Clear old FrontierOR files 5401-5583 of 5583

xie2019/solution_logger.py

@@ -1,61 +0,0 @@
-"""
-Convergence logger for optimization algorithms.
-
-Records incumbent solutions with timestamps to a JSONL file.
-This module is provided to LLM-generated programs — they only need to call
-`log(objective_value)` whenever a better feasible solution is found.
-
-Usage in generated code:
-    from solution_logger import SolutionLogger
-    logger = SolutionLogger(log_path, sense="minimize")  # or "maximize"
-    # ... inside algorithm loop:
-    logger.log(objective_value)
-"""
-
-import json
-import time
-
-
-class SolutionLogger:
-    def __init__(self, log_path, sense="minimize"):
-        """
-        Args:
-            log_path: Path to the JSONL output file.
-            sense: "minimize" or "maximize".
-        """
-        self.log_path = log_path
-        self.sense = sense
-        self.start_time = time.time()
-        self.best_obj = None
-        self.min_interval = 0.1  # seconds, avoid excessive writes
-
-        self._last_log_time = 0.0
-        # Clear the file
-        with open(self.log_path, "w") as f:
-            pass
-
-    def log(self, objective_value):
-        """Record a new incumbent if it improves on the best known."""
-        if objective_value is None:
-            return
-
-        # Check if this is an improvement
-        if self.best_obj is not None:
-            if self.sense == "minimize" and objective_value >= self.best_obj:
-                return
-            if self.sense == "maximize" and objective_value <= self.best_obj:
-                return
-
-        elapsed = time.time() - self.start_time
-
-        # Throttle writes
-        if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval:
-            self.best_obj = objective_value
-            return
-
-        self.best_obj = objective_value
-        self._last_log_time = elapsed
-
-        with open(self.log_path, "a") as f:
-            f.write(json.dumps({"time": round(elapsed, 3),
-                                "objective_value": objective_value}) + "\n")

xie2019/solution_schema.json

@@ -1,12 +0,0 @@
-{
-    "objective_value": "<float or null> Total weighted deviation cost summing berth-position and start-time penalties across all vessels.",
-    "vessels": [
-        {
-            "id": "<int> Zero-based index identifying the vessel.",
-            "berth": "<int> Index of the berth assigned to the vessel.",
-            "start_time": "<int> Time step at which the vessel begins service.",
-            "end_time": "<int> Time step at which the vessel completes service.",
-            "profile": "<int> Index of the QC assignment profile assigned to the vessel."
-        }
-    ]
-}

