Documentation for GCAM

The Global Change Analysis Model

View the Project on GitHub JGCRI/gcam-doc

At each time step, GCAM searches for a vector of prices that cause all markets to be cleared and all consistency conditions to be satisfied. The mapping from input prices to output market disequilibria is a vector function . The GCAM solver is responsible for finding the root of this equation; that is, the point at which .

GCAM has several solver algorithms at its disposal. The solver algorithms can be combined so that several of them are used in sequence. The mix of algorithms can be varied from one model timestep to the next and can be customized for markets that require special treatment. Additionally, each solver algorithm has several adjustable parameters that are user configurable. These configuration options are specified in the solver configuration file.

The bisection solver is a multidimensional generalization of one-dimensional bisection methods. The one-dimensional methods work by first establishing a bracket, identified by a change of sign in , around the solution. At each iteration is evaluated at the midpoint of the bracket interval, and the sign of the result tells us which half of the interval contains the solution. The endpoints of the bracket are adjusted to the newly-identified interval, and the iterations continue until the interval is small enough that we have effectively isolated the solution.

For a system of equations in multiple dimensions, it is generally not possible to construct a rigorous bracket around a solution. Even if we find two points for which all of the equations in the system have opposite sign, there is no guarantee that a solution exists somewhere on the line connecting the two points. The reason why is that the solution to the equation is a single point where all components of are simultaneously zero. As we move from one of our putative bracket points to the other the overwhelming likelihood is that the components of will change sign at different places along the path, none of which will be the solution we seek. Thus, in general, bracketing and bisection is not a viable strategy for solving a system of equations.

Despite this shortcoming, bisection can often be used to get the solver into the general vicinity of the solution, from which one of the other solvers can easily find it. For initial guesses far from the solution, bisection can often be faster than more sophisticated solution techniques. Moreover, bisection places no conditions on the partial derivatives of , making it useful for jostling the solver out of singular regions. These two properties make bisection a useful technique for getting a solution started, before finishing with another technique.

**bracket-interval**: The amount by which the bracket boundary will be increased or decreased when attempting to bracket the solution. This is a multiplicative factor, so a bracket interval of means that we will increase prices by multiplying by and decrease them by dividing by .**max-bracket-iterations**: The maximum number of steps the solver will take when attempting to bracket the solution.**max-iterations**: The maximum number of bisection iterations the solver will perform before giving up.**solution-info-filter**: A solution info filter predicate that will be used to determine which markets the solver will attempt to solve.

The Broyden Solver is a globally convergent iterative method
based on the one in *Numerical Recipes*, section
9.7^{1}. In each iteration we solve the
equation

for a correction step , where is a secant approximation to the Jacobian matrix of first derivatives of . Using an approximate Jacobian allows us to reduce the number of finite-difference Jacobian matrices compared to what would be required if we used the exact Jacobian, , in equation (1). Computing an exact finite-difference Jacobian in GCAM is a very expensive process, so using the secant approximation results in tremendous computational savings.

In each iteration of the Broyden algorithm, we update using the results of the computations in the previous iteration. We solve equation (1) for an iteration step , and we update to . This process is iterated until is sufficiently small.

The procedure just described will often fail to converge when is far from its solution value. This problem is particularly
evident in functions that have rapidly changing or discontinuous
derivatives and in functions where the Jacobian matrix is nearly
singular. In these situations, equation (1) attempts to extrapolate
local information about the gradient of the function to distant
regions where it is invalid. Such a step can easily land the solver in
an unusual region of the parameter space (*i.e.,* a region for which
the model is ill-behaved or undefined), and when this happens the
algorithm rarely recovers.

We mitigate these problems by monitoring and
backtracking along the direction of when an
ordinary iteration step causes to increase. This allows
the solver to follow the step far enough to progress toward the
desired solution, while rejecting large steps that take us further
away from it. Computationally we implement this procedure by scaling
the iteration step by a factor . We start
each iteration with and evaluate . If it has
failed to decrease sufficiently, we decrease until we
obtain an acceptable step. The heuristic for accepting a step and the
procedure for decreasing are described in *Numerical
Recipes*.^{1}

**Because the Broyden solver is the only solver algorithm that can drive
the system all the way to a solution, it should always be included in
the configuration.** Failure to do so will almost always result in a
failure to solve.

The backtracking procedure greatly enhances the convergence properties of the solver over the basic procedure, but the solver will still fail under some circumstances. The most commonly occurring such circumstance is a singular Jacobian caused by one or more prices having no impact on the excess demand in any of the solved markets. In GCAM this situation occurs predictably at high and low price extremes. Many of the input supply and demand functions saturate in extreme price regimes, leading to zero derivatives. One of the ways GCAM deals with this problem is by excluding markets with obvious price extremes from the Broyden solver and using other solver algorithms on them until their prices come into the normal price domain.

