Raymond | dee0849 | 2015-04-02 10:43:13 -0700 | [diff] [blame] | 1 | /* |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more |
| 3 | * contributor license agreements. See the NOTICE file distributed with |
| 4 | * this work for additional information regarding copyright ownership. |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 |
| 6 | * (the "License"); you may not use this file except in compliance with |
| 7 | * the License. You may obtain a copy of the License at |
| 8 | * |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | * |
| 11 | * Unless required by applicable law or agreed to in writing, software |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | * See the License for the specific language governing permissions and |
| 15 | * limitations under the License. |
| 16 | */ |
| 17 | |
| 18 | package org.apache.commons.math.optimization.linear; |
| 19 | |
| 20 | import java.io.IOException; |
| 21 | import java.io.ObjectInputStream; |
| 22 | import java.io.ObjectOutputStream; |
| 23 | import java.io.Serializable; |
| 24 | import java.util.ArrayList; |
| 25 | import java.util.Collection; |
| 26 | import java.util.HashSet; |
| 27 | import java.util.List; |
| 28 | import java.util.Set; |
| 29 | |
| 30 | import org.apache.commons.math.linear.Array2DRowRealMatrix; |
| 31 | import org.apache.commons.math.linear.MatrixUtils; |
| 32 | import org.apache.commons.math.linear.RealMatrix; |
| 33 | import org.apache.commons.math.linear.RealVector; |
| 34 | import org.apache.commons.math.optimization.GoalType; |
| 35 | import org.apache.commons.math.optimization.RealPointValuePair; |
| 36 | import org.apache.commons.math.util.MathUtils; |
| 37 | |
| 38 | /** |
| 39 | * A tableau for use in the Simplex method. |
| 40 | * |
| 41 | * <p> |
| 42 | * Example: |
| 43 | * <pre> |
| 44 | * W | Z | x1 | x2 | x- | s1 | s2 | a1 | RHS |
| 45 | * --------------------------------------------------- |
| 46 | * -1 0 0 0 0 0 0 1 0 <= phase 1 objective |
| 47 | * 0 1 -15 -10 0 0 0 0 0 <= phase 2 objective |
| 48 | * 0 0 1 0 0 1 0 0 2 <= constraint 1 |
| 49 | * 0 0 0 1 0 0 1 0 3 <= constraint 2 |
| 50 | * 0 0 1 1 0 0 0 1 4 <= constraint 3 |
| 51 | * </pre> |
| 52 | * W: Phase 1 objective function</br> |
| 53 | * Z: Phase 2 objective function</br> |
| 54 | * x1 & x2: Decision variables</br> |
| 55 | * x-: Extra decision variable to allow for negative values</br> |
| 56 | * s1 & s2: Slack/Surplus variables</br> |
| 57 | * a1: Artificial variable</br> |
| 58 | * RHS: Right hand side</br> |
| 59 | * </p> |
| 60 | * @version $Revision: 922713 $ $Date: 2010-03-14 02:26:13 +0100 (dim. 14 mars 2010) $ |
| 61 | * @since 2.0 |
| 62 | */ |
| 63 | class SimplexTableau implements Serializable { |
| 64 | |
| 65 | /** Column label for negative vars. */ |
| 66 | private static final String NEGATIVE_VAR_COLUMN_LABEL = "x-"; |
| 67 | |
| 68 | /** Serializable version identifier. */ |
| 69 | private static final long serialVersionUID = -1369660067587938365L; |
| 70 | |
| 71 | /** Linear objective function. */ |
| 72 | private final LinearObjectiveFunction f; |
| 73 | |
| 74 | /** Linear constraints. */ |
| 75 | private final List<LinearConstraint> constraints; |
| 76 | |
| 77 | /** Whether to restrict the variables to non-negative values. */ |