xu2024/feasibility_check.py

@@ -1,192 +0,0 @@
-#!/usr/bin/env python3
-"""
-Feasibility checker for Cardinality Constrained Mean-Variance Portfolio Optimization.
-
-Constraints (from math_model.txt):
-  (C1) r^T x >= b          : portfolio return must be at least b
-  (C2) e^T x = 1           : portfolio weights sum to 1
-  (C3) sum delta(x_i) <= k : at most k assets have nonzero weights
-  (C4) Objective consistency (Tier C exploit defense): recompute
-       f(x) = (1/2) x^T Q x from the solution variables and reject when
-       the reported objective_value disagrees. All variables on which the
-       objective depends (the vector x) are carried in the solution, so a
-       full recompute is exact.
-"""
-
-import argparse
-import json
-import math
-
-
-def check_feasibility(instance, solution):
-    tol = 1e-5
-    eps = 1e-5
-
-    n = instance["n"]
-    k = instance["k"]
-    b = instance["b"]
-    r = instance["r"]
-    Q = instance["Q"]
-    x = solution["x"]
-
-    violated_constraints = set()
-    violations = []
-    violation_magnitudes = []
-
-    # --- Constraint 1: r^T x >= b ---
-    lhs1 = sum(r[i] * x[i] for i in range(n))
-    rhs1 = b
-    violation_amount1 = rhs1 - lhs1  # for >= constraint: how much RHS exceeds LHS
-    if violation_amount1 > tol:
-        violated_constraints.add(1)
-        violations.append(
-            f"Constraint 1 violated: portfolio return r^T x = {lhs1:.10f} < b = {rhs1:.10f}"
-        )
-        normalizer1 = max(abs(rhs1), eps)
-        violation_magnitudes.append({
-            "constraint": 1,
-            "lhs": lhs1,
-            "rhs": rhs1,
-            "raw_excess": violation_amount1,
-            "normalizer": normalizer1,
-            "ratio": violation_amount1 / normalizer1,
-        })
-
-    # --- Constraint 2: e^T x = 1 ---
-    lhs2 = sum(x[i] for i in range(n))
-    rhs2 = 1.0
-    violation_amount2 = abs(lhs2 - rhs2)
-    if violation_amount2 > tol:
-        violated_constraints.add(2)
-        violations.append(
-            f"Constraint 2 violated: sum of weights = {lhs2:.10f}, should equal 1.0"
-        )
-        normalizer2 = max(abs(rhs2), eps)
-        violation_magnitudes.append({
-            "constraint": 2,
-            "lhs": lhs2,
-            "rhs": rhs2,
-            "raw_excess": violation_amount2,
-            "normalizer": normalizer2,
-            "ratio": violation_amount2 / normalizer2,
-        })
-
-    # --- Constraint 3: sum delta(x_i) <= k ---
-    # delta(x_i) = 1 if x_i != 0, 0 otherwise. Use tol for zero check.
-    num_nonzero = sum(1 for i in range(n) if abs(x[i]) > tol)
-    lhs3 = float(num_nonzero)
-    rhs3 = float(k)
-    violation_amount3 = lhs3 - rhs3  # for <= constraint: how much LHS exceeds RHS
-    if violation_amount3 > tol:
-        violated_constraints.add(3)
-        violations.append(
-            f"Constraint 3 violated: {num_nonzero} nonzero assets exceeds cardinality limit k = {k}"
-        )
-        normalizer3 = max(abs(rhs3), eps)
-        violation_magnitudes.append({
-            "constraint": 3,
-            "lhs": lhs3,
-            "rhs": rhs3,
-            "raw_excess": violation_amount3,
-            "normalizer": normalizer3,
-            "ratio": violation_amount3 / normalizer3,
-        })
-
-    # --- Constraint 4: Objective consistency (Tier C exploit defense) ---
-    # Recompute f(x) = (1/2) * x^T Q x from the solution variables and
-    # reject when the reported value disagrees.
-    reported_obj = solution.get("objective_value")
-    if reported_obj is not None:
-        try:
-            reported = float(reported_obj)
-        except (TypeError, ValueError):
-            reported = None
-        if reported is not None and not (math.isnan(reported) or math.isinf(reported)):
-            # Compute x^T Q x via the symmetric quadratic form.
-            qx = [0.0] * n
-            for i in range(n):
-                Qi = Q[i]
-                acc = 0.0
-                for j in range(n):
-                    acc += Qi[j] * x[j]
-                qx[i] = acc
-            quad = 0.0
-            for i in range(n):
-                quad += x[i] * qx[i]
-            true_obj = 0.5 * quad
-            abs_diff = abs(reported - true_obj)
-            # 0.1% relative tolerance with 1e-6 absolute floor. The Gurobi
-            # objective for n=50/100 portfolios is typically O(1e-4)-O(1e-2)
-            # (a half-variance), so an absolute floor far below 1e-3 is
-            # required to catch sub-permille exploits while tolerating
-            # double-precision noise from the n^2 quadratic accumulation.
-            tol_obj = max(1e-6, 1e-3 * abs(true_obj))
-            if abs_diff > tol_obj:
-                violated_constraints.add(4)
-                violations.append(
-                    f"Constraint 4 violated: reported objective_value={reported} differs from "
-                    f"recomputed (1/2) x^T Q x = {true_obj} "
-                    f"(|diff|={abs_diff:.6g}, tol={tol_obj:.6g})"
-                )
-                normalizer4 = max(abs(true_obj), eps)
-                violation_magnitudes.append({
-                    "constraint": 4,
-                    "lhs": reported,
-                    "rhs": true_obj,
-                    "raw_excess": abs_diff,
-                    "normalizer": normalizer4,
-                    "ratio": abs_diff / normalizer4,
-                })
-        elif reported is not None:
-            # NaN/Inf reported obj -- always a lie since the true obj is
-            # bounded by (1/2) * max-eigenvalue(Q) * ||x||^2 and x sums to 1.
-            violated_constraints.add(4)
-            violations.append(
-                f"Constraint 4 violated: reported objective_value={reported_obj} is NaN or Inf"
-            )
-            violation_magnitudes.append({
-                "constraint": 4,
-                "lhs": reported,
-                "rhs": 0.0,
-                "raw_excess": float("inf"),
-                "normalizer": eps,
-                "ratio": float("inf"),
-            })
-
-    sorted_violated = sorted(violated_constraints)
-    feasible = len(sorted_violated) == 0
-
-    return {
-        "feasible": feasible,
-        "violated_constraints": sorted_violated,
-        "violations": violations,
-        "violation_magnitudes": violation_magnitudes,
-    }
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Feasibility checker for CCMV portfolio optimization")
-    parser.add_argument("--instance_path", required=True, help="Path to instance JSON file")
-    parser.add_argument("--solution_path", required=True, help="Path to solution JSON file")
-    parser.add_argument("--result_path", required=True, help="Path to output result JSON file")
-    args = parser.parse_args()
-
-    with open(args.instance_path, "r") as f:
-        instance = json.load(f)
-    with open(args.solution_path, "r") as f:
-        solution = json.load(f)
-
-    result = check_feasibility(instance, solution)
-
-    with open(args.result_path, "w") as f:
-        json.dump(result, f, indent=2)
-
-    print(f"Feasibility: {result['feasible']}")
-    if not result["feasible"]:
-        for v in result["violations"]:
-            print(f"  - {v}")
-    print(f"Result saved to {args.result_path}")
-
-
-if __name__ == "__main__":
-    main()