**ftol**: Convergence criterion for the solver. The solver is considered converged when the magnitude of the largest component of is less than ftol.**max-iterations**: Maximum number of iterations of the main loop to execute before giving up. This is only loosely related to the number of evaluations used as a criterion in the general parameters. In an iteration with no backtracking, the algorithm will make a single evaluation. An iteration that backtracks may take anywhere from two evaluations to a dozen or more. In some particularly difficult cases the algorithm may beforced to compute a finite-difference Jacobian, which will count for approximately evaluations, where is the total number of markets and constraints being solved (usually a few hundred).**solution-info-filter**: A solution info filter predicate that will be used to determine which markets the solver will attempt to solve.

This solver is a predecessor to the Broyden solver, which supersedes it. The NR solver is deprecated and is scheduled to be removed in the next GCAM release. The Broyden solver should be used instead.

Though the Preconditioner is treated as a solver its purpose is not to solve the GCAM system as such; rather it is a supplement to the other algorithms that attempts to modify initial guesses to avoid problems associated with singular matrices. It does this by exploiting things that we know about the GCAM system, such as the fact that most resource supply curves have a turn-on price, below which none of the resource will be produced. Therefore, including the Preconditioner in the solver configuration can often prevent solution failures and cause periods to solve in fewer iterations.

**max-iterations**: The preconditioner can make either one or two passes over the system. The heuristics used on the second pass are slightly different than those used on the first pass. It is not useful to perform more than two passes**ftol**: Increment by which an initial guess is set inside the allowable range of an input price, when the original guess was outside the allowable range.**solution-info-filter**: A solution info filter predicate that will be used to determine which markets will be preconditioned.

In addition to these, the parser also recognizes the tokens
**price-increase-fac**, **price-decrease-fac**, and
**large-price-thresh**; however, none of these are currently used.

The solver configuration, including the choice of solver or solvers to use and settable solver parameters, is selected in the solver configuration file. This file is specified in the input Configuration file using a line of the form:

`<Value name="solver_config">../input/solution/cal_solver_config.xml</Value>`

The solver configuration file comprises a series of blocks that specify the configuration of the solver to use in each period of the model run. For example:

```
<user-configurable-solver year="2005">
<solution-tolerance>0.001</solution-tolerance>
<solution-floor>0.0001</solution-floor>
<calibration-tolerance>0.01</calibration-tolerance>
<max-model-calcs>2500</max-model-calcs>
.
.
.
</user-configurable-solver>
```

The first line indicates that this is the configuration to be used in 2005. If the same solver configuration is to be used for all subsequent years, we can add the “fillout” parameter:

`<user-configurable-solver year="2010" fillout="1">`

The first few lines of the configuration specify General Solver Parameters, which apply to all solver components. For the most part, these parameters dictate things like stopping conditions, which apply to all of the solver components that will be used.

Following the general parameters are one or more blocks that specify solver components, which direct the solver to run particular solution algorithms. For example:

```
<broyden-solver-component>
<max-iterations>25</max-iterations>
<ftol>1.0e-3</ftol>
<solution-info-filter>solvable-nr || (market-type="Tax" && solvable)</solution-info-filter>
</broyden-solver-component>
```

The first few lines specify parameters that are specific to this
particular solver component. The `solution-info-filter`

line gives a
predicate for determining which
markets the component will attempt to solve. This filter allows us to
exclude from a solver component markets that are likely to cause that
component to fail (for example, markets that make the Jacobian matrix
singular for the Broyden solver) or whose solution is unlikely
to be improved by the component.

The algorithms will be run consecutively in the order listed in the configuration file. When all have been run, the termination criteria will be checked. The sequence of components will be repeated until either the model is solved or the maximum number of model evaluations has been exceeded.

**calibration-tolerance**: During the calibration periods, the tolerance for determining whether or not the calibration was successful.**max-model-calcs**: The maximum number of times the full model will be evaluated in the course of the solution. If the solver has not found a valid solution by that time, it exits and issues a warning.**solution-tolerance**: The relative excess demand threshold, below which the model is considered solved. Loosely speaking, this is the accuracy to which we wish to solve the model. It should be set greater than or equal to the`ftol`

parameter in the Broyden solver component, so that any candidate solution returned by the Broyden algorithm will be guaranteed to pass this test as well.**solution-floor**: The absolute excess demand threshold, below which the model is considered solved. This parameter covers the cases where a low demand value makes achieving a relative excess demand threshold unfeasible.

The predicates available to filter markets are:

**all**: True for any market.**market-name**: True if the name of the market matches the name given in the filter.**market-type**: True if the type of the market matches the type given in the filter. The available market types are Normal, Calibration, Inverse-Calibration, Tax, RES, Subsidy, Trial-Value, Demand, and Price.**solvable**: True for markets that should be solved. “Solvable” in this context refers to intent, not capability. A market is “solvable” if its price is an independent variable (*i.e.*, not determined by another market). Additionally, constraint markets (*e.g.*, greenhouse gas caps) are “unsolvable” when the constraint is not binding.**solvable-nr**: True for markets that should be solved and meets the criteria for solving with a Broyden solver. For most markets these criteria are that the price and supply are nonzero. (Exception: trial-value markets are exempt from the nonzero price and supply requirements.)**unsolved**: True for markets that are not yet solved.

