Updated notebook

Author: gourav3017

examples/sparse_plus_low_rank.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# <center>Compressing Influence Matrix Using Sparse Plus Low Rank</center>\n",
     "\n",
-    "The dose influence matrix is a fundamental component in the optimization process, but its handling can introduce computational challenges. The original matrix, being dense, requires substantial computational resources to manage. Hence, PortPy, by default, loads a truncated version of the matrix. For more accurate dose calculations or customized sparsification, users have the option to load the dense influence matrix. This example will guide you through the following processes:\n",
+    "The dose influence matrix is a fundamental component in the optimization process, but its handling can introduce computational challenges. The original matrix, being dense, requires substantial computational resources to manage. Hence, sparse influence matrix is being used in planning acorss various TPS to avoid computational issue. But it often leads to sub-optimal solutions, thus comprimising accuracy and plan quality. Hence we introduce novel technique of compressing original dense matrix using sparse plus low rank technique\n",
     "\n",
     "1. Generating a plan utilizing the sparse matrix with naive threshold of 1% of max(A)\n",
     "2. Compressed planning using Sparse plus low rank method\n",
@@ -25,7 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9d19e81e",
+   "id": "e22f040d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2191b75f",
+   "id": "76d3e070",
    "metadata": {
     "scrolled": false
    },
@@ -291,10 +291,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16277fc6",
+   "id": "55164877",
    "metadata": {},
    "source": [
-    "Instead of optimizing the plan using sparse influence matrix $ S $ we optimize it using sparse plus low rank of small values of influence matrix $L = A-S$, which is given by,\n",
+    "Instead of optimizing the plan using sparse influence matrix $ S $ we optimize it using sparse plus low rank of small values of dense influence matrix $L = A-S$, which is given by,\n",
     "$\n",
     "\\mbox{Minimize}: f(S\\mathbf{x} + HW\\mathbf{x})\\\\\n",
     "\\mbox{s.t}\n",
@@ -327,7 +327,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "702fdd4f",
+   "id": "3192b1f6",
    "metadata": {
     "scrolled": false
    },
@@ -745,7 +745,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2e3ad747",
+   "id": "46a80e0a",
    "metadata": {},
    "outputs": [],
    "source": []