xu2024/gurobi_code.py

@@ -1,80 +0,0 @@
-import argparse
-import json
-
-import gurobipy as gp
-from gurobipy import GRB
-import os as _os, sys as _sys
-# Walk up from this file's directory to find repo root (containing scripts/).
-_repo = _os.path.dirname(_os.path.abspath(__file__))
-while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
-    _repo = _os.path.dirname(_repo)
-if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
-    _sys.path.insert(0, _repo)
-try:
-    from scripts.utils.gurobi_log_helper import install_gurobi_logger
-except ImportError:
-    def install_gurobi_logger(log_path):  # no-op fallback when scripts/ unavailable
-        pass
-
-
-def solve(instance_path: str, solution_path: str, time_limit: float) -> None:
-    with open(instance_path, "r") as f:
-        data = json.load(f)
-
-    n = data["n"]
-    k = data["k"]
-    b = data["b"]
-    r = data["r"]
-    Q = data["Q"]
-
-    model = gp.Model("CCMV")
-    model.setParam("Threads", 1)
-    model.Params.TimeLimit = time_limit
-
-    x = model.addVars(n, lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="x")
-
-    # Binary indicator variables for cardinality constraint.
-    z = model.addVars(n, vtype=GRB.BINARY, name="z")
-
-    # Objective: min (1/2) x^T Q x
-    obj = 0.5 * gp.quicksum(Q[i][j] * x[i] * x[j] for i in range(n) for j in range(n))
-    model.setObjective(obj, GRB.MINIMIZE)
-
-    # Return constraint: r^T x >= b
-    model.addConstr(gp.quicksum(r[i] * x[i] for i in range(n)) >= b, name="return")
-
-    # Budget constraint: e^T x = 1
-    model.addConstr(gp.quicksum(x[i] for i in range(n)) == 1, name="budget")
-
-    # Indicator constraints: z[i] = 0 forces x[i] = 0, exactly representing delta(x_i).
-    for i in range(n):
-        model.addGenConstrIndicator(z[i], False, x[i] == 0, name=f"indicator_{i}")
-
-    # Cardinality constraint: sum z_i <= k
-    model.addConstr(gp.quicksum(z[i] for i in range(n)) <= k, name="cardinality")
-
-    model.optimize()
-
-    result = {}
-    if model.SolCount > 0:
-        result["objective_value"] = model.ObjVal
-        result["x"] = [x[i].X for i in range(n)]
-    else:
-        result["objective_value"] = None
-
-    with open(solution_path, "w") as f:
-        json.dump(result, f, indent=2)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Cardinality Constrained Mean-Variance Portfolio Optimization (Gurobi)"
-    )
-    parser.add_argument("--instance_path", type=str, required=True, help="Path to instance JSON file")
-    parser.add_argument("--solution_path", type=str, default="gurobi_solution_1.json", help="Path to output solution JSON file")
-    parser.add_argument("--time_limit", type=int, required=True, help="Solver time limit in seconds")
-    parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions")
-    args = parser.parse_args()
-    install_gurobi_logger(args.log_path)
-
-    solve(args.instance_path, args.solution_path, args.time_limit)

xu2024/gurobi_feasi_result/large_feasi_result_1.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

xu2024/gurobi_feasi_result/large_feasi_result_2.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

xu2024/gurobi_feasi_result/large_feasi_result_3.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

xu2024/gurobi_feasi_result/large_feasi_result_4.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

xu2024/gurobi_feasi_result/large_feasi_result_5.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

xu2024/gurobi_feasi_result/tiny_feasi_result.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

xu2024/gurobi_solution/large_solution_1.json

@@ -1,207 +0,0 @@
-{
-  "objective_value": 6.904188041509924e-05,
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xu2024/gurobi_solution/large_solution_2.json

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-{
-  "objective_value": 2.8534478920633733e-05,
-  "x": [
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xu2024/gurobi_solution/large_solution_3.json

