Concepts index

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	Index Entry	Section

$
	‘`$dimetaphi`’	2.3.1 Setup a model and compile with cmz
	‘`$zinit`’	2.3.1 Setup a model and compile with cmz

A
	adjoint	4.2.1 Overview of optimisation with Miniker
	‘`aspha.data`’	2.3.3 Running a simulation and using the output
	‘`aspha.data`’, GTLS	4.6.3 Generalized tangent linear system analysis run and output
	‘`aspha.data`’, SVD	4.5.3 Singular Value Decomposition run and output

B
	Borel sweep	4.4 Feedback gain
	Borel sweep graphics	4.4.2 Borel sweep results
	Borel sweep results	4.4.2 Borel sweep results

C
	cells	Introduction
	cernlib	A.2 Common requisites
	command law	4.2.2 Control laws
	compilation	2.3.2 Setup a model and compile with make
	configuration of source	A.4.2 Configuration
	controlling the run	2.4 Controlling the run

D
	‘`data.data`’	3.8.2 Data
	‘`data.data`’	4.3.2.2 Kalman filter results
	dimetaphi	3.9.1 The explicit size sequence
	‘`dimetaphi`’	3.9.1 The explicit size sequence
	‘`dimetaphi`’, Kalman filter	4.3.1.1 Kalman filter vectors dimensions
	down node	3.3 Describing 1D gridded model
	‘`dres.data`’	2.3.3 Running a simulation and using the output
	‘`dres.data`’
	‘`dres.data`’, GTLS	4.6.3 Generalized tangent linear system analysis run and output

E
	equations, grid	Grid node equations
	error vector dimension	4.3.1.1 Kalman filter vectors dimensions

F
	FDL, GNU Free Documentation License	C.1 GNU Free Documentation License
	feature setting	3.1 Overview of additional features setting
	Feedback gain	4.4 Feedback gain
	`ffl` (linearity test)	2.4.1 Executing code at the end of each time step
	final cost	4.2.1 Overview of optimisation with Miniker

G
	Generalized linear tangent system	4.6 Generalized linear tangent system analysis
	‘`gradpj.data`’	4.2.4 Sensitivity of cost function to parameters
	graphics	2.3.4 Doing graphics
	graphics with `gnuplot`	2.3.4 Doing graphics
	graphics with `PAW`	2.3.4 Doing graphics
	graphics, Borel sweep	4.4.2 Borel sweep results
	GTLS	4.6 Generalized linear tangent system analysis
	GTLS output	4.6.3 Generalized tangent linear system analysis run and output
	GTLS run	4.6.3 Generalized tangent linear system analysis run and output

H
	Heaviside function	3.6 Rule of programming non continuous models

I
	initial variance-covariance on states	Initial variance-covariance matrix on the state
	installation with make	A.4.3 Installation with make
	integrand cost	4.2.1 Overview of optimisation with Miniker

K
	Kalman filter	4.3 Kalman filter
	Kalman filter output	4.3.2.2 Kalman filter results
	Kalman filter results	4.3.2.2 Kalman filter results

L
	lapack	A.2 Common requisites
	limiting conditions	Limiting conditions
	linearity test	2.4.1 Executing code at the end of each time step
	logical flags	3.1 Overview of additional features setting
	Lyapunov exponents	4.6 Generalized linear tangent system analysis

M
	‘`Makefile.miniker`’	5.1 Make variables
	‘`Makefile.sltc`’	4.5.2 Singular Value Decomposition with make
	‘`Makefile.sltcirc`’	4.6.2 Generalized tangent linear system with make
	‘`mini_ker.cmz`’	A.3 Miniker with cmz
	`mod`	2.3.1 Setup a model and compile with cmz
	model equations	3.9.2 Entering the model equations, with explicit sizes
	model size	3.9.1 The explicit size sequence
	‘`Model.hlp`’	2.3.3 Running a simulation and using the output
	mortran	Introduction
	mortran	2.2.1 All you need to know about mortran and cmz directives
	`mortran`, with make	A.4.1 Additional requirements for Miniker with make

O
	‘`obs.data`’	3.8.1 Observations
	observation function	3.8.1 Observations
	observations	4.3.1.2 Error and observation matrices
	observations, general	4.3 Kalman filter
	optimisation	4.2.1 Overview of optimisation with Miniker
	output file	2.3.3 Running a simulation and using the output
	output, GTLS	4.6.3 Generalized tangent linear system analysis run and output
	output, Kalman filter	4.3.2.2 Kalman filter results
	output, sensitivity	4.1 Automatic sensitivity computation
	output, SVD	4.5.3 Singular Value Decomposition run and output

P
	printing	2.4.2 Controlling the printout and data output
	Programming environments	A.1 Programming environments
	propagator	4.6 Generalized linear tangent system analysis

R
	requirements, with make	A.4.1 Additional requirements for Miniker with make
	‘`res.data`’	2.3.3 Running a simulation and using the output
	results, Borel sweep	4.4.2 Borel sweep results
	results, Kalman filter	4.3.2.2 Kalman filter results
	run, GTLS	4.6.3 Generalized tangent linear system analysis run and output
	run, SVD	4.5.3 Singular Value Decomposition run and output
	running model	2.3.3 Running a simulation and using the output

S
	select flag	3.1 Overview of additional features setting
	‘`selseq.kumac`’	3.1 Overview of additional features setting
	‘`selseq.kumac`’	A.3 Miniker with cmz
	‘`sens.data`’	4.1 Automatic sensitivity computation
	sensitivities	4.1 Automatic sensitivity computation
	sensitivity, output	4.1 Automatic sensitivity computation
	sequence	2.1 General structure of the code
	sequences	2. Miniker model programming
	‘`sigma.data`’	4.1 Automatic sensitivity computation
	Singular Value Decomposition	4.5 Stability analysis of fastest modes
	‘`sltc.exe`’	4.5 Stability analysis of fastest modes
	‘`sltc.exe`’	4.5.3 Singular Value Decomposition run and output
	‘`sltcirc.exe`’	4.6 Generalized linear tangent system analysis
	‘`sltcirc.exe`’	4.6.3 Generalized tangent linear system analysis run and output
	`smod`	4.5.1 Singular Value Decomposition with cmz
	`smod`	4.6.1 Generalized tangent linear system with cmz
	starting point	Starting points
	state matrix	4.5 Stability analysis of fastest modes
	SVD	4.5 Stability analysis of fastest modes
	SVD output	4.5.3 Singular Value Decomposition run and output
	SVD run	4.5.3 Singular Value Decomposition run and output

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