# CLSP — Convex Least Squares Programming

**Convex Least Squares Programming (CLSP)** is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction (Lasso, Ridge, Elastic Net) to ensure numerical stability, constraint enforcement, and interpretability. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R², normalized RMSE (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands. All calculations use numpy.float64 precision.

## Installation

```bash
pip install pyclsp
```

## Quick Example

```python
import   numpy as     np
from     clsp  import CLSP

# CMLS (RP), based on known stationary points for y = D @ x + e, x to be estimated

seed     = 123456789
rng      = np.random.default_rng(seed)

# sample (dataset)
k        = 500                                               # number of observations in D
p        = 6                                                 # number of regressors
c        = 1                                                 # sum of coefficients
D        = np.empty((k, p))
D[:, 0 ] = 1.0                                               # constant
D[:, 1:] = rng.normal(size=(k, p - 1))                       # D.,j ~ N(0,1), 2 <= j <= p
b_true   = rng.normal(size=p)                                # b_true ~ N(0,1)
b_true   = (b_true / b_true.sum()) * c
e        = rng.normal(size=(k, 1))                           # e ~ N(0,1)
y        = (D @ b_true).reshape(-1, 1) + e                   # y_t = D @ b_true + e_t

# model
b        = np.vstack([
               np.asarray([c]),                              # c (the sum of coefficients)
               np.zeros((k - 2, 1)),                         # zeros
               np.zeros((k - 1, 1)),                         # zeros
               y                                             # values of y_t
           ])
C        = np.vstack([
               np.ones((1, p)),                              # a row of ones
               np.diff(D, n=2, axis=0),                      # the 2nd differences
               np.diff(D, n=1, axis=0)                       # the 1st differences
           ])
S        = np.block([
               [np.zeros((  1, k-2))],                       # a zero vector
               [np.diag(np.sign(np.diff(y.ravel(), n=2)))],  # a diagonal sign matrix
               [np.zeros((k-1, k-2))],                       # a zero matrix
           ])
model    = CLSP().solve(
               problem="cmls", b=b, C=C, S=S, M=D,
               r=1,                                          # a solution without refinement
               alpha=1.0                                     # a unique MNBLUE estimator
           )

# results
print("true beta (x_M):")
print(np.round(np.asarray(b_true).flatten(), 4))
print("beta hat (x_M hat):")
print(np.round(model.x.flatten(), 4))

model.summary(display=True)

print("  Monte Carlo t-test:")
for kw, val in model.ttest(sample_size=30,                   # NRMSE_partial sample
                           seed=seed, distribution="normal", # seed and distribution
                           partial=True).items():
    print(f"    {kw}: {float(val):.6f}")
```

## User Reference

For comprehensive information on the estimator’s capabilities, advanced configuration options, and implementation details, please refer to the docstrings provided in each of the individual .py source files. These docstrings contain complete descriptions of available methods, their parameters, expected input formats, and output structures.

### The `CLSP` Class

```python
self.__init__()
```

Stores the solution, goodness-of-fit statistics, and ancillary parameters.

The class has three core methods: `solve()`, `corr()`, and `ttest()`.

**Selected attributes:**
`self.A`             : *np.ndarray*
design matrix `A` = [`C` | `S`; `M` | `Q`], where `Q` is either a zero matrix or *S_residual*.

`self.b`             : *np.ndarray*
vector of the right-hand side.

`self.zhat`          : *np.ndarray*
vector of the first-step estimate.

`self.r`             : *int*
number of refinement iterations performed in the first step.

`self.z`             : *np.ndarray*
vector of the final solution. If the second step is disabled, it equals `self.zhat`.

`self.x`             : *np.ndarray*
`m` x `p` matrix or vector containing the variable component of `z`.

`self.y`             : *np.ndarray*
vector containing the slack component of `z`.

`self.kappaC`        : *float*
spectral κ() for *C_canon*.

`self.kappaB`        : *float*
spectral κ() for *B* = *C_canon^+*`A`.

`self.kappaA`        : *float*
spectral κ() for `A`.

`self.rmsa`          : *float*
total root mean square alignment (RMSA).

`self.r2_partial`    : *float*
R^2 for the `M` block in `A`.

`self.nrmse` : *float*
mean square error calculated from `A` and normalized by standard deviation (NRMSE).

`self.nrmse_partial` : *float*
mean square error calculated from the `M` block in `A` and normalized by standard deviation (NRMSE).

`self.z_lower`       : *np.ndarray*
lower bound of the diagnostic interval (confidence band) based on κ(`A`).

