{flushleft} 348\hspace*{0.175in} 349\begin{math} 350\begin{array}{ccc} 351{\tt spadd\_to\_rows}({\cal A},2,-x) & = & 352\left( \begin{array}{ccc} 1 & 0 & 0 \\ -x & -x+5 & -x \\ 0 & 0 & 9 353\end{array} \right) 354\end{array} 355\end{math} 356\end{flushleft} 357 358{\bf Related functions:} 359 360\hspace*{0.175in} 361{\tt spadd\_columns}, {\tt spadd\_rows}, {\tt spmult\_rows}, 362{\tt spmult\_columns}. 363 364 365\subsection{spadd\_to\_rows} 366 367\hspace*{0.175in} see: {\tt spadd\_to\_columns}. 368 369 370\subsection{spaugment\_columns, spstack\_rows} 371 372\hspace*{0.175in} {\tt spaugment\_columns(${\cal A}$,column\_list);} 373 374\hspace*{0.1in} 375\begin{tabular}{l l l} 376${\cal A}$ &:-& a sparse matrix. \\ 377column\_list &:-& either a positive integer or a list of positive 378 integers. 379\end{tabular} 380 381{\bf Synopsis:} 382 383\begin{addtolength}{\leftskip}{0.22in} 384{\tt spaugment\_columns} gets hold of the columns of ${\cal A}$ specified 385in column\_list and sticks them together. 386 387{\tt spstack\_rows} performs the same task on rows of 388 ${\cal A}$. 389 390\end{addtolength} 391 392{\bf Examples:} 393 394\begin{flushleft} 395\hspace*{0.1in} 396\begin{math} 397\begin{array}{ccc} 398{\tt spaugment\_columns}({\cal A},\{1,2\}) & = & 399\left( \begin{array}{cc} 1 & 0 \\ 0 & 5 \\ 0 & 0 400\end{array} \right) 401\end{array} 402\end{math} 403\end{flushleft} 404 405\vspace*{0.1in} 406 407\begin{flushleft} 408\hspace*{0.1in} 409\begin{math} 410\begin{array}{ccc} 411{\tt spstack\_rows}({\cal A},\{1,3\}) & = & 412\left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 0 & 9 413\end{array} \right) 414\end{array} 415\end{math} 416\end{flushleft} 417 418{\bf Related functions:} 419 420\hspace*{0.175in} {\tt spget\_columns}, {\tt spget\_rows}, 421{\tt spsub\_matrix}. 422 423 424\subsection{spband\_matrix} 425 426\hspace*{0.175in} {\tt spband\_matrix(expr\_list,square\_size);} 427 428\hspace*{0.1in} 429\begin{tabular}{l l l} 430expr\_list \hspace*{0.088in} &:-& \parbox[t]{.72\linewidth} 431{either a single scalar expression or a list of an odd number of scalar 432expressions.} 433\end{tabular} 434 435\vspace*{0.04in} 436\hspace*{0.1in} 437\begin{tabular}{l l l} 438square\_size &:-& a positive integer. 439\end{tabular} 440 441{\bf Synopsis:} 442 443\begin{addtolength}{\leftskip}{0.22in} 444 {\tt spband\_matrix} creates a sparse square matrix of 445 dimension square\_size. 446 447\end{addtolength} 448 449{\bf Examples:} 450 451\begin{flushleft} 452\hspace*{0.1in} 453\begin{math} 454\begin{array}{ccc} 455{\tt spband\_matrix}(\{x,y,z\},6) & = & 456\left( \begin{array}{cccccc} y & z & 0 & 0 & 0 & 0 \\ x & y & z & 0 & 0 457& 0 \\ 0 & x & y & z & 0 & 0 \\ 0 & 0 & x & y & z & 0 \\ 0 & 0 & 0 & x & 458 y & z \\ 0 & 0 & 0 & 0 & x & y 459\end{array} \right) 460\end{array} 461\end{math} 462\end{flushleft} 463 464{\bf Related functions:} 465 466\hspace*{0.175in} {\tt spdiagonal}. 467 468 469\subsection{spblock\_matrix} 470 471\hspace*{0.175in} {\tt spblock\_matrix(r,c,matrix\_list);} 472 473\hspace*{0.1in} 474\begin{tabular}{l l l} 475r,c &:-& positive integers. \\ 476matrix\_list &:-& a list of matrices of either sparse or matrix type. 477\end{tabular} 478 479{\bf Synopsis:} 480 481\begin{addtolength}{\leftskip}{0.22in} 482{\tt spblock\_matrix} creates a sparse matrix that consists of r by c matrices 483filled from the matrix\_list row wise. 484 485\end{addtolength} 486 487 488{\bf Examples:} 489 490\begin{flushleft} 491\hspace*{0.1in} 492\begin{math} 493\begin{array}{ccc} 494{\cal B} = \left( \begin{array}{cc} 1 & 0 \\ 0 & 1 495\end{array} \right), & 496{\cal C} = \left( \begin{array}{c} 5 \\ 0 497\end{array} \right), & 498{\cal D} = \left( \begin{array}{cc} 22 & 0 \\ 0 & 0 499\end{array} \right) 500\end{array} 501\end{math} 502\end{flushleft} 503 504\vspace*{0.175in} 505 506\begin{flushleft} 507\hspace*{0.1in} 508\begin{math} 509\begin{array}{ccc} 510{\tt spblock\_matrix}(2,3,\{{\cal B,C,D,D,C,B}\}) & = & 511\left( \begin{array}{ccccc} 1 & 0 & 5 & 22 & 0 \\ 0 & 1 & 0 & 0 & 0 512\\ 51322 & 0 & 5 & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 514\end{array} \right) 515\end{array} 516\end{math} 517\end{flushleft} 518 519 520\subsection{spchar\_matrix} 521 522\hspace*{0.175in} {\tt spchar\_matrix(${\cal A},\lambda$);} 523 524\hspace*{0.1in} 525\begin{tabular}{l l l} 526${\cal A}$ &:-& a square sparse matrix. \\ 527$\lambda$ &:-& a symbol or algebraic expression. 528\end{tabular} 529 530{\bf Synopsis:} 531 532\begin{addtolength}{\leftskip}{0.22in} 533{\tt spchar\_matrix} creates the characteristic matrix ${\cal C}$ of 534${\cal A}$. 535 536This is ${\cal C} = \lambda * {\cal I} - {\cal A}$. 537 538\end{addtolength} 539 540{\bf Examples:} 541 542\begin{flushleft} 543\hspace*{0.1in} 544\begin{math} 545\begin{array}{ccc} 546{\tt spchar\_matrix}({\cal A},x) & = & 547\left( \begin{array}{ccc} x-1 & 0 & 0 \\ 0 & x-5 & 0 \\ 0 & 0 & x-9 548\end{array} \right) 549\end{array} 550\end{math} 551\end{flushleft} 552 553{\bf Related functions:} 554 555\hspace*{0.175in} {\tt spchar\_poly}. 