Predicates can be combined with the logical operators **and** (&&)
(*NB:* Since the & character is reserved in XML, it must be written as
`&amp;`

), **or** (||), and **not** (!) to form compound
predicates. These conjunctions can be grouped with parentheses.

For example:

` <solution-info-filter>solvable-nr || (market-type="Tax" &amp;&amp; solvable)</solution-info-filter>`

or

` <solution-info-filter>unsolved &amp;&amp; solvable &amp;&amp; `

` !(market-name="globalcrude oil" || market-name="globalnatural gas" || market-name="globalcoal") </solution-info-filter>`

The first of these would accept all solvable-nr markets and all solvable tax markets (even if they don’t meet the NR requirements). The second would exclude the global crude oil, natural gas, and coal markets and accept all other solvable markets that are not yet solved.

In order to compute its iteration step, the Broyden solver must solve equation (1) for . In GCAM’s default configuration it does this using an L-U Decomposition subroutine provided in the Boost libraries. L-U Decomposition is quick, but it has no way to recover from a singular Jacobian matrix. Worse, in cases where the Jacobian matrix is formally not singular, but is nevertheless badly ill-conditioned, the algorithm can spend many iterations generating iteration steps that are badly corrupted by roundoff error. Sometimes the algorithm can recover from such a situation, and sometimes not, but either way it wastes a lot of time.

To combat this effect, we developed a version of the solver that uses Singular Value Decomposition (SVD). The idea behind SVD is to use more sophisticated linear algebra techniques to detect and filter out components of that are corrupted by ill-conditioning in the Jacobian matrix. SVD is a more complex algorithm than L-U and takes longer to compute, but the iterations saved can more than offset the additional computational cost.

To use SVD you must have LAPACK and BLAS libraries installed, along with the Boost numeric bindings. There are many implementations of LAPACK and BLAS available, but some of them are better than others. In general, you shouldn’t try to use LAPACK with GCAM unless you have access to a vendor-supplied implementation, such as the Intel Math Kernel Libraries (MKL).

The SVD solver must be selected at compile time. If you have an old
GCAM build in your workspace you will have to run `make clean`

to
clear out the old object files. You will also need to set the
following environment variables:

```
export USE_LAPACK=1
export BOOST_NUMERIC_BINDINGS=/path/to/numeric/binding/sources
```

If you are using MKL, additionally set the following environment variables:

```
export MKL_CFLAGS=-fopenmp -I/path/to/mkl/include/files
export MKL_LDFLAGS=-fopenmp -L/path/to/mkl/lib -lmkl_intel_lp64 -lmkl_core -lmkl_gnu_thread -ldl -lpthread -lm
export MKL_RPATH=-Wl,rpath,/path/to/mkl/lib
```

If you are using some other LAPACK, you must instead set (assuming that LAPACK and BLAS are installed in the same place):

```
export LAPACKINC=/path/to/lapack/include/files
export LAPACKLIB=/path/to/lapack/library/files
```

Once you have set up the environment, building with `make gcam`

will
build the LAPACK-enabled version of GCAM. If you are using a version of
LAPACK that you provided, then that is all you have to do; you can run
GCAM with LAPACK normally. If you are using MKL, then there is some
run-time environment setup you should do. While not strictly
required, these settings will give you better performance in the SVD
subroutines (replace NN with the number of processors you have):

```
export MKL_NUM_THREADS=1
export MKL_DOMAIN_NUM_THREADS="BLAS=NN"
```

For newer solver algorithms, the GCAM solver is decoupled from the rest of the GCAM model by an abstraction layer that presents the model to the solver as a vector function of a single vector argument: . Both vectors have length equal to the number of solved markets. The input vector is the log of the prices (or price-analogs, for special markets that redefine the price method). The output vector is the excess demand (or analog, for markets that redefine it), . The abstraction layer also takes care of scaling inputs and outputs to a common order of magnitude and making additional corrections for certain special cases.

The solver is mostly unaware of the details of how the input vector is
processed for use in the model or how the model is invoked. The sole
exception is the `partial`

method, which allows the solver to signal
that it is computing a partial derivative. This allows the abstraction
layer to optimize the model computation by skipping the calculation of
values that have not changed. It does not affect the results returned.

The abstraction layer takes care of all of the manipulations that have to happen to get the model to run with a set of input prices. In order, these are:

- Zeroing all market supplies and demands
*(full calculation only)*. - Saving the current market values and setting flags
*(partial calculation only)*. - Determining the affected nodes
*(partial calculation only)* - Converting from log price to price.
- Calling
`world->calc()`

- Converting from excess demand to log excess demand.
- Restoring any state that was modified.

This arrangement greatly simplifies the development of solver algorithms and eliminates redundant code that was formerly duplicated in each algorithm. Some older algorithms bypass the abstraction layer and perform these tasks directly. Over time, these solvers will be phased out and replaced with versions that call the model through the abstraction layer.