`self.z_upper`       : *np.ndarray*
upper bound of the diagnostic interval (confidence band) based on κ(`A`).

`self.x_lower`       : *np.ndarray*
lower bound of the diagnostic interval (confidence band) based on κ(`A`).

`self.x_upper`       : *np.ndarray*
upper bound of the diagnostic interval (confidence band) based on κ(`A`).

`self.y_lower`       : *np.ndarray*
lower bound of the diagnostic interval (confidence band) based on κ(`A`).

`self.y_upper`       : *np.ndarray*
upper bound of the diagnostic interval (confidence band) based on κ(`A`).

### Solver Method: `solve()`

```python
self.solve(problem, C, S, M, b, m, p, i, j, zero_diagonal, r, Z, tolerance, iteration_limit, final, alpha)
```

Solves the Convex Least Squares Programming (CLSP) problem.

This method performs a two-step estimation:
(1) a pseudoinverse-based solution using either the Moore–Penrose or Bott–Duffin inverse, optionally iterated for refinement;
(2) a convex-programming correction using Lasso, Ridge, or Elastic Net regularization (if enabled).

**Parameters:**
`problem` : *str*, optional
    Structural template for matrix construction. One of:
    - *'ap'* or *'tm'* : allocation (tabular) matrix problem (AP).
    - *'cmls'* or *'rp'* : constrained-model least squares (regression) problem.
    - anything else: general CLSP problem (user-defined `C` and/or `M`).

`C`, `S`, `M` : *np.ndarray* or *None*
    Blocks of the design matrix `A` = [`C` | `S`; `M` | `Q`]. If `C` and/or `M` are provided, the matrix `A` is constructed accordingly (please note that for AP, `C` is constructed automatically and known values are specified in `M`).

`b` : *np.ndarray* or *None*
    Right-hand side vector. Must have as many rows as `A` (please note that for AP, it should start with row sums). Required.

`m`, `p` : *int* or *None*
    Dimensions of X ∈ ℝ^{m×p}, relevant for AP.

`i`, `j` : *int*, default = *1*
    Grouping sizes for row and column sum constraints in AP.

`zero_diagonal` : *bool*, default = *False*
    If *True*, enforces structural zero diagonals.

`r` : *int*, default = *1*
    Number of refinement iterations for the pseudoinverse-based estimator.

`Z` : *np.ndarray* or *None*
    A symmetric idempotent matrix (projector) defining the subspace for Bott–Duffin pseudoinversion. If *None*, the identity matrix is used, reducing the Bott–Duffin inverse to the Moore–Penrose case.

`rcond` : *float* or *bool*, default = *False*
    Regularization parameter for the Moore-Penrose and Bott-Duffin inverses, providing numerically stable inversion and ensuring convergence of singular values.
    If True, an automatic tolerance equal to `tolerance` is applied. If set to a float, it specifies the relative cutoff below which small singular values are treated as zero.

`tolerance` : *float*, default = *square root of machine epsilon*
    Convergence tolerance for NRMSE change between refinement iterations.

`iteration_limit` : *int*, default = *50*
    Maximum number of iterations allowed in the refinement loop.

`final` : *bool*, default = *True*
    If *True*, a convex programming problem is solved to refine `zhat`. The resulting solution `z` minimizes a weighted L1/L2 norm around `zhat` subject to `Az` = `b`.

`alpha` : *float*, *list[float]* or *None*, default = *None*
    Regularization parameter (weight) in the final convex program:
    - `α = 0`: Lasso (L1 norm)
    - `α = 1`: Tikhonov Regularization/Ridge (L2 norm)
    - `0 < α < 1`: Elastic Net
    If a scalar float is provided, that value is used after clipping to [0, 1].
    If a list/iterable of floats is provided, each candidate is evaluated via a full solve, and the α with the smallest NRMSE is selected.
    If None, α is chosen, based on an error rule: α = min(1.0, NRMSE_{α = 0} / (NRMSE_{α = 0} + NRMSE_{α = 1} + tolerance))

`*args`, `**kwargs` : optional
    CVXPY arguments passed to the CVXPY solver.

**Returns:**
*self*

### Correlogram Method: `corr()`

```python
self.corr(reset, threshold)
```

Computes the structural correlogram of the CLSP constraint part.

This method performs a row-deletion sensitivity analysis on the canonical constraint matrix `[C` | `S`], denoted as *C_canon*, and evaluates the marginal effect of each constraint row on numerical stability, angular alignment, and estimator sensitivity.