556 557 558\subsection{spchar\_poly} 559 560\hspace*{0.175in} {\tt spchar\_poly(${\cal A},\lambda$);} 561 562\hspace*{0.1in} 563\begin{tabular}{l l l} 564${\cal A}$ &:-& a sparse square matrix. \\ 565$\lambda$ &:-& a symbol or algebraic expression. 566\end{tabular} 567 568{\bf Synopsis:} 569 570\begin{addtolength}{\leftskip}{0.22in} 571{\tt spchar\_poly} finds the characteristic polynomial of 572 ${\cal A}$. 573 574This is the determinant of $\lambda * {\cal I} - {\cal A}$. 575 576\end{addtolength} 577 578{\bf Examples:} 579 580\hspace*{0.175in} 581{\tt spchar\_poly({\cal A},$x$) $= x^3-15*x^2-59*x-45$} 582 583{\bf Related functions:} 584 585\hspace*{0.175in} {\tt spchar\_matrix}. 586 587 588\subsection{spcholesky} 589 590\hspace*{0.175in} {\tt spcholesky(${\cal A}$);} 591 592\hspace*{0.1in} 593\begin{tabular}{l l l} 594${\cal A}$ &:-& a positive definite sparse matrix containing numeric entries. 595\end{tabular} 596 597{\bf Synopsis:} 598 599\begin{addtolength}{\leftskip}{0.22in} 600{\tt spcholesky} computes the cholesky decomposition of ${\cal A}$. 601 602It returns \{${\cal L,U}$\} where ${\cal L}$ 603is a lower matrix, ${\cal U}$ is an upper matrix, \\ ${\cal A} = 604{\cal LU}$, and ${\cal U} = {\cal L}^T$. 605 606\end{addtolength} 607 608{\bf Examples:} 609 610\begin{flushleft} 611\hspace*{0.175in} 612\begin{math} 613{\cal F} = \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 6149 615\end{array} \right) 616\end{math} 617\end{flushleft} 618 619\begin{flushleft} 620\hspace*{0.1in} 621\begin{math} 622\begin{array}{ccc} 623${\tt cholesky}$({\cal F}) & = & 624\left\{ \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & \sqrt{5} 625& 0 \\ 6260 & 0& 3 \end{array} \right), \left( 627\begin{array}{ccc} 1 & 0 & 0 \\ 0 & \sqrt{5} & 0 \\ 0 628& 0 & 3 \end{array} \right) 629\right\} \end{array} 630\end{math} 631\end{flushleft} 632 633{\bf Related functions:} 634 635\hspace*{0.175in} {\tt splu\_decom}. 636 637 638\subsection{spcoeff\_matrix} 639 640\hspace*{0.175in} {\tt spcoeff\_matrix(\{\lineqlist{}\});} 641 642\hspace*{0.1in} 643\begin{tabular}{l l l} 644\lineqlist &:-& \parbox[t]{.435\linewidth}{linear equations. Can be 645of the form {\it equation $=$ number} or just {\it equation}.} 646\end{tabular} 647 648{\bf Synopsis:} 649 650\begin{addtolength}{\leftskip}{0.22in} 651{\tt spcoeff\_matrix} creates the coefficient matrix 652 ${\cal C}$ of the linear equations. 653 654It returns \{${\cal C,X,B}$\} such that ${\cal CX} = {\cal B}$. 655 656\end{addtolength} 657 658 659{\bf Examples:} 660 661\begin{math} 662\hspace*{0.175in} 663{\tt spcoeff\_matrix}(\{y-20*w=10,y-z=20,y+4+3*z,w+x+50\}) = 664\end{math} 665 666\vspace*{0.1in} 667 668\begin{flushleft} 669\hspace*{0.175in} 670\begin{math} 671\left\{ \left( \begin{array}{cccc} 1 & -20 & 0 & 0 \\ 1 & 0 & -1 & 0 \\ 672 1 & 0 & 3 & 0 \\ 0 & 1 & 0 & 1 673\end{array} \right), \left( \begin{array}{c} y \\ w \\ z \\ x \end{array} 674\right), \left( \begin{array}{c} 10 \\ 20 \\ -4 \\ 50 675\end{array} \right) \right\} 676\end{math} 677\end{flushleft} 678 679\subsection{spcol\_dim, sprow\_dim} 680 681\hspace*{0.175in} {\tt column\_dim(${\cal A}$);} 682 683\hspace*{0.1in} 684\begin{tabular}{l l l} 685${\cal A}$ &:-& a sparse matrix. 686\end{tabular} 687 688{\bf Synopsis:} 689 690\hspace*{0.175in} {\tt spcol\_dim} finds the column dimension of 691 ${\cal A}$. 692 693\hspace*{0.175in} {\tt sprow\_dim} finds the row dimension of ${\cal A}$. 694 695{\bf Examples:} 696 697\hspace*{0.175in} 698{\tt spcol\_dim}(${\cal A}$) = 3 699 700\subsection{spcompanion} 701 702\hspace*{0.175in} {\tt spcompanion(poly,x);} 703 704\hspace*{0.1in} 705\begin{tabular}{l l l} 706poly &:-& a monic univariate polynomial in x. \\ 707x &:-& the variable. 708\end{tabular} 709 710{\bf Synopsis:} 711 712\begin{addtolength}{\leftskip}{0.22in} 713 {\tt spcompanion} creates the companion matrix ${\cal C}$ 714 of poly. 715 716This is the square matrix of dimension n, where n is the degree of poly 717w.r.t. x. 718 719The entries of ${\cal C}$ are: 720 ${\cal C}$(i,n) = -coeffn(poly,x,i-1) for i = 1 721 \ldots n, ${\cal C}$(i,i-1) = 1 for i = 2 \ldots n and 722 the rest are 0. 723 724\end{addtolength} 725 726 727{\bf Examples:} 728 729\begin{flushleft} 730\hspace*{0.1in} 731\begin{math} 732\begin{array}{ccc} 733{\tt spcompanion}(x^4+17*x^3-9*x^2+11,x) & = & 734\left( \begin{array}{cccc} 0 & 0 & 0 & -11 \\ 1 & 0 & 0 & 0 \\ 7350 & 1 & 0 & 9 \\ 0 & 0 & 1 & -17 736\end{array} \right) 737\end{array} 738\end{math} 739\end{flushleft} 740 741{\bf Related functions:} 742 743\hspace*{0.175in} {\tt spfind\_companion}. 744 745 746\subsection{spcopy\_into} 747 748\hspace*{0.175in} {\tt spcopy\_into(${\cal A,B}$,r,c);} 749 750\hspace*{0.1in} 751\begin{tabular}{l l l} 752${\cal A,B}$ &:-& matrices of type sparse or matrix. \\ 753r,c &:-& positive integers. 754\end{tabular} 755 756{\bf Synopsis:} %{\bf What it does:} 757 758\hspace*{0.175in} {\tt spcopy\_into} copies matrix ${\cal A}$ into 759 ${\cal B}$ with ${\cal A}$(1,1) at ${\cal B}$(r,c). 