| 78 | private final boolean restrictToNonNegative; |
| 79 | |
| 80 | /** The variables each column represents */ |
| 81 | private final List<String> columnLabels = new ArrayList<String>(); |
| 82 | |
| 83 | /** Simple tableau. */ |
| 84 | private transient RealMatrix tableau; |
| 85 | |
| 86 | /** Number of decision variables. */ |
| 87 | private final int numDecisionVariables; |
| 88 | |
| 89 | /** Number of slack variables. */ |
| 90 | private final int numSlackVariables; |
| 91 | |
| 92 | /** Number of artificial variables. */ |
| 93 | private int numArtificialVariables; |
| 94 | |
| 95 | /** Amount of error to accept in floating point comparisons. */ |
| 96 | private final double epsilon; |
| 97 | |
| 98 | /** |
| 99 | * Build a tableau for a linear problem. |
| 100 | * @param f linear objective function |
| 101 | * @param constraints linear constraints |
| 102 | * @param goalType type of optimization goal: either {@link GoalType#MAXIMIZE} |
| 103 | * or {@link GoalType#MINIMIZE} |
| 104 | * @param restrictToNonNegative whether to restrict the variables to non-negative values |
| 105 | * @param epsilon amount of error to accept in floating point comparisons |
| 106 | */ |
| 107 | SimplexTableau(final LinearObjectiveFunction f, |
| 108 | final Collection<LinearConstraint> constraints, |
| 109 | final GoalType goalType, final boolean restrictToNonNegative, |
| 110 | final double epsilon) { |
| 111 | this.f = f; |
| 112 | this.constraints = normalizeConstraints(constraints); |
| 113 | this.restrictToNonNegative = restrictToNonNegative; |
| 114 | this.epsilon = epsilon; |
| 115 | this.numDecisionVariables = f.getCoefficients().getDimension() + |
| 116 | (restrictToNonNegative ? 0 : 1); |
| 117 | this.numSlackVariables = getConstraintTypeCounts(Relationship.LEQ) + |
| 118 | getConstraintTypeCounts(Relationship.GEQ); |
| 119 | this.numArtificialVariables = getConstraintTypeCounts(Relationship.EQ) + |
| 120 | getConstraintTypeCounts(Relationship.GEQ); |
| 121 | this.tableau = createTableau(goalType == GoalType.MAXIMIZE); |
| 122 | initializeColumnLabels(); |
| 123 | } |
| 124 | |
| 125 | /** |
| 126 | * Initialize the labels for the columns. |
| 127 | */ |
| 128 | protected void initializeColumnLabels() { |
| 129 | if (getNumObjectiveFunctions() == 2) { |
| 130 | columnLabels.add("W"); |
| 131 | } |
| 132 | columnLabels.add("Z"); |
| 133 | for (int i = 0; i < getOriginalNumDecisionVariables(); i++) { |
| 134 | columnLabels.add("x" + i); |
| 135 | } |
| 136 | if (!restrictToNonNegative) { |
| 137 | columnLabels.add(NEGATIVE_VAR_COLUMN_LABEL); |
| 138 | } |
| 139 | for (int i = 0; i < getNumSlackVariables(); i++) { |
| 140 | columnLabels.add("s" + i); |
| 141 | } |
| 142 | for (int i = 0; i < getNumArtificialVariables(); i++) { |
| 143 | columnLabels.add("a" + i); |
| 144 | } |
| 145 | columnLabels.add("RHS"); |
| 146 | } |
| 147 | |
| 148 | /** |
| 149 | * Create the tableau by itself. |
| 150 | * @param maximize if true, goal is to maximize the objective function |
| 151 | * @return created tableau |
| 152 | */ |
| 153 | protected RealMatrix createTableau(final boolean maximize) { |
| 154 | |
| 155 | // create a matrix of the correct size |
| 156 | int width = numDecisionVariables + numSlackVariables + |
| 157 | numArtificialVariables + getNumObjectiveFunctions() + 1; // + 1 is for RHS |
