Mercurial > octave-dspies
view doc/interpreter/optim.txi @ 19010:3fb030666878 draft default tip dspies
Added special-case logical-indexing function
* logical-index.h (New file) : Logical-indexing function. May be called on
octave_value types via call_bool_index
* nz-iterators.h : Add base-class nz_iterator for iterator types. Array has
template bool for whether to internally store row-col or compute on the fly
Add skip_ahead method which skips forward to the next nonzero after its
argument
Add flat_index for computing octave_idx_type index of current position (with
assertion failure in the case of overflow)
Move is_zero to separate file
* ov-base-diag.cc, ov-base-mat.cc, ov-base-sparse.cc, ov-perm.cc
(do_index_op): Add call to call_bool_index in logical-index.h
* Array.h : Move forward-declaration for array_iterator to separate header file
* dim-vector.cc (dim_max): Refers to idx-bounds.h (max_idx)
* array-iter-decl.h (New file): Header file for forward declaration of
array-iterator
* direction.h : Add constants fdirc and bdirc to avoid having to reconstruct
them
* dv-utils.h, dv-utils.cc (New files) :
Utility functions for querying and constructing dim-vectors
* idx-bounds.h (New file) :
Utility constants and functions for determining whether things will overflow
the maximum allowed bounds
* interp-idx.h (New function : to_flat_idx) : Converts row-col pair to linear
index of octave_idx_type
* is-zero.h (New file) : Function for determining whether an element is zero
* logical-index.tst : Add tests for correct return-value dimensions and large
sparse matrix behavior
author | David Spies <dnspies@gmail.com> |
---|---|
date | Fri, 25 Jul 2014 13:39:31 -0600 |
parents | d63878346099 |
children |
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@c Copyright (C) 1996-2013 John W. Eaton @c @c This file is part of Octave. @c @c Octave is free software; you can redistribute it and/or modify it @c under the terms of the GNU General Public License as published by the @c Free Software Foundation; either version 3 of the License, or (at @c your option) any later version. @c @c Octave is distributed in the hope that it will be useful, but WITHOUT @c ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or @c FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License @c for more details. @c @c You should have received a copy of the GNU General Public License @c along with Octave; see the file COPYING. If not, see @c <http://www.gnu.org/licenses/>. @node Optimization @chapter Optimization Octave comes with support for solving various kinds of optimization problems. Specifically Octave can solve problems in Linear Programming, Quadratic Programming, Nonlinear Programming, and Linear Least Squares Minimization. @menu * Linear Programming:: * Quadratic Programming:: * Nonlinear Programming:: * Linear Least Squares:: @end menu @c @cindex linear programming @cindex quadratic programming @cindex nonlinear programming @cindex optimization @cindex LP @cindex QP @cindex NLP @node Linear Programming @section Linear Programming Octave can solve Linear Programming problems using the @code{glpk} function. That is, Octave can solve @tex $$ \min_x c^T x $$ @end tex @ifnottex @example min C'*x @end example @end ifnottex subject to the linear constraints @tex $Ax = b$ where $x \geq 0$. @end tex @ifnottex @math{A*x = b} where @math{x @geq{} 0}. @end ifnottex @noindent The @code{glpk} function also supports variations of this problem. @DOCSTRING(glpk) @node Quadratic Programming @section Quadratic Programming Octave can also solve Quadratic Programming problems, this is @tex $$ \min_x {1 \over 2} x^T H x + x^T q $$ @end tex @ifnottex @example min 0.5 x'*H*x + x'*q @end example @end ifnottex subject to @tex $$ Ax = b \qquad lb \leq x \leq ub \qquad A_{lb} \leq A_{in} \leq A_{ub} $$ @end tex @ifnottex @example @group A*x = b lb <= x <= ub A_lb <= A_in*x <= A_ub @end group @end example @end ifnottex @DOCSTRING(qp) @DOCSTRING(pqpnonneg) @node Nonlinear Programming @section Nonlinear Programming Octave can also perform general nonlinear minimization using a successive quadratic programming solver. @DOCSTRING(sqp) @node Linear Least Squares @section Linear Least Squares Octave also supports linear least squares minimization. That is, Octave can find the parameter @math{b} such that the model @tex $y = xb$ @end tex @ifnottex @math{y = x*b} @end ifnottex fits data @math{(x,y)} as well as possible, assuming zero-mean Gaussian noise. If the noise is assumed to be isotropic the problem can be solved using the @samp{\} or @samp{/} operators, or the @code{ols} function. In the general case where the noise is assumed to be anisotropic the @code{gls} is needed. @DOCSTRING(ols) @DOCSTRING(gls) @DOCSTRING(lsqnonneg) @DOCSTRING(optimset) @DOCSTRING(optimget)