Add philosophy section to the docs

docs/intro.rst

@@ -6,25 +6,65 @@
 Introduction
 ============
 
-`Nonlinear programming (NLP) <https://en.wikipedia.org/wiki/Nonlinear_programming>`_ 
-is the process of solving an optimization problem where some of the constraints 
-are not linear equalities and/or the objective function is not a linear function. 
-Such optimization problems are pervasive in business and logistics: inventory 
-management, scheduling employees, equipment delivery, and many more. 
+`dwave-optimization` enables you to formulate optimization models for workforce
+scheduling, production scheduling, resource allocation, logistics routing, and
+many more.
+The model can then be submitted to the
+`Leap <https://cloud.dwavesys.com/leap/>`_\ |TM| service's quantum-classical 
+hybrid nonlinear solver to find good solutions.
+
+Features
+========
+
+.. toctree::
+  :maxdepth: 1
+  :hidden:
+
+  philosophy
+
+`dwave-optimization` and the hybrid nonlinear solver incorporate features and
+design principals from each of the following areas:
+
+.. grid:: 2
+    :gutter: 3
+
+    .. grid-item-card:: Quantum Optimization
+        :class-body: sd-outline-warning
+        :link: optimization_philosophy_quantum_optimization
+        :link-type: ref
+
+        Take advantage of quantum-mechanical effects not available to classical
+        compute.
+
+    .. grid-item-card:: (Mixed-Integer) Linear Programming
+        :class-body: sd-outline-info
+        :link: optimization_philosophy_linear_programming
+        :link-type: ref
+
+        Learn the basics of solving optimization problems with linear program
+        solvers.
+
+    .. grid-item-card:: Lists, Sets, and other combinatorial variables
+        :class-body: sd-outline-info
+        :link: optimization_philosophy_constraint_programming
+        :link-type: ref
+
+        Explore how lists, sets, and other combinatorial structures make
+        optimization simpler and more performant.
+
+    .. grid-item-card:: Tensor Programming
+        :class-body: sd-outline-info
+        :link: optimization_philosophy_tensor_programming
+        :link-type: ref
+
+        Use N-dimensional arrays and operations to work with your data directly
+        and succinctly.
 
 .. _intro_optimization_nonlinear_models: 
 
 Nonlinear Models
 ================
 
-`dwave-optimization` enables you to formulate the nonlinear models needed for 
-such industrial optimization problems. The model can then be submitted to the
-`Leap <https://cloud.dwavesys.com/leap/>`_\ |TM| service's quantum-classical 
-hybrid nonlinear-program solver to find good solutions.
-
-Examples of problems suited to such solution methods are resource routing,
-scheduling, allocation, and job-shop scheduling.  
-
 Successful implementation, as for any solver, requires following some 
 :ref:`best practices <intro_optimization_usage_guidelines>` in formulating 
 your model.  

docs/philosophy.rst

@@ -0,0 +1,63 @@
+.. _optimization_philosophy:
+
+.. |TM| replace:: :sup:`TM`
+
+==========
+Philosophy
+==========
+
+`dwave-optimization` and
+the `Leap <https://cloud.dwavesys.com/leap/>`_\ |TM| service's quantum-classical 
+hybrid nonlinear solver
+incorporate features and design principals from each of the following areas:
+
+.. _optimization_philosophy_quantum_optimization:
+
+Quantum Optimization
+====================
+
+:term:`Quantum computing` has the potential to help solve some of the most complex
+technical, scientific, national defense, and commercial problems that
+organizations face.
+
+:term:`Quantum annealing` processors naturally return low-energy solutions.
+A fundamental rule of physics is that everything tends to seek a minimum energy state.
+Objects slide down hills; hot things cool down over time.
+This behavior is also true in the world of quantum physics.
+Quantum annealing simply uses quantum physics to find low-energy states of a
+problem and therefore the optimal or near-optimal combination of elements.
+
+To learn more about solving optimization problems with quantum computers, see the
+`Problem Solving Handbook <https://docs.dwavesys.com/docs/latest/doc_handbook.html>`_.
+
+.. _optimization_philosophy_linear_programming:
+
+(Mixed-Integer) Linear Programming
+==================================
+
+Much of the mathematical optimization done today is done with
+`linear programming <https://en.wikipedia.org/wiki/Linear_programming>`_
+solvers and techniques.
+
+Users familiar with linear programming techniques will find many concepts in
+`dwave-optimization` familiar: objective functions; continuous, linear, and binary variables;
+constraints; and feasible regions are all concepts found in linear programming.
+
+However, unlike other linear solvers on the market, `dwave-optimization` allows
+`nonlinear <https://en.wikipedia.org/wiki/Nonlinear_programming>`_ relationships
+among variables, constraints, and the objective function.
+
+.. _optimization_philosophy_constraint_programming:
+
+Lists, Sets, and other combinatorial variables
+==============================================
+
+Lorem ipsum
+
+.. _optimization_philosophy_tensor_programming:
+
+Tensor Programming
+==================
+
+Lorem ipsum
+

docs/requirements.txt

@@ -1,5 +1,5 @@
 pydata-sphinx-theme==0.14.3
 sphinx==7.3.7
-sphinx-design==0.5.0
+sphinx-design==0.6.1
 breathe==4.35.0
 reno[sphinx]==4.1.0  # keep synced with requirements.txt