760 761{\bf Examples:} 762 763\begin{flushleft} 764\hspace*{0.175in} 765\begin{math} 766{\cal G} = \left( \begin{array}{cccc} 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 7670 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 768\end{array} \right) 769\end{math} 770\end{flushleft} 771 772\begin{flushleft} 773\hspace*{0.1in} 774\begin{math} 775\begin{array}{ccc} 776{\tt spcopy\_into}({\cal A,G},1,2) & = & 777\left( \begin{array}{cccc} 0 & 1 & 0 & 0 \\ 0 & 0 & 5 & 0 \\ 0 & 0 & 0 778& 9 \\ 0 & 0 & 0 & 0 779\end{array} \right) 780\end{array} 781\end{math} 782\end{flushleft} 783 784{\bf Related functions:} 785 786\begin{addtolength}{\leftskip}{0.22in} 787{\tt spaugment\_columns}, {\tt spextend}, {\tt spmatrix\_augment}, 788{\tt spmatrix\_stack}, {\tt spstack\_rows}, {\tt spsub\_matrix}. 789 790\end{addtolength} 791 792 793\subsection{spdiagonal} 794 795\hspace*{0.175in} {\tt spdiagonal(\{\matlist{}\});}\lazyfootnote{} 796 797\hspace*{0.1in} 798\begin{tabular}{l l l} 799\matlist &:-& \parbox[t]{.58\linewidth}{each can be either a scalar 800expr or a square matrix of sparse or matrix type. } 801\end{tabular} 802 803{\bf Synopsis:} %{\bf What it does:} 804 805\hspace*{0.175in} {\tt spdiagonal} creates a sparse matrix that contains the 806input on the diagonal. 807 808{\bf Examples:} 809 810\begin{flushleft} 811\hspace*{0.175in} 812\begin{math} 813{\cal H} = \left( \begin{array}{cc} 66 & 77 \\ 88 & 99 814\end{array} \right) 815\end{math} 816\end{flushleft} 817 818\begin{flushleft} 819\hspace*{0.1in} 820\begin{math} 821\begin{array}{ccc} 822{\tt spdiagonal}(\{{\cal A},x,{\cal H}\}) & = & 823\left( \begin{array}{cccccc} 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 5 & 0 & 0 & 0 824& 0 \\ 0 & 0 & 9 & 0 & 0 & 0 \\ 0 & 0 & 0 & x & 0 & 0 \\ 0 & 0 & 0 & 0 825& 66 & 77 \\ 0 & 0 & 0 & 0 & 88 & 99 826\end{array} \right) 827\end{array} 828\end{math} 829\end{flushleft} 830 831{\bf Related functions:} 832 833\hspace*{0.175in} {\tt spjordan\_block}. 834 835 836\subsection{spextend} 837 838\hspace*{0.175in} {\tt spextend(${\cal A}$,r,c,expr);} 839 840\hspace*{0.1in} 841\begin{tabular}{l l l} 842${\cal A}$ &:-& a sparse matrix. \\ 843r,c &:-& positive integers. \\ 844expr &:-& algebraic expression or symbol. 845\end{tabular} 846 847{\bf Synopsis:} 848 849\begin{addtolength}{\leftskip}{0.22in} 850 {\tt spextend} returns a copy of ${\cal A}$ that has been 851 extended by r rows and c columns. The new entries are 852 made equal to expr. 853 854\end{addtolength} 855 856{\bf Examples:} 857 858\begin{flushleft} 859\hspace*{0.1in} 860\begin{math} 861\begin{array}{ccc} 862{\tt spextend}({\cal A},1,2,0) & = & 863\left( \begin{array}{ccccc} 1 & 0 & 0 & 0 & 0 \\ 0 & 5 & 0 & 0 & 0 864\\ 0 & 0 & 9 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 865\end{array} \right) 866\end{array} 867\end{math} 868\end{flushleft} 869 870{\bf Related functions:} 871 872\begin{addtolength}{\leftskip}{0.22in} 873\parbox[t]{0.95\linewidth}{{\tt spcopy\_into}, {\tt spmatrix\_augment}, 874{\tt spmatrix\_stack}, {\tt spremove\_columns}, {\tt spremove\_rows}.} 875 876\end{addtolength} 877 878 879\subsection{spfind\_companion} 880 881\hspace*{0.175in} {\tt spfind\_companion(${\cal A}$,x);} 882 883\hspace*{0.1in} 884\begin{tabular}{l l l} 885${\cal A}$ &:-& a sparse matrix. \\ 886x &:-& the variable. 887\end{tabular} 888 889{\bf Synopsis:} 890 891\begin{addtolength}{\leftskip}{0.22in} 892 Given a sparse companion matrix, {\tt spfind\_companion} finds the polynomial 893from which it was made. 894 895\end{addtolength} 896 897 898{\bf Examples:} 899 900\begin{flushleft} 901\hspace*{0.175in} 902\begin{math} 903{\cal C} = \left( \begin{array}{cccc} 0 & 0 & 0 & -11 \\ 1 & 0 & 0 & 0 904\\ 0 & 1 & 0 & 9 \\ 0 & 0 & 1 & -17 905\end{array} \right) 906\end{math} 907\end{flushleft} 908 909\vspace*{3mm} 910 911\begin{flushleft} 912\hspace*{0.175in} 913\begin{math} 914{\tt spfind\_companion}({\cal C},x) = x^4+17*x^3-9*x^2+11 915\end{math} 916\end{flushleft} 917 918\vspace*{3mm} 919 920{\bf Related functions:} 921 922\hspace*{0.175in} {\tt spcompanion}. 923 924\subsection{spget\_columns, spget\_rows} 925 926\hspace*{0.175in} {\tt spget\_columns(${\cal A}$,column\_list);} 927 928\hspace*{0.1in} 929\begin{tabular}{l l l} 930${\cal A}$ &:-& a sparse matrix. \\ 931c &:-& either a positive integer or a list of positive 932 integers. 933\end{tabular} 934 935{\bf Synopsis:} %{\bf What it does:} 936 937\begin{addtolength}{\leftskip}{0.22in} 938{\tt spget\_columns} removes the columns of ${\cal A}$ specified in 939 column\_list and returns them as a list of column 940 matrices. 941 942\end{addtolength} 943\hspace*{0.175in} {\tt spget\_rows} performs the same task on the rows of 944 ${\cal A}$. 945 946{\bf Examples:} 947 948\begin{flushleft} 949\hspace*{0.1in} 950\begin{math} 951\begin{array}{ccc} 952{\tt spget\_columns}({\cal A},\{1,3\}) & = & 953\left\{ 954 \left( \begin{array}{c} 1 \\ 0 \\ 0 \end{array} \right), 955 \left( \begin{array}{c} 0 \\ 0 \\ 9 \end{array} \right) 956\right\} 957\end{array} 958\end{math} 959\end{flushleft} 960 961\vspace*{0.1in} 962 963\begin{flushleft} 964\hspace*{0.1in} 965\begin{math} 966\begin{array}{ccc} 967{\tt spget\_rows}({\cal A},2) & = & 968\left\{ 969 \left( \begin{array}{ccc} 0 & 5 & 0 \end{array} \right) 970\right\} 971\end{array} 972\end{math} 973\end{flushleft} 974 975{\bf Related functions:} 976 977\hspace*{0.175in} {\tt spaugment\_columns}, {\tt spstack\_rows}, 978{\tt spsub\_matrix}. 