@@ -1,105 +0,0 @@
-{
-  "objective_value": 5.705001488687638e-05,
-  "x": [
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xu2024/gurobi_solution/large_solution_4.json

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xu2024/gurobi_solution/large_solution_5.json

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xu2024/gurobi_solution/tiny_solution.json

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xu2024/gurobi_solution_log/large_log_1.jsonl

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xu2024/gurobi_solution_log/large_log_2.jsonl

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xu2024/gurobi_solution_log/large_log_3.jsonl

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xu2024/gurobi_solution_log/large_log_4.jsonl

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xu2024/gurobi_solution_log/large_log_5.jsonl

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xu2024/gurobi_solution_log/tiny_log.jsonl

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xu2024/instance/tiny_instance.json

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-}

xu2024/instance_schema.json

@@ -1,7 +0,0 @@
-{
-  "n": "<int> Number of risky assets available for investment.",
-  "k": "<int> Maximum number of assets that may receive a nonzero weight in the portfolio.",
-  "b": "<float> Minimum acceptable weighted average return that the portfolio must achieve.",
-  "r": "<list[float], length n> Expected return of each asset.",
-  "Q": "<list[list[float]], shape [n][n]> Covariance between the returns of each pair of assets."
-}

xu2024/mathematical_formulation.md

@@ -1,41 +0,0 @@
-# Original Formulation: Cardinality Constrained Mean-Variance Portfolio Optimization ($\mathcal{P}_{CCMV}$)
-
-*Source: An Efficient Global Optimal Method for Cardinality Constrained Portfolio Optimization, Wei Xu, Jie Tang, Ka Fai Cedric Yiu, Jian Wen Peng, 2023 (INFORMS Journal on Computing 36(2):690–704).*
-
-## Sets and Indices
-
-- $i = 1, 2, \ldots, n$ : index of the risky assets in the asset pool.
-
-## Parameters
-
-- $n$ : total number of risky assets in the asset pool.
-
-- $k \in \mathbb{N}$ : restriction on the number of invested assets (cardinality).
-
-- $b \in \mathbb{R}$ : lowest target (required) level of portfolio return.
-
-- $r_i$ : expected return (yield) of asset $i$, with $\mathbf{r} = [r_1, r_2, \ldots, r_n]^\top \in \mathbb{R}^n$.
-
-- $\mathbf{Q} \in \mathbb{R}^{n \times n}$ : covariance matrix of the asset returns ($n$-dimensional symmetric positive definite).
-
-- $\mathbf{e} = [1, 1, \ldots, 1]^\top \in \mathbb{R}^n$ : column vector of all ones.
-
-- $\delta(\cdot)$ : indicator function used to determine which assets are selected, given by $$\delta(x) = \begin{cases} 0, & x = 0, \\ 1, & x \neq 0. \end{cases}$$
-
-## Decision Variables
-
-- $x_i \in \mathbb{R}$ : portfolio weight of the $i$-th asset, $i = 1, 2, \ldots, n$, with $\mathbf{x} = [x_1, x_2, \ldots, x_n]^\top \in \mathbb{R}^n$ (real-valued; short-selling permitted).
-
-## Objective
-
-$$\begin{align}
-(\mathcal{P}_{CCMV}) \quad \min_{\mathbf{x} \in \mathbb{R}^n} \quad & f(\mathbf{x}) = \frac{1}{2} \mathbf{x}^\top \mathbf{Q} \mathbf{x} \tag{1}
-\end{align}$$
-
-## Constraints
-
-$$\begin{align}
-\text{s.t.} \quad & \mathbf{r}^\top \mathbf{x} \geq b, \tag{1}\\
-& \mathbf{e}^\top \mathbf{x} = 1, \tag{1}\\
-& \sum_{i=1}^{n} \delta(x_i) \leq k. \tag{1}
-\end{align}$$

xu2024/problem_description.txt

@@ -1,7 +0,0 @@
-# Problem Description
-
-An investor faces a pool of risky assets and must allocate wealth among them by choosing a portfolio weight for each asset. Each asset has a known expected return, and the joint risk structure of all assets is captured by a symmetric positive definite covariance matrix whose row and column count equals the total number of assets, where each entry describes the covariance between the returns of two assets. A minimum acceptable portfolio return level and a maximum number of assets that may be held in the portfolio are also given as inputs.
-
-The investor must determine the weight assigned to each asset in the portfolio. These weights may take any real value, whether positive, zero, or negative, meaning that short-selling is permitted. The weights must sum to exactly one, reflecting the requirement that the entire budget is fully invested. The weighted sum of the individual expected returns, using the chosen portfolio weights, must be at least the minimum acceptable return level. Among all assets in the pool, at most the specified maximum number may receive a nonzero weight; all remaining assets must have a weight of exactly zero.
-
-The goal is to minimize one-half of the portfolio variance, which equals one-half of the quadratic form obtained by multiplying the transpose of the weight vector by the covariance matrix and then by the weight vector itself. This quantity measures the risk of the portfolio: the weighted combination of all pairwise asset return covariances, scaled by the respective portfolio weights.