| 158 | int height = constraints.size() + getNumObjectiveFunctions(); |
| 159 | Array2DRowRealMatrix matrix = new Array2DRowRealMatrix(height, width); |
| 160 | |
| 161 | // initialize the objective function rows |
| 162 | if (getNumObjectiveFunctions() == 2) { |
| 163 | matrix.setEntry(0, 0, -1); |
| 164 | } |
| 165 | int zIndex = (getNumObjectiveFunctions() == 1) ? 0 : 1; |
| 166 | matrix.setEntry(zIndex, zIndex, maximize ? 1 : -1); |
| 167 | RealVector objectiveCoefficients = |
| 168 | maximize ? f.getCoefficients().mapMultiply(-1) : f.getCoefficients(); |
| 169 | copyArray(objectiveCoefficients.getData(), matrix.getDataRef()[zIndex]); |
| 170 | matrix.setEntry(zIndex, width - 1, |
| 171 | maximize ? f.getConstantTerm() : -1 * f.getConstantTerm()); |
| 172 | |
| 173 | if (!restrictToNonNegative) { |
| 174 | matrix.setEntry(zIndex, getSlackVariableOffset() - 1, |
| 175 | getInvertedCoeffiecientSum(objectiveCoefficients)); |
| 176 | } |
| 177 | |
| 178 | // initialize the constraint rows |
| 179 | int slackVar = 0; |
| 180 | int artificialVar = 0; |
| 181 | for (int i = 0; i < constraints.size(); i++) { |
| 182 | LinearConstraint constraint = constraints.get(i); |
| 183 | int row = getNumObjectiveFunctions() + i; |
| 184 | |
| 185 | // decision variable coefficients |
| 186 | copyArray(constraint.getCoefficients().getData(), matrix.getDataRef()[row]); |
| 187 | |
| 188 | // x- |
| 189 | if (!restrictToNonNegative) { |
| 190 | matrix.setEntry(row, getSlackVariableOffset() - 1, |
| 191 | getInvertedCoeffiecientSum(constraint.getCoefficients())); |
| 192 | } |
| 193 | |
| 194 | // RHS |
| 195 | matrix.setEntry(row, width - 1, constraint.getValue()); |
| 196 | |
| 197 | // slack variables |
| 198 | if (constraint.getRelationship() == Relationship.LEQ) { |
| 199 | matrix.setEntry(row, getSlackVariableOffset() + slackVar++, 1); // slack |
| 200 | } else if (constraint.getRelationship() == Relationship.GEQ) { |
| 201 | matrix.setEntry(row, getSlackVariableOffset() + slackVar++, -1); // excess |
| 202 | } |
| 203 | |
| 204 | // artificial variables |
| 205 | if ((constraint.getRelationship() == Relationship.EQ) || |
| 206 | (constraint.getRelationship() == Relationship.GEQ)) { |
| 207 | matrix.setEntry(0, getArtificialVariableOffset() + artificialVar, 1); |
| 208 | matrix.setEntry(row, getArtificialVariableOffset() + artificialVar++, 1); |
| 209 | matrix.setRowVector(0, matrix.getRowVector(0).subtract(matrix.getRowVector(row))); |
| 210 | } |
| 211 | } |
| 212 | |
| 213 | return matrix; |
| 214 | } |
| 215 | |
| 216 | /** |
| 217 | * Get new versions of the constraints which have positive right hand sides. |
| 218 | * @param originalConstraints original (not normalized) constraints |
| 219 | * @return new versions of the constraints |
| 220 | */ |
| 221 | public List<LinearConstraint> normalizeConstraints(Collection<LinearConstraint> originalConstraints) { |
| 222 | List<LinearConstraint> normalized = new ArrayList<LinearConstraint>(); |
| 223 | for (LinearConstraint constraint : originalConstraints) { |
| 224 | normalized.add(normalize(constraint)); |
| 225 | } |
| 226 | return normalized; |
| 227 | } |
| 228 | |
| 229 | /** |
| 230 | * Get a new equation equivalent to this one with a positive right hand side. |