979 980 981\subsection{spget\_rows} 982 983\hspace*{0.175in} see: {\tt spget\_columns}. 984 985 986\subsection{spgram\_schmidt} 987 988\hspace*{0.175in} {\tt spgram\_schmidt(\{\veclist{}\});} 989 990\hspace*{0.1in} 991\begin{tabular}{l l l} 992\veclist &:-& \parbox[t]{.62\linewidth}{linearly independent vectors. 993 Each vector must be written as a list of 994predefined sparse (column) matrices, eg: sparse a(4,1);, a(1,1):=1;} 995\end{tabular} 996 997{\bf Synopsis:} 998 999\begin{addtolength}{\leftskip}{0.22in} 1000{\tt spgram\_schmidt} performs the gram\_schmidt 1001 orthonormalisation on the input vectors. 1002 1003It returns a list of orthogonal normalised vectors. 1004 1005\end{addtolength} 1006 1007{\bf Examples:} 1008 1009Suppose a,b,c,d correspond to sparse matrices representing the following 1010lists:- \{\{1,0,0,0\},\{1,1,0,0\},\{1,1,1,0\},\{1,1,1,1\}\}. 1011 1012{\tt spgram\_schmidt(\{\{a\},\{b\},\{c\},\{d\}\})} = 1013\{\{1,0,0,0\},\{0,1,0,0\},\{0,0,1,0\},\{0,0,0,1\}\} 1014 1015\subsection{sphermitian\_tp} 1016 1017\hspace*{0.175in} {\tt sphermitian\_tp(${\cal A}$);} 1018 1019\hspace*{0.1in} 1020\begin{tabular}{l l l} 1021${\cal A}$ &:-& a sparse matrix. 1022\end{tabular} 1023 1024{\bf Synopsis:} 1025 1026\begin{addtolength}{\leftskip}{0.22in} 1027 {\tt sphermitian\_tp} computes the hermitian transpose of 1028 ${\cal A}$. 1029 1030\end{addtolength} 1031 1032{\bf Examples:} 1033 1034\begin{flushleft} 1035\hspace*{0.175in} 1036\begin{math} 1037{\cal J} = \left( \begin{array}{ccc} i+1 & i+2 & i+3 \\ 0 & 0 & 0 \\ 0 & 1038i & 0 1039\end{array} \right) 1040\end{math} 1041\end{flushleft} 1042 1043\vspace*{0.1in} 1044 1045\begin{flushleft} 1046\hspace*{0.1in} 1047\begin{math} 1048\begin{array}{ccc} 1049{\tt sphermitian\_tp}({\cal J}) & = & 1050\left( \begin{array}{ccc} -i+1 & 0 & 0 \\ -i+2 & 0 & -i \\-i+3 & 0 & 0 1051\end{array} \right) 1052\end{array} 1053\end{math} 1054\end{flushleft} 1055 1056{\bf Related functions:} 1057 1058\hspace*{0.175in} {\tt tp}\footnote{standard reduce call for the 1059transpose of a matrix - see {\REDUCE} User's Manual[2].}. 1060 1061 1062\subsection{sphessian} 1063 1064\hspace*{0.175in} {\tt sphessian(expr,variable\_list);} 1065 1066\hspace*{0.1in} 1067\begin{tabular}{l l l} 1068expr &:-& a scalar expression. \\ 1069variable\_list &:-& either a single variable or a list of variables. 1070\end{tabular} 1071 1072{\bf Synopsis:} 1073 1074\begin{addtolength}{\leftskip}{0.22in} 1075 {\tt sphessian} computes the hessian matrix of expr w.r.t. 1076 the variables in variable\_list. 1077 1078\end{addtolength} 1079 1080{\bf Examples:} 1081 1082\begin{flushleft} 1083\hspace*{0.1in} 1084\begin{math} 1085\begin{array}{ccc} 1086{\tt sphessian}(x*y*z+x^2,\{w,x,y,z\}) & = & 1087\left( \begin{array}{cccc} 0 & 0 & 0 & 0 \\ 0 & 2 & z & y \\ 0 & z & 0 1088& x \\ 0 & y & x & 0 1089\end{array} \right) 1090\end{array} 1091\end{math} 1092\end{flushleft} 1093 1094 1095\subsection{spjacobian} 1096 1097\hspace*{0.175in} {\tt spjacobian(expr\_list,variable\_list);} 1098 1099\hspace*{0.1in} 1100\begin{tabular}{l l l} 1101expr\_list \hspace*{0.175in} &:-& \parbox[t]{.72\linewidth}{either a 1102single algebraic expression or a list of algebraic expressions.} 1103\end{tabular} 1104 1105\vspace*{0.04in} 1106\hspace*{0.1in} 1107\begin{tabular}{l l l} 1108variable\_list &:-& either a single variable or a list of variables. 1109\end{tabular} 1110 1111{\bf Synopsis:} 1112 1113\begin{addtolength}{\leftskip}{0.22in} 1114{\tt spjacobian} computes the jacobian matrix of expr\_list w.r.t. 1115variable\_list. 1116 1117\end{addtolength} 1118 1119{\bf Examples:} 1120 1121\hspace*{0.175in} 1122{\tt spjacobian(\{$x^4,x*y^2,x*y*z^3$\},\{$w,x,y,z$\})} = 1123 1124\vspace*{0.1in} 1125 1126\begin{flushleft} 1127\hspace*{0.175in} 1128\begin{math} 1129\left( \begin{array}{cccc} 0 & 4*x^3 & 0 & 0 \\ 0 & y^2 & 2*x*y & 0 \\ 11300 & y*z^3 & x*z^3 & 3*x*y*z^2 1131\end{array} \right) 1132\end{math} 1133\end{flushleft} 1134 1135{\bf Related functions:} 1136 1137\hspace*{0.175in} {\tt sphessian}, {\tt df}\footnote{standard reduce call 1138for differentiation - see {\REDUCE} User's Manual[2].}. 1139 1140 1141\subsection{spjordan\_block} 1142 1143\hspace*{0.175in} {\tt spjordan\_block(expr,square\_size);} 1144 1145\hspace*{0.1in} 1146\begin{tabular}{l l l} 1147expr &:-& an algebraic expression or symbol. \\ 1148square\_size &:-& a positive integer. 1149\end{tabular} 1150 1151{\bf Synopsis:} 1152 1153\begin{addtolength}{\leftskip}{0.22in} 1154{\tt spjordan\_block} computes the square jordan block matrix ${\cal J}$ 1155 of dimension square\_size. 1156 1157\end{addtolength} 1158 1159{\bf Examples:} 1160 1161\begin{flushleft} 1162\hspace*{0.1in} 1163\begin{math} 1164\begin{array}{ccc} 1165{\tt spjordan\_block(x,5)} & = & 1166\left( \begin{array}{ccccc} x & 1 & 0 & 0 & 0 \\ 0 & x & 1 & 0 & 0 \\ 0 1167& 0 & x & 1 & 0 \\ 0 & 0 & 0 & x & 1 \\ 0 & 0 & 0 & 0 & x 1168\end{array} \right) 1169\end{array} 1170\end{math} 1171\end{flushleft} 1172 1173{\bf Related functions:} 1174 1175\hspace*{0.175in} {\tt spdiagonal}, {\tt spcompanion}. 