xu2024/solution_logger.py

@@ -1,61 +0,0 @@
-"""
-Convergence logger for optimization algorithms.
-
-Records incumbent solutions with timestamps to a JSONL file.
-This module is provided to LLM-generated programs — they only need to call
-`log(objective_value)` whenever a better feasible solution is found.
-
-Usage in generated code:
-    from solution_logger import SolutionLogger
-    logger = SolutionLogger(log_path, sense="minimize")  # or "maximize"
-    # ... inside algorithm loop:
-    logger.log(objective_value)
-"""
-
-import json
-import time
-
-
-class SolutionLogger:
-    def __init__(self, log_path, sense="minimize"):
-        """
-        Args:
-            log_path: Path to the JSONL output file.
-            sense: "minimize" or "maximize".
-        """
-        self.log_path = log_path
-        self.sense = sense
-        self.start_time = time.time()
-        self.best_obj = None
-        self.min_interval = 0.1  # seconds, avoid excessive writes
-
-        self._last_log_time = 0.0
-        # Clear the file
-        with open(self.log_path, "w") as f:
-            pass
-
-    def log(self, objective_value):
-        """Record a new incumbent if it improves on the best known."""
-        if objective_value is None:
-            return
-
-        # Check if this is an improvement
-        if self.best_obj is not None:
-            if self.sense == "minimize" and objective_value >= self.best_obj:
-                return
-            if self.sense == "maximize" and objective_value <= self.best_obj:
-                return
-
-        elapsed = time.time() - self.start_time
-
-        # Throttle writes
-        if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval:
-            self.best_obj = objective_value
-            return
-
-        self.best_obj = objective_value
-        self._last_log_time = elapsed
-
-        with open(self.log_path, "a") as f:
-            f.write(json.dumps({"time": round(elapsed, 3),
-                                "objective_value": objective_value}) + "\n")

xu2024/solution_schema.json

@@ -1,4 +0,0 @@
-{
-  "objective_value": "<float> One-half of the portfolio variance, measuring the overall risk of the chosen portfolio.",
-  "x": "<list[float], length n> Weight of each asset in the portfolio, where positive values indicate long positions and negative values indicate short positions."
-}

