danjcalderone

Topical-Index, Alphabetic-Index,

-A-

• Adjacency matrices: graph(slides),
• Affine spaces: affine(slides),
• Algebraic graph theory: graphflows(mono), graph(slides), treescycles(blog),
• Algorithms: algos(slides),
• Arrays: arrays(slides),

-B-

• Bases: bases(web),
• Bellman-Ford (algorithm): algos(slides),
• Bipartite Matching bipartite(slides),
• Block-matrix: block(slides), block(web),

-C-

• Characteristic polynomial: eig(slides),
• Circuits: circuits(slides),
• Classification: classify(slides),
• Column geometry: cols(mono), colrow(slides), cols(slides), fundthm(slides), colrows(web), eig(slides),
• Combinations - linear: combs(lec), lincombs(web),
• Combinations - positive: combs(lec),
• Combinations - convex: combs(lec), cvxcombs(web),
• Complex Numbers: complex(lec),
• Complex Eigenvalues: eig(slides),
• Congruent Transforms: cong(web),
• Controllability: ltisys(slides), ctrlobs(slides),
• Coordinates: coords(lec), coords(web),
• Cycles (graphs): graph(slides), treescycles(blog),

-D-

• Derivatives: derivs(lec), derivs(web),
• Diagonalization: eig(slides),
• Dijkstra's algorithm: algos(slides),
• Discrete Fourier Transforms: dft(slides),
• Disturbance Rejection: disturbs(slides),
• Drawing: drawing(slides),
• Duality: duals(slides),

-E-

• Eigenvalues/Eigenvectors: eig(slides),
• Elementary matrices: gerows(slides), gecols(slides),
• Exponential (matrix) eig(slides),

-F-

• Floyd-Walsh (algorithm): algos(slides),
• Fund-Thm (linear algebra): graph(slides), treescycles(blog),

-G-

• Graphs: graph(slides),
• Gaussian Elimination: gausselim(web),
• Grammians: graph(slides),

-H-

• Heap sort (algorithm): algos(slides),
• High dimensional inner products: highdim(slides),

-I-

• Incidence matrices: graph(slides), treescycles(blog),
• Inner products: matprod(lec), inner(lec), inner(web), outer(web),
• Integration by Parts: calc(slides),
• Inverses: invs(lec), invs(web), inv(ref),

-K-

• Kadane's algorithm: algos(slides),
• Kahn's topological sort (algorithm): algos(slides),
• Kinematics: kinematics(slides),
• Kalman filters: kfvis(slides), kf(ref), kfexamples(ipynb),
• KKT Conditions kktmat(slides),
• KKT Sensitivity kktsens(slides),
• KMP algorithm: algos(slides),

-L-

• Laplacians: graph(slides),
• Left-Right Inverses: lfinvs(web),
• Leibniz Integral Rule: calc(slides),
• Linear Algebra: linalg(lec),
• Linear programs (LPs): duals(slides), bipartite(slides),
• Linear systems: ltisys(slides),
• Linear transformations: lintrans(web),
• Linearization: lin(slides),

-M-

• Markov decision processes: mdps(mono), mdps(slides),
• Matrices (2x2): 2x2(mono), eig(slides),
• Matrices (3x3): eig(slides),
• Matrix Addition: matadd(slides), matadd(web),
• Matrix Multiplication: matmult(slides), matmult(web), block(slides), block(web),
• Matrix Norms: matnorms(web),
• Mechanics: dynamics(slides),
• Merge sort (algorithm): algos(slides),

-N-

• Nullspace: nullspace(web), affine(slides),
• Norms: matprod(lec), norms(web),

-O-

• Observability: ltisys(slides), ctrlobs(slides),
• Ordinary Differential Eqns (ODEs): odes(lec),
• Orthonormal transformations: ortho(web),
• Orthonormalization: orthonormal(web),

-P-

• Permutations: eig(slides),
• Polar decomposition: decomps(lec), svd(slides), graph(slides),
• Polynomial (matrices): eig(slides),
• Polytopes: polytope(mono),
• Pos & Neg (semi-)definite: posdef(slides), svd(slides),
• Product Rule: calc(slides),
• Projections: projs(lec), projs(web),
• Pseudo-inverses: pinvs(web),
• Python: python(slides),

-Q-

• Quadratic forms: posdef(slides),
• Quadratic programs (QPs): duals(slides),
• Quick sort (algorithm): algos(slides),

-R-

• Range space: range(web),
• Rank/Rank-Nullity: ranknull(web),
• Regression: regress(slides),
• Rotations: rotations(lec), rotations(slides),
• Row geometry: colrow(slides), eig(slides),

-S-

• Second order cone programs (SOCPs): duals(slides),
• Semi-definite programs (SDPs): duals(slides),
• Similarity Transforms: sim(web), eig(slides),
• Simplex Method simplex(ipynb), simplex(ex), simplex(blog), simplexroute(blog),
• Singular Value Decomposition: decomps(lec), svd(slides), graph(slides),
• Span: span(lec),
• Spectral mapping theorem: eig(slides),
• Successive Convexification (Trajectory Planning): scvx(blog),

-T-

• Transposes: transpose(web),
• Trees-spanning (graphs): graph(slides), treescycles(blog),

-U-

• Uncertainty modeling: lfruncertainty(slides),

-V-

• Vectors/Vector sets: vecs(mono), vecs(web), vecsets(web), vecadd(web),