1176 1177 1178\subsection{splu\_decom} 1179 1180%{\bf How to use it:} 1181 1182\hspace*{0.175in} {\tt splu\_decom(${\cal A}$);} 1183 1184\hspace*{0.1in} 1185\begin{tabular}{l l l} 1186${\cal A}$ &:-& \parbox[t]{.848\linewidth}{a sparse matrix containing either 1187numeric entries or imaginary entries with numeric coefficients.} 1188\end{tabular} 1189 1190{\bf Synopsis:} %{\bf What it does:} 1191 1192\begin{addtolength}{\leftskip}{0.22in} 1193 {\tt splu\_decom} performs LU decomposition on ${\cal A}$, 1194 ie: it returns \{${\cal L,U}$\} where ${\cal L}$ 1195 is a lower diagonal matrix, ${\cal U}$ an upper diagonal 1196 matrix and ${\cal A} = {\cal LU}$. 1197 1198\end{addtolength} 1199 1200{\bf caution:} 1201 1202\begin{addtolength}{\leftskip}{0.22in} 1203The algorithm used can swap the rows of ${\cal A}$ 1204 during the calculation. This means that ${\cal LU}$ does 1205 not equal ${\cal A}$ but a row equivalent of it. Due to 1206 this, {\tt splu\_decom} returns \{${\cal L,U}$,vec\}. The 1207 call {\tt spconvert(${\cal A}$,vec)} will return the 1208 sparse matrix that has been decomposed, ie: ${\cal LU} = $ 1209 {\tt spconvert(${\cal A}$,vec)}. 1210 1211\end{addtolength} 1212 1213{\bf Examples:} 1214 1215\begin{flushleft} 1216\hspace*{0.175in} 1217\begin{math} 1218{\cal K} = \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 1219\end{array} \right) 1220\end{math} 1221\end{flushleft} 1222 1223\begin{flushleft} 1224\hspace*{0.1in} 1225\begin{math} 1226\begin{array}{cccc} 1227${\tt lu} := {\tt splu\_decom}$({\cal K}) & = & 1228\left\{ 1229 \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 \end{array} \right), 1230 \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 1231\end{array} \right), 1232 [\; 1 \; 2 \; 3 \; ] 1233\right\} 1234\end{array} 1235\end{math} 1236\end{flushleft} 1237 1238\vspace*{0.1in} 1239 1240\begin{flushleft} 1241\hspace*{0.1in} 1242\begin{math} 1243\begin{array}{ccc} 1244${\tt first lu * second lu}$ & = & 1245 \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 1246 \end{array} \right) 1247\end{array} 1248\end{math} 1249\end{flushleft} 1250 1251\begin{flushleft} 1252\hspace*{0.1in} 1253\begin{math} 1254\begin{array}{ccc} 1255${\tt convert(${\cal K}$,third lu}$) \hspace*{0.055in} & = & 1256 \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 1257 \end{array} \right) 1258\end{array} 1259\end{math} 1260\end{flushleft} 1261 1262{\bf Related functions:} 1263 1264\hspace*{0.175in} {\tt spcholesky}. 1265 1266 1267\subsection{spmake\_identity} 1268 1269\hspace*{0.175in} {\tt spmake\_identity(square\_size);} 1270 1271\hspace*{0.1in} 1272\begin{tabular}{l l l} 1273square\_size &:-& a positive integer. 1274\end{tabular} 1275 1276{\bf Synopsis:} 1277 1278\hspace*{0.175in} {\tt spmake\_identity} creates the identity matrix of 1279 dimension square\_size. 1280 1281{\bf Examples:} 1282 1283\begin{flushleft} 1284\hspace*{0.1in} 1285\begin{math} 1286\begin{array}{ccc} 1287{\tt spmake\_identity}(4) & = & 1288 \left( \begin{array}{cccc} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 1289& 0 & 1 & 0 \\ 0 & 0 & 0 & 1 1290 \end{array} \right) 1291\end{array} 1292\end{math} 1293\end{flushleft} 1294 1295{\bf Related functions:} 1296 1297\hspace*{0.175in} {\tt spdiagonal}. 1298 1299 1300\subsection{spmatrix\_augment, spmatrix\_stack} 1301 1302\hspace*{0.175in} {\tt spmatrix\_augment(\{\matlist\});}\lazyfootnote{} 1303 1304\hspace*{0.1in} 1305\begin{tabular}{l l l} 1306\matlist &:-& matrices. 1307\end{tabular} 1308 1309{\bf Synopsis:} 1310 1311\hspace*{0.175in} {\tt spmatrix\_augment} joins the matrices in 1312 matrix\_list together horizontally. 1313 1314\hspace*{0.175in} 1315{\tt spmatrix\_stack} joins the matrices in matrix\_list 1316 together vertically. 1317 1318{\bf Examples:} 1319 1320\begin{flushleft} 1321\hspace*{0.1in} 1322\begin{math} 1323\begin{array}{ccc} 1324{\tt spmatrix\_augment}(\{{\cal A,A}\}) & = & 1325 \left( \begin{array}{cccccc} 1 & 0 & 0 & 1 & 0 & 0 \\ 0 & 5 & 0 1326& 0 & 5 & 0 \\ 0 & 0 & 9 & 0 & 0 & 9 1327 \end{array} \right) 1328\end{array} 1329\end{math} 1330\end{flushleft} 1331 1332\vspace*{0.1in} 1333 1334\begin{flushleft} 1335\hspace*{0.1in} 1336\begin{math} 1337\begin{array}{ccc} 1338{\tt spmatrix\_stack}(\{{\cal A,A}\}) & = & 1339 \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 1340\\ 1 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9 1341 \end{array} \right) 1342\end{array} 1343\end{math} 1344\end{flushleft} 1345 1346{\bf Related functions:} 1347 1348\hspace*{0.175in} {\tt spaugment\_columns}, {\tt spstack\_rows}, 1349{\tt spsub\_matrix}. 1350 1351 1352\subsection{matrixp} 1353 1354%{\bf How to use it:} 1355 1356\hspace*{0.175in} {\tt matrixp(test\_input);} 1357 1358\hspace*{0.1in} 1359\begin{tabular}{l l l} 1360test\_input &:-& anything you like. 1361\end{tabular} 1362 1363{\bf Synopsis:} %{\bf What it does:} 1364 1365\begin{addtolength}{\leftskip}{0.22in} 1366{\tt matrixp} is a boolean function that returns t if 1367 the input is a matrix of type sparse or matrix and nil otherwise. 