yang2013/feasibility_check.py

@@ -1,237 +0,0 @@
-#!/usr/bin/env python3
-"""
-Feasibility checker for the 0-1 Quadratic Knapsack Problem (QKP).
-
-Mathematical formulation (Yang et al., 2013):
-    max  sum_{i=1}^{n} sum_{j=1}^{i} p_{ij} * x_i * x_j
-    s.t.
-        Constraint 1: sum_{j=1}^{n} w_j * x_j <= c
-        Constraint 2: x_j in {0, 1}, j = 1, ..., n
-        Constraint 3: Binary domain — auxiliary vars in {0,1} (auto)
-        Constraint 4: Integer domain — auxiliary vars integral (auto)
-        Constraint 5: Objective consistency — reported objective_value must
-                      equal the recomputed sum_{j<=i} p_{ij} * x_i * x_j.
-"""
-
-import argparse
-import json
-
-
-def check_feasibility(instance, solution):
-    tol = 1e-5
-    eps = 1e-5
-
-    n = instance["n"]
-    c = instance["capacity"]
-    w = instance["weights"]
-
-    # Accept either the full solution dict or a bare solution vector.
-    if isinstance(solution, dict):
-        if "solution_vector" in solution:
-            solution_vector = solution["solution_vector"]
-        elif "solution" in solution:
-            solution_vector = solution["solution"]
-        else:
-            raise ValueError("No solution vector found in solution dict")
-        reported_obj = solution.get("objective_value")
-    else:
-        solution_vector = solution
-        reported_obj = None
-
-    violated_constraints = set()
-    violations = []
-    violation_magnitudes = []
-
-    # --- Constraint 1: sum_{j=1}^{n} w_j * x_j <= c ---
-    lhs = sum(w[j] * solution_vector[j] for j in range(n))
-    rhs = c
-    violation_amount = lhs - rhs  # For <= constraint: how much LHS exceeds RHS
-    if violation_amount > tol:
-        violated_constraints.add(1)
-        normalizer = max(abs(rhs), eps)
-        ratio = violation_amount / normalizer
-        violations.append(
-            f"Knapsack capacity exceeded: total weight {lhs} > capacity {rhs}"
-        )
-        violation_magnitudes.append({
-            "constraint": 1,
-            "lhs": float(lhs),
-            "rhs": float(rhs),
-            "raw_excess": float(violation_amount),
-            "normalizer": float(normalizer),
-            "ratio": float(ratio),
-        })
-
-    # --- Constraint 2: x_j in {0, 1} for all j ---
-    non_binary_items = []
-    for j in range(n):
-        val = solution_vector[j]
-        # Check if val is 0 or 1 (within tolerance)
-        dist_to_0 = abs(val - 0)
-        dist_to_1 = abs(val - 1)
-        violation_amount = min(dist_to_0, dist_to_1)
-        if violation_amount > tol:
-            non_binary_items.append(j)
-            rhs_val = round(val)  # nearest binary value
-            normalizer = max(abs(rhs_val), eps)
-            ratio = violation_amount / normalizer
-            violation_magnitudes.append({
-                "constraint": 2,
-                "lhs": float(val),
-                "rhs": float(rhs_val),
-                "raw_excess": float(violation_amount),
-                "normalizer": float(normalizer),
-                "ratio": float(ratio),
-            })
-
-    if non_binary_items:
-        violated_constraints.add(2)
-        violations.append(
-            f"Non-binary variables at indices: {non_binary_items}"
-        )
-
-
-        # =====================================================================
-        # =====================================================================
-        # Constraint 3: Binary domain — variables must be 0 or 1
-    _domain_check_vars_binary = []
-    _domain_check_vars_integer = []
-
-    # =====================================================================
-    # Variable Domain Checks (auto-generated by add_domain_checks.py)
-    # =====================================================================
-    # Constraint 3: Binary domain — variables must be 0 or 1
-    for var_name, var_dict in _domain_check_vars_binary:
-        if isinstance(var_dict, dict):
-            for key, val in var_dict.items():
-                try:
-                    v = float(val)
-                except (TypeError, ValueError):
-                    continue
-                if abs(v - round(v)) > tol or round(v) not in (0, 1):
-                    viol = min(abs(v - 0), abs(v - 1))
-                    if viol > tol:
-                        violated_constraints.add(3)
-                        violations.append(
-                            f"Constraint 3 (binary domain): {var_name}[{key}] = {v} not in {0, 1}")
-                        violation_magnitudes.append({
-                            "constraint": 3,
-                            "lhs": v,
-                            "rhs": 1.0,
-                            "raw_excess": float(viol),
-                            "normalizer": 1.0,
-                            "ratio": float(viol),