For each row `i` in `C_canon`, it computes:
    - The Root Mean Square Alignment (`RMSA_i`) with all other rows `j` ≠ `i`.
    - The change in condition numbers κ(`C`), κ(`B`), and κ(`A`) when row `i` is deleted.
    - The effect on estimation quality: changes in `nrmse`, `zhat`, `z`, and `x` when row `i` is deleted.

Additionally, it computes the total `rmsa` statistic across all rows, summarizing the overall angular alignment of *C_canon*.

**Parameters:**
`reset` : *bool*, default = *False*
    If *True*, forces recomputation of all diagnostic values (the results are preserved for eventual reproduction after the method is called).

`threshold` : *float*, default = *0*
    If positive, limits the output to constraints with `RMSA_i` ≥ `threshold`.

**Returns:**
*dict* of *list*
    A dictionary containing per-row diagnostic values:
    {
        `"constraint"`   : `[1, 2, ..., k]`,  # 1-based indices
        `"rmsa_i"`       : list of `RMSA_i` values,
        `"rmsa_dkappaC"` : list of Δκ(`C`) after deleting row `i`,
        `"rmsa_dkappaB"` : list of Δκ(`B`) after deleting row `i`,
        `"rmsa_dkappaA"` : list of Δκ(`A`) after deleting row `i`,
        `"rmsa_dnrmse"`  : list of Δ`nrmse` after deleting row `i`,
        `"rmsa_dzhat"`   : list of Δ`zhat` after deleting row `i`,
        `"rmsa_dz"`      : list of Δ`z` after deleting row `i`,
        `"rmsa_dx"`      : list of Δ`x` after deleting row `i`,
    }

### T-Test Method: `ttest`

```python
self.ttest(reset, sample_size, seed, distribution, partial, simulate)
```

Perform bootstrap or Monte Carlo t-tests on the NRMSE statistic from the CLSP estimator.

This function either (a) resamples residuals via a nonparametric bootstrap to generate an empirical NRMSE sample, or (b) produces synthetic right-hand side vectors `b` from a user-defined or default distribution and re-estimates the model. It tests whether the observed NRMSE significantly deviates from the null distribution of resampled or simulated NRMSE values.

**Parameters:**
`reset` : *bool*, default = *False*
    If *True*, forces recomputation of the NRMSE null distribution (under H₀) (the results are preserved for eventual reproduction after the method is called).

`sample_size` : *int*, default = *50*
    Size of the Monte Carlo simulated sample under H₀.

`seed` : *int* or *None*, optional
    Optional random seed to override the default.

`distribution` : *str* or *None*, default = *’normal’*
    Distribution for generating simulated `b` vectors. One of (standard): *'normal'*, *'uniform'*, or *'laplace'*.
    
`partial` : *bool*, default = *False*
    If True, runs the t-test on the partial NRMSE: during simulation, the C-block entries are preserved and the M-block entries are simulated.

`simulate` : bool, default = **False**
    If True, performs a parametric Monte Carlo simulation by generating synthetic right-hand side vectors `b`. If False (default), executes a nonparametric bootstrap procedure on residuals without re-estimation.

**Returns:**
*dict*
    Dictionary with test results and null distribution statistics:
    {
        `'p_one_left'`  : P(nrmse ≤ null mean),
        `'p_one_right'` : P(nrmse ≥ null mean),
        `'p_two_sided'` : 2-sided t-test p-value,
        `'nrmse'`       : observed value,
        `'mean_null'`   : mean of the null distribution (under H₀),
        `'std_null'`    : standard deviation of the null distribution (under H₀)
    }

### Summary Method: `summarize` or `summary`

```python
self.summarize(display)
self.summary(display)
```

Return or print a summary for the CLSP estimator.

**Parameters:**
`display` : bool, default = **False**
    If True, prints the summary instead of returning a dictionary.

**Returns:**
*dict*
    Dictionary of estimator configuration, numerical stability, and goodness of fit statistics:
    {
        `'inverse'`    : type of generalized inverse used ('Bott-Duffin' or 'Moore-Penrose'),
        ...
        `'final'`      : boolean flag indicating whether the second step was present,
        ...
        `'rmsa'`       : total RMSA,
        ...
        `'r2_partial'` : coefficient of determination for the M block within A,
        ...
    }

## Bibliography
Bolotov, I. (2025). CLSP: Linear Algebra Foundations of a Modular Two-Step Convex Optimization-Based Estimator for Ill-Posed Problems. *Mathematics*, *13*(21), 3476. [https://doi.org/10.3390/math13213476](https://doi.org/10.3390/math13213476)

## License
MIT License — see the [LICENSE](LICENSE) file.
