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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math.linear;
+
+
+
+/**
+ * Interface handling decomposition algorithms that can solve A &times; X = B.
+ * <p>Decomposition algorithms decompose an A matrix has a product of several specific
+ * matrices from which they can solve A &times; X = B in least squares sense: they find X
+ * such that ||A &times; X - B|| is minimal.</p>
+ * <p>Some solvers like {@link LUDecomposition} can only find the solution for
+ * square matrices and when the solution is an exact linear solution, i.e. when
+ * ||A &times; X - B|| is exactly 0. Other solvers can also find solutions
+ * with non-square matrix A and with non-null minimal norm. If an exact linear
+ * solution exists it is also the minimal norm solution.</p>
+ *
+ * @version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $
+ * @since 2.0
+ */
+public interface DecompositionSolver {
+
+    /** Solve the linear equation A &times; X = B for matrices A.
+     * <p>The A matrix is implicit, it is provided by the underlying
+     * decomposition algorithm.</p>
+     * @param b right-hand side of the equation A &times; X = B
+     * @return a vector X that minimizes the two norm of A &times; X - B
+     * @exception IllegalArgumentException if matrices dimensions don't match
+     * @exception InvalidMatrixException if decomposed matrix is singular
+     */
+    double[] solve(final double[] b)
+        throws IllegalArgumentException, InvalidMatrixException;
+
+    /** Solve the linear equation A &times; X = B for matrices A.
+     * <p>The A matrix is implicit, it is provided by the underlying
+     * decomposition algorithm.</p>
+     * @param b right-hand side of the equation A &times; X = B
+     * @return a vector X that minimizes the two norm of A &times; X - B
+     * @exception IllegalArgumentException if matrices dimensions don't match
+     * @exception InvalidMatrixException if decomposed matrix is singular
+     */
+    RealVector solve(final RealVector b)
+        throws IllegalArgumentException, InvalidMatrixException;
+
+    /** Solve the linear equation A &times; X = B for matrices A.
+     * <p>The A matrix is implicit, it is provided by the underlying
+     * decomposition algorithm.</p>
+     * @param b right-hand side of the equation A &times; X = B
+     * @return a matrix X that minimizes the two norm of A &times; X - B
+     * @exception IllegalArgumentException if matrices dimensions don't match
+     * @exception InvalidMatrixException if decomposed matrix is singular
+     */
+    RealMatrix solve(final RealMatrix b)
+        throws IllegalArgumentException, InvalidMatrixException;
+
+    /**
+     * Check if the decomposed matrix is non-singular.
+     * @return true if the decomposed matrix is non-singular
+     */
+    boolean isNonSingular();
+
+    /** Get the inverse (or pseudo-inverse) of the decomposed matrix.
+     * @return inverse matrix
+     * @throws InvalidMatrixException if decomposed matrix is singular
+     */
+    RealMatrix getInverse()
+        throws InvalidMatrixException;
+
+}