-                        })
-
-    # Constraint 4: Integer domain — variables must be integral
-    for var_name, var_dict in _domain_check_vars_integer:
-        if isinstance(var_dict, dict):
-            for key, val in var_dict.items():
-                try:
-                    v = float(val)
-                except (TypeError, ValueError):
-                    continue
-                frac = abs(v - round(v))
-                if frac > tol:
-                    violated_constraints.add(4)
-                    violations.append(
-                        f"Constraint 4 (integer domain): {var_name}[{key}] = {v} is not integer")
-                    violation_magnitudes.append({
-                        "constraint": 4,
-                        "lhs": v,
-                        "rhs": round(v),
-                        "raw_excess": float(frac),
-                        "normalizer": max(abs(round(v)), eps),
-                        "ratio": float(frac / max(abs(round(v)), eps)),
-                    })
-
-    # --- Constraint 5: Objective consistency ---
-    # Recompute the QKP objective from the solution vector and the symmetric
-    # profit matrix and compare against the reported objective_value. This is
-    # a Tier C defense against LLM exploits that fabricate objective values.
-    profit_matrix = instance.get("profit_matrix")
-    if profit_matrix is not None and reported_obj is not None:
-        try:
-            reported = float(reported_obj)
-        except (TypeError, ValueError):
-            reported = None
-        if reported is not None:
-            # Round x to nearest binary value so the obj recompute is robust
-            # to tiny floating-point noise even if constraint 2 fires.
-            x_bin = [1 if abs(solution_vector[j] - 1) <= 0.5 else 0
-                     for j in range(n)]
-            true_obj = 0.0
-            for i in range(n):
-                if not x_bin[i]:
-                    continue
-                row = profit_matrix[i]
-                # j ranges from 0..i  (j <= i in 1-indexed math == j <= i in 0-indexed)
-                for j in range(i + 1):
-                    if x_bin[j]:
-                        true_obj += float(row[j])
-            abs_diff = abs(reported - true_obj)
-            # QKP profits are non-negative integers, so true_obj is integral:
-            # any integer disagreement (>= 1) must fire. Use 0.5 absolute floor
-            # with a 1e-6 relative slack for safety on very large objectives.
-            tol_obj = max(0.5, 1e-6 * abs(true_obj))
-            if abs_diff > tol_obj:
-                violated_constraints.add(5)
-                normalizer = max(abs(true_obj), eps)
-                ratio = abs_diff / normalizer
-                violations.append(
-                    f"Objective consistency violated: reported objective_value="
-                    f"{reported} differs from recomputed sum_{{j<=i}}"
-                    f"(p_ij * x_i * x_j)={true_obj} "
-                    f"(|diff|={abs_diff:.6g}, tol={tol_obj:.6g})"
-                )
-                violation_magnitudes.append({
-                    "constraint": 5,
-                    "lhs": float(reported),
-                    "rhs": float(true_obj),
-                    "raw_excess": float(abs_diff),
-                    "normalizer": float(normalizer),
-                    "ratio": float(ratio),
-                })
-
-    feasible = len(violated_constraints) == 0
-    return {
-        "feasible": feasible,
-        "violated_constraints": sorted(violated_constraints),
-        "violations": violations,
-        "violation_magnitudes": violation_magnitudes,
-    }
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Feasibility checker for the 0-1 Quadratic Knapsack Problem"
-    )
-    parser.add_argument("--instance_path", type=str, required=True,
-                        help="Path to the JSON instance file")
-    parser.add_argument("--solution_path", type=str, required=True,
-                        help="Path to the JSON solution file")
-    parser.add_argument("--result_path", type=str, required=True,
-                        help="Path to write the JSON feasibility result")
-    args = parser.parse_args()
-
-    with open(args.instance_path, "r") as f:
-        instance = json.load(f)
-
-    with open(args.solution_path, "r") as f:
-        solution = json.load(f)
-
-    # Pass the full solution dict so the obj-recompute check sees both the
-    # vector and the reported objective_value.
-    result = check_feasibility(instance, solution)
-
-    with open(args.result_path, "w") as f:
-        json.dump(result, f, indent=2)
-
-    print(f"Feasible: {result['feasible']}")
-    if not result["feasible"]:
-        for v in result["violations"]:
-            print(f"  - {v}")
-
-
-if __name__ == "__main__":
-    main()