1368 1369\end{addtolength} 1370 1371{\bf Examples:} 1372 1373\hspace*{0.175in} {\tt matrixp}(${\cal A}$) = t 1374 1375\hspace*{0.175in} {\tt matrixp}(doodlesackbanana) = nil 1376 1377{\bf Related functions:} 1378 1379\hspace*{0.175in} {\tt squarep}, {\tt symmetricp}, {\tt sparsematp}. 1380 1381 1382\subsection{spmatrix\_stack} 1383 1384\hspace*{0.175in} see: {\tt spmatrix\_augment}. 1385 1386 1387\subsection{spminor} 1388 1389\hspace*{0.175in} {\tt spminor(${\cal A}$,r,c);} 1390 1391\hspace*{0.1in} 1392\begin{tabular}{l l l} 1393${\cal A}$ &:-& a sparse matrix. \\ 1394r,c &:-& positive integers. 1395\end{tabular} 1396 1397{\bf Synopsis:} 1398 1399\begin{addtolength}{\leftskip}{0.22in} 1400 {\tt spminor} computes the (r,c)'th minor of ${\cal A}$. 1401 1402\end{addtolength} 1403 1404{\bf Examples:} 1405 1406\begin{flushleft} 1407\hspace*{0.1in} 1408\begin{math} 1409\begin{array}{ccc} 1410{\tt spminor}({\cal A},1,3) & = & 1411 \left( \begin{array}{cc} 0 & 5 \\ 0 & 0 1412 \end{array} \right) 1413\end{array} 1414\end{math} 1415\end{flushleft} 1416 1417{\bf Related functions:} 1418 1419\hspace*{0.175in} {\tt spremove\_columns}, {\tt spremove\_rows}. 1420 1421 1422\subsection{spmult\_columns, spmult\_rows} 1423 1424\hspace*{0.175in} {\tt spmult\_columns(${\cal A}$,column\_list,expr);} 1425 1426\hspace*{0.1in} 1427\begin{tabular}{l l l} 1428${\cal A}$ &:-& a sparse matrix. \\ 1429column\_list &:-& a positive integer or a list of positive integers. \\ 1430expr &:-& an algebraic expression. 1431\end{tabular} 1432 1433{\bf Synopsis:} 1434 1435\begin{addtolength}{\leftskip}{0.22in} 1436{\tt spmult\_columns} returns a copy of ${\cal A}$ in which 1437 the columns specified in column\_list have been 1438multiplied by expr. 1439 1440{\tt spmult\_rows} performs the same task on the rows of ${\cal A}$. 1441 1442\end{addtolength} 1443 1444{\bf Examples:} 1445 1446\begin{flushleft} 1447\hspace*{0.1in} 1448\begin{math} 1449\begin{array}{ccc} 1450{\tt spmult\_columns}({\cal A},\{1,3\},x) & = & 1451 \left( \begin{array}{ccc} x & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 9*x 1452 \end{array} \right) 1453\end{array} 1454\end{math} 1455\end{flushleft} 1456 1457\vspace*{0.1in} 1458 1459\begin{flushleft} 1460\hspace*{0.1in} 1461\begin{math} 1462\begin{array}{ccc} 1463{\tt spmult\_rows}({\cal A},2,10) & = & 1464 \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 50 & 0 \\ 0 & 0 & 14659 \end{array} \right) 1466\end{array} 1467\end{math} 1468\end{flushleft} 1469 1470{\bf Related functions:} 1471 1472\hspace*{0.175in} {\tt spadd\_to\_columns}, {\tt spadd\_to\_rows}. 1473 1474 1475\subsection{\tt spmult\_rows} 1476 1477\hspace*{0.175in} see: {\tt spmult\_columns}. 1478 1479 1480\subsection{sppivot} 1481 1482\hspace*{0.175in} {\tt sppivot(${\cal A}$,r,c);} 1483 1484\hspace*{0.1in} 1485\begin{tabular}{l l l} 1486${\cal A}$ &:-& a sparse matrix. \\ 1487r,c &:-& positive integers such that ${\cal A}$(r,c) neq 0. 1488\end{tabular} 1489 1490{\bf Synopsis:} %{\bf What it does:} 1491 1492\begin{addtolength}{\leftskip}{0.22in} 1493{\tt sppivot} pivots ${\cal A}$ about it's (r,c)'th entry. 1494 1495To do this, multiples of the r'th row are added to every 1496 other row in the matrix. 1497 1498This means that the c'th column 1499 will be 0 except for the (r,c)'th entry. 1500 1501\end{addtolength} 1502 1503{\bf Related functions:} 1504 1505\hspace*{0.175in} {\tt sprows\_pivot}. 1506 1507 1508\subsection{sppseudo\_inverse} 1509 1510\hspace*{0.175in} {\tt sppseudo\_inverse(${\cal A}$);} 1511 1512\hspace*{0.1in} 1513\begin{tabular}{l l l} 1514${\cal A}$ &:-& a sparse matrix. 1515\end{tabular} 1516 1517{\bf Synopsis:} 1518 1519\begin{addtolength}{\leftskip}{0.22in} 1520{\tt sppseudo\_inverse}, also known as the Moore-Penrose inverse, computes 1521the pseudo inverse of ${\cal A}$. 1522 1523\end{addtolength} 1524 1525{\bf Examples:} 1526 1527\begin{flushleft} 1528\hspace*{0.175in} 1529\begin{math} 1530{\cal R} = \left( \begin{array}{cccc} 0 & 0 & 3 & 0 \\ 9 & 0 & 7 & 0 1531\end{array} \right) 1532\end{math} 1533\end{flushleft} 1534 1535\begin{flushleft} 1536\hspace*{0.1in} 1537\begin{math} 1538\begin{array}{ccc} 1539{\tt sppseudo\_inverse}({\cal R}) & = & 1540 \left( \begin{array}{cc} -0.26 & 0.11 \\ 0 & 0 \\ 0.33 & 0 1541\\ 0.25 & -0.05 1542 \end{array} \right) 1543\end{array} 1544\end{math} 1545\end{flushleft} 1546 1547{\bf Related functions:} 1548 1549\hspace*{0.175in} {\tt spsvd}. 1550 1551\subsection{spremove\_columns, spremove\_rows} 1552 1553\hspace*{0.175in} {\tt spremove\_columns(${\cal A}$,column\_list);} 1554 1555\hspace*{0.1in} 1556\begin{tabular}{l l l} 1557${\cal A}$ &:-& a sparse matrix. \\ 1558column\_list &:-& either a positive integer or a list of 1559 positive integers. 1560\end{tabular} 1561 1562{\bf Synopsis:} 1563 1564\hspace*{0.175in} {\tt spremove\_columns} removes the columns specified in 1565 column\_list from ${\cal A}$. 