| 231 | * @param constraint reference constraint |
| 232 | * @return new equation |
| 233 | */ |
| 234 | private LinearConstraint normalize(final LinearConstraint constraint) { |
| 235 | if (constraint.getValue() < 0) { |
| 236 | return new LinearConstraint(constraint.getCoefficients().mapMultiply(-1), |
| 237 | constraint.getRelationship().oppositeRelationship(), |
| 238 | -1 * constraint.getValue()); |
| 239 | } |
| 240 | return new LinearConstraint(constraint.getCoefficients(), |
| 241 | constraint.getRelationship(), constraint.getValue()); |
| 242 | } |
| 243 | |
| 244 | /** |
| 245 | * Get the number of objective functions in this tableau. |
| 246 | * @return 2 for Phase 1. 1 for Phase 2. |
| 247 | */ |
| 248 | protected final int getNumObjectiveFunctions() { |
| 249 | return this.numArtificialVariables > 0 ? 2 : 1; |
| 250 | } |
| 251 | |
| 252 | /** |
| 253 | * Get a count of constraints corresponding to a specified relationship. |
| 254 | * @param relationship relationship to count |
| 255 | * @return number of constraint with the specified relationship |
| 256 | */ |
| 257 | private int getConstraintTypeCounts(final Relationship relationship) { |
| 258 | int count = 0; |
| 259 | for (final LinearConstraint constraint : constraints) { |
| 260 | if (constraint.getRelationship() == relationship) { |
| 261 | ++count; |
| 262 | } |
| 263 | } |
| 264 | return count; |
| 265 | } |
| 266 | |
| 267 | /** |
| 268 | * Get the -1 times the sum of all coefficients in the given array. |
| 269 | * @param coefficients coefficients to sum |
| 270 | * @return the -1 times the sum of all coefficients in the given array. |
| 271 | */ |
| 272 | protected static double getInvertedCoeffiecientSum(final RealVector coefficients) { |
| 273 | double sum = 0; |
| 274 | for (double coefficient : coefficients.getData()) { |
| 275 | sum -= coefficient; |
| 276 | } |
| 277 | return sum; |
| 278 | } |
| 279 | |
| 280 | /** |
| 281 | * Checks whether the given column is basic. |
| 282 | * @param col index of the column to check |
| 283 | * @return the row that the variable is basic in. null if the column is not basic |
| 284 | */ |
| 285 | protected Integer getBasicRow(final int col) { |
| 286 | Integer row = null; |
| 287 | for (int i = 0; i < getHeight(); i++) { |
| 288 | if (MathUtils.equals(getEntry(i, col), 1.0, epsilon) && (row == null)) { |
| 289 | row = i; |
| 290 | } else if (!MathUtils.equals(getEntry(i, col), 0.0, epsilon)) { |
| 291 | return null; |
| 292 | } |
| 293 | } |
| 294 | return row; |
| 295 | } |
| 296 | |
| 297 | /** |
| 298 | * Removes the phase 1 objective function, positive cost non-artificial variables, |
| 299 | * and the non-basic artificial variables from this tableau. |
| 300 | */ |
| 301 | protected void dropPhase1Objective() { |
| 302 | if (getNumObjectiveFunctions() == 1) { |
| 303 | return; |
| 304 | } |
| 305 | |
| 306 | List<Integer> columnsToDrop = new ArrayList<Integer>(); |
| 307 | columnsToDrop.add(0); |
| 308 | |
| 309 | // positive cost non-artificial variables |
| 310 | for (int i = getNumObjectiveFunctions(); i < getArtificialVariableOffset(); i++) { |
| 311 | if (MathUtils.compareTo(tableau.getEntry(0, i), 0, epsilon) > 0) { |
| 312 | columnsToDrop.add(i); |
| 313 | } |
| 314 | } |
| 315 | |