yang2013/gurobi_code.py

@@ -1,147 +0,0 @@
-#!/usr/bin/env python3
-"""Solve the 0-1 Quadratic Knapsack Problem (QKP) using Gurobi.
-
-Problem formulation (Yang et al., 2013):
-    max  sum_{i=1}^{n} sum_{j=1}^{i} p_{ij} * x_i * x_j
-    s.t. sum_{j=1}^{n} w_j * x_j <= c
-         x_j in {0, 1}, j = 1, ..., n
-"""
-
-import argparse
-import json
-import time
-
-import gurobipy as gp
-from gurobipy import GRB
-import os as _os, sys as _sys
-# Walk up from this file's directory to find repo root (containing scripts/).
-_repo = _os.path.dirname(_os.path.abspath(__file__))
-while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
-    _repo = _os.path.dirname(_repo)
-if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
-    _sys.path.insert(0, _repo)
-try:
-    from scripts.utils.gurobi_log_helper import install_gurobi_logger
-except ImportError:
-    def install_gurobi_logger(log_path):  # no-op fallback when scripts/ unavailable
-        pass
-
-
-def solve_qkp(instance_path: str, solution_path: str, time_limit: int) -> None:
-    # Load instance
-    with open(instance_path, "r") as f:
-        instance = json.load(f)
-
-    instance_id = instance["instance_id"]
-    n = instance["n"]
-    capacity = instance["capacity"]
-    weights = instance["weights"]
-    profit_matrix = instance["profit_matrix"]
-
-    # Default solution path
-    if solution_path is None:
-        solution_path = f"gurobi_solution_{instance_id}.json"
-
-    # Build model
-    model = gp.Model("QKP")
-    model.setParam("Threads", 1)
-    model.setParam("TimeLimit", time_limit)
-
-    # Binary decision variables
-    x = model.addVars(n, vtype=GRB.BINARY, name="x")
-
-    # Knapsack constraint: sum w_j * x_j <= c
-    model.addConstr(
-        gp.quicksum(weights[j] * x[j] for j in range(n)) <= capacity,
-        name="knapsack",
-    )
-
-    # Objective: max sum_{i=1}^{n} sum_{j=1}^{i} p_{ij} * x_i * x_j
-    # Diagonal terms (i == j): p_{jj} * x_j * x_j = p_{jj} * x_j (since binary)
-    # Off-diagonal terms (i != j): p_{ij} * x_i * x_j
-    obj = gp.QuadExpr()
-
-    # Linear (diagonal) terms
-    for j in range(n):
-        if profit_matrix[j][j] != 0:
-            obj += profit_matrix[j][j] * x[j]
-
-    # Quadratic (off-diagonal) terms: sum over i > j of p_{ij} * x_i * x_j
-    # The formulation sums i from 1..n, j from 1..i, so for i != j we get
-    # each unordered pair (i, j) with i > j exactly once.
-    for i in range(1, n):
-        for j in range(i):
-            if profit_matrix[i][j] != 0:
-                obj += profit_matrix[i][j] * x[i] * x[j]
-
-    model.setObjective(obj, GRB.MAXIMIZE)
-
-    # Solve
-    start_time = time.time()
-    model.optimize()
-    solve_time = time.time() - start_time
-
-    # Extract solution
-    result = {
-        "instance_id": instance_id,
-        "n": n,
-        "solver": "gurobi",
-        "time_limit": time_limit,
-        "solve_time": solve_time,
-    }
-
-    if model.SolCount > 0:
-        result["objective_value"] = model.ObjVal
-        result["best_bound"] = model.ObjBound
-        result["mip_gap"] = model.MIPGap
-        result["solution"] = [int(x[j].X + 0.5) for j in range(n)]
-        result["status"] = "optimal" if model.Status == GRB.OPTIMAL else "feasible"
-    else:
-        result["objective_value"] = None
-        result["solution"] = None
-        result["status"] = "infeasible_or_no_solution"
-
-    result["gurobi_status"] = model.Status
-
-    # Write solution
-    with open(solution_path, "w") as f:
-        json.dump(result, f, indent=2)
-
-    print(f"Status: {result['status']}")
-    if result["objective_value"] is not None:
-        print(f"Objective value: {result['objective_value']}")
-    print(f"Solve time: {solve_time:.2f}s")
-    print(f"Solution written to: {solution_path}")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Solve the 0-1 Quadratic Knapsack Problem using Gurobi"
-    )
-    parser.add_argument(
-        "--instance_path",
-        type=str,
-        required=True,
-        help="Path to the JSON instance file",
-    )
-    parser.add_argument(
-        "--solution_path",
-        type=str,
-        default=None,
-        help="Path for the output solution JSON (default: gurobi_solution_{instance_id}.json)",
-    )
-    parser.add_argument(
-        "--time_limit",
-        type=int,
-        required=True,
-        help="Maximum solver time in seconds",
-    )
-    parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions")
-    args = parser.parse_args()
-    install_gurobi_logger(args.log_path)
-
-    solve_qkp(args.instance_path, args.solution_path, args.time_limit)
-
-
-if __name__ == "__main__":
-    main()

yang2013/gurobi_feasi_result/large_feasi_result_1.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

yang2013/gurobi_feasi_result/large_feasi_result_2.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

yang2013/gurobi_feasi_result/large_feasi_result_3.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

yang2013/gurobi_feasi_result/large_feasi_result_4.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

yang2013/gurobi_feasi_result/large_feasi_result_5.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

yang2013/gurobi_feasi_result/tiny_feasi_result.json

@@ -1,6 +0,0 @@
-{
-  "feasible": true,
-  "violated_constraints": [],
-  "violations": [],
-  "violation_magnitudes": []
-}

yang2013/gurobi_solution/large_solution_1.json

@@ -1,2014 +0,0 @@
-{
-  "instance_id": 10,
-  "n": 2000,
-  "solver": "gurobi",
-  "time_limit": 3600,
-  "solve_time": 3600.0880575180054,
-  "objective_value": 38991017.0,
-  "best_bound": 42015978.34053395,
-  "mip_gap": 0.077580980781649,
-  "solution": [
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    0,
-    1,
-    1,
-    0,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    0,
-    0,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    0,
-    1,
-    1,
-    0,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    0,
-    0,
-    1,
-    1,
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-    1,
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-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    0,
-    1,
-    1,
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-    1,
-    1,
-    0,
-    1,
-    0,
-    1,
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yang2013/gurobi_solution/large_solution_3.json

@@ -1,2014 +0,0 @@
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yang2013/gurobi_solution/large_solution_5.json

@@ -1,2014 +0,0 @@
-{
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yang2013/gurobi_solution/tiny_solution.json

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-{
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yang2013/gurobi_solution_log/large_log_1.jsonl

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yang2013/gurobi_solution_log/large_log_2.jsonl

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yang2013/gurobi_solution_log/large_log_3.jsonl

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-{"time": 8.287, "objective_value": -0.0}