1566 1567\hspace*{0.175in} {\tt spremove\_rows} performs the same task on the rows 1568 of ${\cal A}$. 1569 1570{\bf Examples:} 1571 1572\begin{flushleft} 1573\hspace*{0.1in} 1574\begin{math} 1575\begin{array}{ccc} 1576{\tt spremove\_columns}({\cal A},2) & = & 1577 \left( \begin{array}{cc} 1 & 0 \\ 0 & 0 \\ 0 & 9 1578 \end{array} \right) 1579\end{array} 1580\end{math} 1581\end{flushleft} 1582 1583\vspace*{0.1in} 1584 1585\begin{flushleft} 1586\hspace*{0.1in} 1587\begin{math} 1588\begin{array}{ccc} 1589{\tt spremove\_rows}({\cal A},\{1,3\}) & = & 1590 \left( \begin{array}{ccc} 0 & 5 & 0 1591 \end{array} \right) 1592\end{array} 1593\end{math} 1594\end{flushleft} 1595 1596 1597{\bf Related functions:} 1598 1599\hspace*{0.175in} {\tt spminor}. 1600 1601 1602\subsection{spremove\_rows} 1603 1604\hspace*{0.175in} see: {\tt spremove\_columns}. 1605 1606 1607\subsection{sprow\_dim} 1608 1609\hspace{0.175in} see: {\tt spcolumn\_dim}. 1610 1611 1612\subsection{sprows\_pivot} 1613 1614\hspace*{0.175in} {\tt sprows\_pivot(${\cal A}$,r,c,\{row\_list\});} 1615 1616\hspace*{0.1in} 1617\begin{tabular}{l l l} 1618${\cal A}$ &:-& a sparse matrix. \\ 1619r,c &:-& positive integers such that ${\cal A}$(r,c) neq 0.\\ 1620row\_list &:-& positive integer or a list of positive integers. 1621\end{tabular} 1622 1623{\bf Synopsis:} 1624 1625\begin{addtolength}{\leftskip}{0.22in} 1626{\tt sprows\_pivot} performs the same task as {\tt sppivot} but applies 1627the pivot only to the rows specified in row\_list. 1628 1629\end{addtolength} 1630 1631{\bf Related functions:} 1632 1633\hspace*{0.175in} {\tt sppivot}. 1634 1635\subsection{sparsematp} 1636 1637\hspace*{0.175in} {\tt sparsematp(${\cal A}$);} 1638 1639\hspace*{0.1in} 1640\begin{tabular}{l l l} 1641${\cal A}$ &:-& a matrix. 1642\end{tabular} 1643 1644{\bf Synopsis:} 1645 1646\begin{addtolength}{\leftskip}{0.22in} 1647{\tt sparsematp} is a boolean function that returns t if 1648 the matrix is declared sparse and nil otherwise. 1649 1650\end{addtolength} 1651 1652{\bf Examples:} 1653 1654\begin{flushleft} 1655\hspace*{0.175in} 1656{\cal L}:= {\tt mat((1,2,3),(4,5,6),(7,8,9));} 1657\end{flushleft} 1658 1659\vspace*{0.1in} 1660 1661\hspace*{0.175in} {\tt sparsematp}(${\cal A}$) = t 1662 1663\hspace*{0.175in} {\tt sparsematp}(${\cal L}$) = nil 1664 1665{\bf Related functions:} 1666 1667\hspace*{0.175in} {\tt matrixp}, {\tt symmetricp}, {\tt squarep}. 1668 1669 1670\subsection{squarep} 1671 1672\hspace*{0.175in} {\tt squarep(${\cal A}$);} 1673 1674\hspace*{0.1in} 1675\begin{tabular}{l l l} 1676${\cal A}$ &:-& a matrix. 1677\end{tabular} 1678 1679{\bf Synopsis:} 1680 1681\begin{addtolength}{\leftskip}{0.22in} 1682{\tt squarep} is a boolean function that returns t if 1683 the matrix is square and nil otherwise. 1684 1685\end{addtolength} 1686 1687{\bf Examples:} 1688 1689\begin{flushleft} 1690\hspace*{0.175in} 1691\begin{math} 1692{\cal L} = \left( \begin{array}{ccc} 1 & 3 & 5 1693\end{array} \right) 1694\end{math} 1695\end{flushleft} 1696 1697\vspace*{0.1in} 1698 1699\hspace*{0.175in} {\tt squarep}(${\cal A}$) = t 1700 1701\hspace*{0.175in} {\tt squarep}(${\cal L}$) = nil 1702 1703{\bf Related functions:} 1704 1705\hspace*{0.175in} {\tt matrixp}, {\tt symmetricp}, {\tt sparsematp}. 1706 1707 1708\subsection{spstack\_rows} 1709 1710\hspace*{0.175in} see: {\tt spaugment\_columns}. 1711 1712 1713\subsection{spsub\_matrix} 1714 1715\hspace*{0.175in} {\tt spsub\_matrix(${\cal A}$,row\_list,column\_list);} 1716 1717\hspace*{0.1in} 1718\begin{tabular}{l l l} 1719${\cal A}$ &:-& a sparse matrix. \\ 1720row\_list, column\_list &:-& \parbox[t]{.605\linewidth}{either a 1721positive integer or a list of positive integers.} 1722\end{tabular} 1723 1724{\bf Synopsis:} 1725 1726 1727\begin{addtolength}{\leftskip}{0.22in} 1728 1729{\tt spsub\_matrix} produces the matrix consisting of the 1730 intersection of the rows specified in row\_list and the 1731columns specified in column\_list. 1732 1733\end{addtolength} 1734 1735{\bf Examples:} 1736 1737\begin{flushleft} 1738\hspace*{0.1in} 1739\begin{math} 1740\begin{array}{ccc} 1741{\tt spsub\_matrix}({\cal A},\{1,3\},\{2,3\}) & = & 1742 \left( \begin{array}{cc} 5 & 0\\ 0 & 9 1743 \end{array} \right) 1744\end{array} 1745\end{math} 1746\end{flushleft} 1747 1748{\bf Related functions:} 1749 1750\hspace*{0.175in} {\tt spaugment\_columns}, {\tt spstack\_rows}. 1751 1752 1753\subsection{spsvd (singular value decomposition)} 1754 1755\hspace*{0.175in} {\tt spsvd(${\cal A}$);} 1756 1757\hspace*{0.1in} 1758\begin{tabular}{l l l} 1759${\cal A}$ &:-& a sparse matrix containing only numeric entries. 1760\end{tabular} 1761 1762{\bf Synopsis:} %{\bf What it does:} 1763 1764\begin{addtolength}{\leftskip}{0.22in} 1765{\tt spsvd} computes the singular value decomposition of ${\cal A}$. 1766 1767It returns \{${\cal U},\sum,{\cal V}$\} where ${\cal A} = {\cal U} 1768\sum {\cal V}^T$ and $\sum = diag(\sigma_{1}, \ldots ,\sigma_{n}). \; 1769\sigma_{i}$ for $i= (1 \ldots n)$ are the singular values of ${\cal A}$. 1770 1771 1772n is the column dimension of ${\cal A}$. 