| 316 | // non-basic artificial variables |
| 317 | for (int i = 0; i < getNumArtificialVariables(); i++) { |
| 318 | int col = i + getArtificialVariableOffset(); |
| 319 | if (getBasicRow(col) == null) { |
| 320 | columnsToDrop.add(col); |
| 321 | } |
| 322 | } |
| 323 | |
| 324 | double[][] matrix = new double[getHeight() - 1][getWidth() - columnsToDrop.size()]; |
| 325 | for (int i = 1; i < getHeight(); i++) { |
| 326 | int col = 0; |
| 327 | for (int j = 0; j < getWidth(); j++) { |
| 328 | if (!columnsToDrop.contains(j)) { |
| 329 | matrix[i - 1][col++] = tableau.getEntry(i, j); |
| 330 | } |
| 331 | } |
| 332 | } |
| 333 | |
| 334 | for (int i = columnsToDrop.size() - 1; i >= 0; i--) { |
| 335 | columnLabels.remove((int) columnsToDrop.get(i)); |
| 336 | } |
| 337 | |
| 338 | this.tableau = new Array2DRowRealMatrix(matrix); |
| 339 | this.numArtificialVariables = 0; |
| 340 | } |
| 341 | |
| 342 | /** |
| 343 | * @param src the source array |
| 344 | * @param dest the destination array |
| 345 | */ |
| 346 | private void copyArray(final double[] src, final double[] dest) { |
| 347 | System.arraycopy(src, 0, dest, getNumObjectiveFunctions(), src.length); |
| 348 | } |
| 349 | |
| 350 | /** |
| 351 | * Returns whether the problem is at an optimal state. |
| 352 | * @return whether the model has been solved |
| 353 | */ |
| 354 | boolean isOptimal() { |
| 355 | for (int i = getNumObjectiveFunctions(); i < getWidth() - 1; i++) { |
| 356 | if (MathUtils.compareTo(tableau.getEntry(0, i), 0, epsilon) < 0) { |
| 357 | return false; |
| 358 | } |
| 359 | } |
| 360 | return true; |
| 361 | } |
| 362 | |
| 363 | /** |
| 364 | * Get the current solution. |
| 365 | * |
| 366 | * @return current solution |
| 367 | */ |
| 368 | protected RealPointValuePair getSolution() { |
| 369 | int negativeVarColumn = columnLabels.indexOf(NEGATIVE_VAR_COLUMN_LABEL); |
| 370 | Integer negativeVarBasicRow = negativeVarColumn > 0 ? getBasicRow(negativeVarColumn) : null; |
| 371 | double mostNegative = negativeVarBasicRow == null ? 0 : getEntry(negativeVarBasicRow, getRhsOffset()); |
| 372 | |
| 373 | Set<Integer> basicRows = new HashSet<Integer>(); |
| 374 | double[] coefficients = new double[getOriginalNumDecisionVariables()]; |
| 375 | for (int i = 0; i < coefficients.length; i++) { |
| 376 | int colIndex = columnLabels.indexOf("x" + i); |
| 377 | if (colIndex < 0) { |
| 378 | coefficients[i] = 0; |
| 379 | continue; |
| 380 | } |
| 381 | Integer basicRow = getBasicRow(colIndex); |
| 382 | if (basicRows.contains(basicRow)) { |
| 383 | // if multiple variables can take a given value |
| 384 | // then we choose the first and set the rest equal to 0 |
| 385 | coefficients[i] = 0; |
| 386 | } else { |
| 387 | basicRows.add(basicRow); |
| 388 | coefficients[i] = |
| 389 | (basicRow == null ? 0 : getEntry(basicRow, getRhsOffset())) - |
| 390 | (restrictToNonNegative ? 0 : mostNegative); |
| 391 | } |
| 392 | } |
| 393 | return new RealPointValuePair(coefficients, f.getValue(coefficients)); |
| 394 | } |
| 395 | |
| 396 | /** |
| 397 | * Subtracts a multiple of one row from another. |
| 398 | * <p> |
| 399 | * After application of this operation, the following will hold: |
| 400 | * minuendRow = minuendRow - multiple * subtrahendRow |
| 401 | * </p> |
| 402 | * @param dividendRow index of the row |