1773 1774\end{addtolength} 1775 1776{\bf Examples:} 1777 1778\begin{flushleft} 1779\hspace*{0.175in} 1780\begin{math} 1781{\cal Q} = \left( \begin{array}{cc} 1 & 0 \\ 0 & 3 1782\end{array} \right) 1783\end{math} 1784\end{flushleft} 1785 1786\begin{eqnarray} 1787\hspace*{0.1in} 1788{\tt svd({\cal Q})} & = & 1789\left\{ 1790 \left( \begin{array}{cc} -1 & 0 \\ 0 & 0 \end{array} \right), 1791\left( \begin{array}{cc} 1.0 & 0 \\ 0 & 5.0 \end{array} \right), 1792\right. \nonumber \\ & & \left. \: \; 1793\, \left( \begin{array}{cc} -1 & 0 \\ 0 & -1 \end{array} \right) 1794\right\} \nonumber \end{eqnarray} 1795 1796\subsection{spswap\_columns, spswap\_rows} 1797 1798\hspace*{0.175in} {\tt spswap\_columns(${\cal A}$,c1,c2);} 1799 1800\hspace*{0.1in} 1801\begin{tabular}{l l l} 1802${\cal A}$ &:-& a sparse matrix. \\ 1803c1,c1 &:-& positive integers. 1804\end{tabular} 1805 1806{\bf Synopsis:} 1807 1808\hspace*{0.175in} 1809{\tt spswap\_columns} swaps column c1 of ${\cal A}$ with column c2. 1810 1811\hspace*{0.175in} {\tt spswap\_rows} performs the same task on 2 rows of 1812 ${\cal A}$. 1813 1814{\bf Examples:} 1815 1816\begin{flushleft} 1817\hspace*{0.1in} 1818\begin{math} 1819\begin{array}{ccc} 1820{\tt spswap\_columns}({\cal A},2,3) & = & 1821 \left( \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 0 & 5 \\ 0 & 9 & 0 1822 \end{array} \right) 1823\end{array} 1824\end{math} 1825\end{flushleft} 1826 1827{\bf Related functions:} 1828 1829\hspace*{0.175in} {\tt spswap\_entries}. 1830 1831 1832\subsection{swap\_entries} 1833 1834\hspace*{0.175in} {\tt spswap\_entries(${\cal A}$,\{r1,c1\},\{r2,c2\});} 1835 1836\hspace*{0.1in} 1837\begin{tabular}{l l l} 1838${\cal A}$ &:-& a sparse matrix. \\ 1839r1,c1,r2,c2 &:-& positive integers. 1840\end{tabular} 1841 1842{\bf Synopsis:} 1843 1844\hspace*{0.175in} {\tt spswap\_entries} swaps ${\cal A}$(r1,c1) with 1845 ${\cal A}$(r2,c2). 1846 1847{\bf Examples:} 1848 1849\begin{flushleft} 1850\hspace*{0.1in} 1851\begin{math} 1852\begin{array}{ccc} 1853{\tt spswap\_entries}({\cal A},\{1,1\},\{3,3\}) & = & 1854 \left( \begin{array}{ccc} 9 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 1 1855 \end{array} \right) 1856\end{array} 1857\end{math} 1858\end{flushleft} 1859 1860{\bf Related functions:} 1861 1862\hspace*{0.175in} {\tt spswap\_columns}, {\tt spswap\_rows}. 1863 1864 1865\subsection{spswap\_rows} 1866 1867\hspace*{0.175in} see: {\tt spswap\_columns}. 1868 1869 1870\subsection{symmetricp} 1871 1872%{\bf How to use it:} 1873 1874\hspace*{0.175in} {\tt symmetricp(${\cal A}$);} 1875 1876\hspace*{0.1in} 1877\begin{tabular}{l l l} 1878${\cal A}$ &:-& a matrix. 1879\end{tabular} 1880 1881{\bf Synopsis:} %{\bf What it does:} 1882 1883\begin{addtolength}{\leftskip}{0.22in} 1884{\tt symmetricp} is a boolean function that returns t if the 1885 matrix is symmetric and nil otherwise. 1886 1887\end{addtolength} 1888 1889{\bf Examples:} 1890 1891\begin{flushleft} 1892\hspace*{0.175in} 1893\begin{math} 1894{\cal M} = \left( \begin{array}{cc} 1 & 2 \\ 3 & 4 1895\end{array} \right) 1896\end{math} 1897\end{flushleft} 1898 1899\vspace*{0.1in} 1900 1901\hspace*{0.175in} {\tt symmetricp}(${\cal A}$) = t 1902 1903\hspace*{0.175in} {\tt symmetricp}(${\cal M}$) = nil 1904 1905{\bf Related functions:} 1906 1907\hspace*{0.175in} {\tt matrixp}, {\tt squarep}, {\tt sparsematp}. 1908 1909\section{Fast Linear Algebra} 1910 1911By turning the {\tt fast\_la} switch on, the speed of the following 1912functions will be increased: 1913 1914\begin{tabular}{l l l l} 1915spadd\_columns & spadd\_rows & spaugment\_columns & spcol\_dim \\ 1916spcopy\_into & spmake\_identity & spmatrix\_augment & spmatrix\_stack\\ 1917spminor & spmult\_column & spmult\_row & sppivot \\ 1918spremove\_columns & spremove\_rows & sprows\_pivot & squarep \\ 1919spstack\_rows & spsub\_matrix & spswap\_columns & spswap\_entries\\ 1920spswap\_rows & symmetricp 1921\end{tabular} 1922 1923The increase in speed will be insignificant unless you are making a 1924significant number(i.e: thousands) of calls. When using this switch, 1925error checking is minimised. This means that illegal input may give 1926strange error messages. Beware. 1927 1928\section{Acknowledgments} 1929This package is an extention of the code from the Linear Algebra Package 1930for \REDUCE{} by Matt Rebbeck[1]. 1931 1932The algorithms for {\tt spcholesky}, {\tt splu\_decom}, and {\tt spsvd} are 1933taken from the book Linear Algebra - J.H. Wilkinson \& C. Reinsch[3]. 1934 1935The {\tt spgram\_schmidt} code comes from Karin Gatermann's Symmetry 1936package[4] for {\REDUCE}. 1937 1938 1939\begin{thebibliography}{} 1940\bibitem{matt} Matt Rebbeck: A Linear Algebra Package for {\REDUCE}, ZIB 1941, Berlin. (1994) 1942\bibitem{Reduce} Anthony C. Hearn: {\REDUCE} User's Manual 3.6. 1943 RAND (1995) 1944\bibitem{WiRe} J. H. Wilkinson \& C. Reinsch: Linear Algebra 1945(volume II). Springer-Verlag (1971) 1946\bibitem{gat} Karin Gatermann: Symmetry: A {\REDUCE} package for the 1947computation of linear representations of groups. ZIB, Berlin. (1992) 1948\end{thebibliography} 1949 1950\end{document} 1951