| 403 | * @param divisor value of the divisor |
| 404 | */ |
| 405 | protected void divideRow(final int dividendRow, final double divisor) { |
| 406 | for (int j = 0; j < getWidth(); j++) { |
| 407 | tableau.setEntry(dividendRow, j, tableau.getEntry(dividendRow, j) / divisor); |
| 408 | } |
| 409 | } |
| 410 | |
| 411 | /** |
| 412 | * Subtracts a multiple of one row from another. |
| 413 | * <p> |
| 414 | * After application of this operation, the following will hold: |
| 415 | * minuendRow = minuendRow - multiple * subtrahendRow |
| 416 | * </p> |
| 417 | * @param minuendRow row index |
| 418 | * @param subtrahendRow row index |
| 419 | * @param multiple multiplication factor |
| 420 | */ |
| 421 | protected void subtractRow(final int minuendRow, final int subtrahendRow, |
| 422 | final double multiple) { |
| 423 | tableau.setRowVector(minuendRow, tableau.getRowVector(minuendRow) |
| 424 | .subtract(tableau.getRowVector(subtrahendRow).mapMultiply(multiple))); |
| 425 | } |
| 426 | |
| 427 | /** |
| 428 | * Get the width of the tableau. |
| 429 | * @return width of the tableau |
| 430 | */ |
| 431 | protected final int getWidth() { |
| 432 | return tableau.getColumnDimension(); |
| 433 | } |
| 434 | |
| 435 | /** |
| 436 | * Get the height of the tableau. |
| 437 | * @return height of the tableau |
| 438 | */ |
| 439 | protected final int getHeight() { |
| 440 | return tableau.getRowDimension(); |
| 441 | } |
| 442 | |
| 443 | /** Get an entry of the tableau. |
| 444 | * @param row row index |
| 445 | * @param column column index |
| 446 | * @return entry at (row, column) |
| 447 | */ |
| 448 | protected final double getEntry(final int row, final int column) { |
| 449 | return tableau.getEntry(row, column); |
| 450 | } |
| 451 | |
| 452 | /** Set an entry of the tableau. |
| 453 | * @param row row index |
| 454 | * @param column column index |
| 455 | * @param value for the entry |
| 456 | */ |
| 457 | protected final void setEntry(final int row, final int column, |
| 458 | final double value) { |
| 459 | tableau.setEntry(row, column, value); |
| 460 | } |
| 461 | |
| 462 | /** |
| 463 | * Get the offset of the first slack variable. |
| 464 | * @return offset of the first slack variable |
| 465 | */ |
| 466 | protected final int getSlackVariableOffset() { |
| 467 | return getNumObjectiveFunctions() + numDecisionVariables; |
| 468 | } |
| 469 | |
| 470 | /** |
| 471 | * Get the offset of the first artificial variable. |
| 472 | * @return offset of the first artificial variable |
| 473 | */ |
| 474 | protected final int getArtificialVariableOffset() { |
| 475 | return getNumObjectiveFunctions() + numDecisionVariables + numSlackVariables; |
| 476 | } |
| 477 | |
| 478 | /** |
| 479 | * Get the offset of the right hand side. |
| 480 | * @return offset of the right hand side |
| 481 | */ |
| 482 | protected final int getRhsOffset() { |
| 483 | return getWidth() - 1; |
| 484 | } |
| 485 | |
| 486 | /** |
| 487 | * Get the number of decision variables. |
| 488 | * <p> |
| 489 | * If variables are not restricted to positive values, this will include 1 |
| 490 | * extra decision variable to represent the absolute value of the most |
| 491 | * negative variable. |
| 492 | * </p> |
| 493 | * @return number of decision variables |
| 494 | * @see #getOriginalNumDecisionVariables() |
| 495 | */ |
| 496 | protected final int getNumDecisionVariables() { |
| 497 | return numDecisionVariables; |
| 498 | } |
| 499 | |
| 500 | /** |
| 501 | * Get the original number of decision variables. |
| 502 | * @return original number of decision variables |
| 503 | * @see #getNumDecisionVariables() |
| 504 | */ |
| 505 | protected final int getOriginalNumDecisionVariables() { |
| 506 | return f.getCoefficients().getDimension(); |
| 507 | } |
| 508 | |
| 509 | /** |
| 510 | * Get the number of slack variables. |
| 511 | * @return number of slack variables |
| 512 | */ |
| 513 | protected final int getNumSlackVariables() { |
| 514 | return numSlackVariables; |
| 515 | } |
| 516 | |
| 517 | /** |
| 518 | * Get the number of artificial variables. |
| 519 | * @return number of artificial variables |
| 520 | */ |
| 521 | protected final int getNumArtificialVariables() { |
| 522 | return numArtificialVariables; |
| 523 | } |
| 524 | |
| 525 | /** |
| 526 | * Get the tableau data. |
| 527 | * @return tableau data |
| 528 | */ |
| 529 | protected final double[][] getData() { |
| 530 | return tableau.getData(); |
| 531 | } |
| 532 | |
| 533 | /** {@inheritDoc} */ |
| 534 | @Override |
| 535 | public boolean equals(Object other) { |
| 536 | |
| 537 | if (this == other) { |
| 538 | return true; |
| 539 | } |
| 540 | |
| 541 | if (other instanceof SimplexTableau) { |
| 542 | SimplexTableau rhs = (SimplexTableau) other; |
| 543 | return (restrictToNonNegative == rhs.restrictToNonNegative) && |
| 544 | (numDecisionVariables == rhs.numDecisionVariables) && |
| 545 | (numSlackVariables == rhs.numSlackVariables) && |
| 546 | (numArtificialVariables == rhs.numArtificialVariables) && |
| 547 | (epsilon == rhs.epsilon) && |
| 548 | f.equals(rhs.f) && |
| 549 | constraints.equals(rhs.constraints) && |
| 550 | tableau.equals(rhs.tableau); |
| 551 | } |
| 552 | return false; |
| 553 | } |
| 554 | |
| 555 | /** {@inheritDoc} */ |
| 556 | @Override |
| 557 | public int hashCode() { |
| 558 | return Boolean.valueOf(restrictToNonNegative).hashCode() ^ |
| 559 | numDecisionVariables ^ |
| 560 | numSlackVariables ^ |
| 561 | numArtificialVariables ^ |
| 562 | Double.valueOf(epsilon).hashCode() ^ |
| 563 | f.hashCode() ^ |
| 564 | constraints.hashCode() ^ |
| 565 | tableau.hashCode(); |
| 566 | } |
| 567 | |
| 568 | /** Serialize the instance. |
| 569 | * @param oos stream where object should be written |
| 570 | * @throws IOException if object cannot be written to stream |
| 571 | */ |
| 572 | private void writeObject(ObjectOutputStream oos) |
| 573 | throws IOException { |
| 574 | oos.defaultWriteObject(); |
| 575 | MatrixUtils.serializeRealMatrix(tableau, oos); |
| 576 | } |
| 577 | |
| 578 | /** Deserialize the instance. |
| 579 | * @param ois stream from which the object should be read |
| 580 | * @throws ClassNotFoundException if a class in the stream cannot be found |
| 581 | * @throws IOException if object cannot be read from the stream |
| 582 | */ |
| 583 | private void readObject(ObjectInputStream ois) |
| 584 | throws ClassNotFoundException, IOException { |
| 585 | ois.defaultReadObject(); |
| 586 | MatrixUtils.deserializeRealMatrix(this, "tableau", ois); |
| 587 | } |
| 588 | |
| 589 | } |